Neuroengineering tools/applications for bidirectional interfaces, brain-computer interfaces, and neuroprosthetic implants - a review of recent progress.

PubMed

Rothschild, Ryan Mark

2010-01-01

The main focus of this review is to provide a holistic amalgamated overview of the most recent human in vivo techniques for implementing brain-computer interfaces (BCIs), bidirectional interfaces, and neuroprosthetics. Neuroengineering is providing new methods for tackling current difficulties; however neuroprosthetics have been studied for decades. Recent progresses are permitting the design of better systems with higher accuracies, repeatability, and system robustness. Bidirectional interfaces integrate recording and the relaying of information from and to the brain for the development of BCIs. The concepts of non-invasive and invasive recording of brain activity are introduced. This includes classical and innovative techniques like electroencephalography and near-infrared spectroscopy. Then the problem of gliosis and solutions for (semi-) permanent implant biocompatibility such as innovative implant coatings, materials, and shapes are discussed. Implant power and the transmission of their data through implanted pulse generators and wireless telemetry are taken into account. How sensation can be relayed back to the brain to increase integration of the neuroengineered systems with the body by methods such as micro-stimulation and transcranial magnetic stimulation are then addressed. The neuroprosthetic section discusses some of the various types and how they operate. Visual prosthetics are discussed and the three types, dependant on implant location, are examined. Auditory prosthetics, being cochlear or cortical, are then addressed. Replacement hand and limb prosthetics are then considered. These are followed by sections concentrating on the control of wheelchairs, computers and robotics directly from brain activity as recorded by non-invasive and invasive techniques.

Brain-controlled body movement assistance devices and methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leuthardt, Eric C.; Love, Lonnie J.; Coker, Rob

Methods, devices, systems, and apparatus, including computer programs encoded on a computer storage medium, for brain-controlled body movement assistance devices. In one aspect, a device includes a brain-controlled body movement assistance device with a brain-computer interface (BCI) component adapted to be mounted to a user, a body movement assistance component operably connected to the BCI component and adapted to be worn by the user, and a feedback mechanism provided in connection with at least one of the BCI component and the body movement assistance component, the feedback mechanism being configured to output information relating to a usage session of themore » brain-controlled body movement assistance device.« less

Active tactile exploration using a brain-machine-brain interface.

PubMed

O'Doherty, Joseph E; Lebedev, Mikhail A; Ifft, Peter J; Zhuang, Katie Z; Shokur, Solaiman; Bleuler, Hannes; Nicolelis, Miguel A L

2011-10-05

Brain-machine interfaces use neuronal activity recorded from the brain to establish direct communication with external actuators, such as prosthetic arms. It is hoped that brain-machine interfaces can be used to restore the normal sensorimotor functions of the limbs, but so far they have lacked tactile sensation. Here we report the operation of a brain-machine-brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and allows signalling of artificial tactile feedback through intracortical microstimulation (ICMS) of the primary somatosensory cortex. Monkeys performed an active exploration task in which an actuator (a computer cursor or a virtual-reality arm) was moved using a BMBI that derived motor commands from neuronal ensemble activity recorded in the primary motor cortex. ICMS feedback occurred whenever the actuator touched virtual objects. Temporal patterns of ICMS encoded the artificial tactile properties of each object. Neuronal recordings and ICMS epochs were temporally multiplexed to avoid interference. Two monkeys operated this BMBI to search for and distinguish one of three visually identical objects, using the virtual-reality arm to identify the unique artificial texture associated with each. These results suggest that clinical motor neuroprostheses might benefit from the addition of ICMS feedback to generate artificial somatic perceptions associated with mechanical, robotic or even virtual prostheses.

Implanted Miniaturized Antenna for Brain Computer Interface Applications: Analysis and Design

PubMed Central

Zhao, Yujuan; Rennaker, Robert L.; Hutchens, Chris; Ibrahim, Tamer S.

2014-01-01

Implantable Brain Computer Interfaces (BCIs) are designed to provide real-time control signals for prosthetic devices, study brain function, and/or restore sensory information lost as a result of injury or disease. Using Radio Frequency (RF) to wirelessly power a BCI could widely extend the number of applications and increase chronic in-vivo viability. However, due to the limited size and the electromagnetic loss of human brain tissues, implanted miniaturized antennas suffer low radiation efficiency. This work presents simulations, analysis and designs of implanted antennas for a wireless implantable RF-powered brain computer interface application. The results show that thin (on the order of 100 micrometers thickness) biocompatible insulating layers can significantly impact the antenna performance. The proper selection of the dielectric properties of the biocompatible insulating layers and the implantation position inside human brain tissues can facilitate efficient RF power reception by the implanted antenna. While the results show that the effects of the human head shape on implanted antenna performance is somewhat negligible, the constitutive properties of the brain tissues surrounding the implanted antenna can significantly impact the electrical characteristics (input impedance, and operational frequency) of the implanted antenna. Three miniaturized antenna designs are simulated and demonstrate that maximum RF power of up to 1.8 milli-Watts can be received at 2 GHz when the antenna implanted around the dura, without violating the Specific Absorption Rate (SAR) limits. PMID:25079941

An online hybrid brain-computer interface combining multiple physiological signals for webpage browse.

PubMed

Long Chen; Zhongpeng Wang; Feng He; Jiajia Yang; Hongzhi Qi; Peng Zhou; Baikun Wan; Dong Ming

2015-08-01

The hybrid brain computer interface (hBCI) could provide higher information transfer rate than did the classical BCIs. It included more than one brain-computer or human-machine interact paradigms, such as the combination of the P300 and SSVEP paradigms. Research firstly constructed independent subsystems of three different paradigms and tested each of them with online experiments. Then we constructed a serial hybrid BCI system which combined these paradigms to achieve the functions of typing letters, moving and clicking cursor, and switching among them for the purpose of browsing webpages. Five subjects were involved in this study. They all successfully realized these functions in the online tests. The subjects could achieve an accuracy above 90% after training, which met the requirement in operating the system efficiently. The results demonstrated that it was an efficient system capable of robustness, which provided an approach for the clinic application.

A Gaussian mixture model based adaptive classifier for fNIRS brain-computer interfaces and its testing via simulation

NASA Astrophysics Data System (ADS)

Li, Zheng; Jiang, Yi-han; Duan, Lian; Zhu, Chao-zhe

2017-08-01

Objective. Functional near infra-red spectroscopy (fNIRS) is a promising brain imaging technology for brain-computer interfaces (BCI). Future clinical uses of fNIRS will likely require operation over long time spans, during which neural activation patterns may change. However, current decoders for fNIRS signals are not designed to handle changing activation patterns. The objective of this study is to test via simulations a new adaptive decoder for fNIRS signals, the Gaussian mixture model adaptive classifier (GMMAC). Approach. GMMAC can simultaneously classify and track activation pattern changes without the need for ground-truth labels. This adaptive classifier uses computationally efficient variational Bayesian inference to label new data points and update mixture model parameters, using the previous model parameters as priors. We test GMMAC in simulations in which neural activation patterns change over time and compare to static decoders and unsupervised adaptive linear discriminant analysis classifiers. Main results. Our simulation experiments show GMMAC can accurately decode under time-varying activation patterns: shifts of activation region, expansions of activation region, and combined contractions and shifts of activation region. Furthermore, the experiments show the proposed method can track the changing shape of the activation region. Compared to prior work, GMMAC performed significantly better than the other unsupervised adaptive classifiers on a difficult activation pattern change simulation: 99% versus  <54% in two-choice classification accuracy. Significance. We believe GMMAC will be useful for clinical fNIRS-based brain-computer interfaces, including neurofeedback training systems, where operation over long time spans is required.

Brain computer interfaces, a review.

PubMed

Nicolas-Alonso, Luis Fernando; Gomez-Gil, Jaime

2012-01-01

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

BCI2000: a general-purpose brain-computer interface (BCI) system.

PubMed

Schalk, Gerwin; McFarland, Dennis J; Hinterberger, Thilo; Birbaumer, Niels; Wolpaw, Jonathan R

2004-06-01

Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.

Concept of software interface for BCI systems

NASA Astrophysics Data System (ADS)

Svejda, Jaromir; Zak, Roman; Jasek, Roman

2016-06-01

Brain Computer Interface (BCI) technology is intended to control external system by brain activity. One of main part of such system is software interface, which carries about clear communication between brain and either computer or additional devices connected to computer. This paper is organized as follows. Firstly, current knowledge about human brain is briefly summarized to points out its complexity. Secondly, there is described a concept of BCI system, which is then used to build an architecture of proposed software interface. Finally, there are mentioned disadvantages of sensing technology discovered during sensing part of our research.

Towards Development of a 3-State Self-Paced Brain-Computer Interface

PubMed Central

Bashashati, Ali; Ward, Rabab K.; Birch, Gary E.

2007-01-01

Most existing brain-computer interfaces (BCIs) detect specific mental activity in a so-called synchronous paradigm. Unlike synchronous systems which are operational at specific system-defined periods, self-paced (asynchronous) interfaces have the advantage of being operational at all times. The low-frequency asynchronous switch design (LF-ASD) is a 2-state self-paced BCI that detects the presence of a specific finger movement in the ongoing EEG. Recent evaluations of the 2-state LF-ASD show an average true positive rate of 41% at the fixed false positive rate of 1%. This paper proposes two designs for a 3-state self-paced BCI that is capable of handling idle brain state. The two proposed designs aim at detecting right- and left-hand extensions from the ongoing EEG. They are formed of two consecutive detectors. The first detects the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement). It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1%) in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1%) in the context of a 2-state self-paced BCI. PMID:18288260

CSP - 2017 International Conference of Mobile Brain Body Imaging (MoBI) and the Neuroscience of Art, Innovation and Creativity

DTIC Science & Technology

2017-09-10

including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services , Directorate for Information Operations and...covered in the conference: 1) Wearable Mobile Brain-Body Imaging (MoBI) technologies (both hardware and software developments); 2) Cognitive and Brain...the state of the art and challenges in cognitive and affective brain-computer interfaces, and their deployment in the service of the arts and the

An adaptive brain actuated system for augmenting rehabilitation

PubMed Central

Roset, Scott A.; Gant, Katie; Prasad, Abhishek; Sanchez, Justin C.

2014-01-01

For people living with paralysis, restoration of hand function remains the top priority because it leads to independence and improvement in quality of life. In approaches to restore hand and arm function, a goal is to better engage voluntary control and counteract maladaptive brain reorganization that results from non-use. Standard rehabilitation augmented with developments from the study of brain-computer interfaces could provide a combined therapy approach for motor cortex rehabilitation and to alleviate motor impairments. In this paper, an adaptive brain-computer interface system intended for application to control a functional electrical stimulation (FES) device is developed as an experimental test bed for augmenting rehabilitation with a brain-computer interface. The system's performance is improved throughout rehabilitation by passive user feedback and reinforcement learning. By continuously adapting to the user's brain activity, similar adaptive systems could be used to support clinical brain-computer interface neurorehabilitation over multiple days. PMID:25565945

Brain Computer Interfaces, a Review

PubMed Central

Nicolas-Alonso, Luis Fernando; Gomez-Gil, Jaime

2012-01-01

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices. PMID:22438708

Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke.

PubMed

Mukaino, Masahiko; Ono, Takashi; Shindo, Keiichiro; Fujiwara, Toshiyuki; Ota, Tetsuo; Kimura, Akio; Liu, Meigen; Ushiba, Junichi

2014-04-01

Brain computer interface technology is of great interest to researchers as a potential therapeutic measure for people with severe neurological disorders. The aim of this study was to examine the efficacy of brain computer interface, by comparing conventional neuromuscular electrical stimulation and brain computer interface-driven neuromuscular electrical stimulation, using an A-B-A-B withdrawal single-subject design. A 38-year-old male with severe hemiplegia due to a putaminal haemorrhage participated in this study. The design involved 2 epochs. In epoch A, the patient attempted to open his fingers during the application of neuromuscular electrical stimulation, irrespective of his actual brain activity. In epoch B, neuromuscular electrical stimulation was applied only when a significant motor-related cortical potential was observed in the electroencephalogram. The subject initially showed diffuse functional magnetic resonance imaging activation and small electro-encephalogram responses while attempting finger movement. Epoch A was associated with few neurological or clinical signs of improvement. Epoch B, with a brain computer interface, was associated with marked lateralization of electroencephalogram (EEG) and blood oxygenation level dependent responses. Voluntary electromyogram (EMG) activity, with significant EEG-EMG coherence, was also prompted. Clinical improvement in upper-extremity function and muscle tone was observed. These results indicate that self-directed training with a brain computer interface may induce activity- dependent cortical plasticity and promote functional recovery. This preliminary clinical investigation encourages further research using a controlled design.

Control of a nursing bed based on a hybrid brain-computer interface.

PubMed

Nengneng Peng; Rui Zhang; Haihua Zeng; Fei Wang; Kai Li; Yuanqing Li; Xiaobin Zhuang

2016-08-01

In this paper, we propose an intelligent nursing bed system which is controlled by a hybrid brain-computer interface (BCI) involving steady-state visual evoked potential (SSVEP) and P300. Specifically, the hybrid BCI includes an asynchronous brain switch based on SSVEP and P300, and a P300-based BCI. The brain switch is used to turn on/off the control system of the electric nursing bed through idle/control state detection, whereas the P300-based BCI is for operating the nursing bed. At the beginning, the user may focus on one group of flashing buttons in the graphic user interface (GUI) of the brain switch, which can simultaneously evoke SSVEP and P300, to switch on the control system. Here, the combination of SSVEP and P300 is used for improving the performance of the brain switch. Next, the user can control the nursing bed using the P300-based BCI. The GUI of the P300-based BCI includes 10 flashing buttons, which correspond to 10 functional operations, namely, left-side up, left-side down, back up, back down, bedpan open, bedpan close, legs up, legs down, right-side up, and right-side down. For instance, he/she can focus on the flashing button "back up" in the GUI of the P300-based BCI to activate the corresponding control such that the nursing bed is adjusted up. Eight healthy subjects participated in our experiment, and obtained an average accuracy of 93.75% and an average false positive rate (FPR) of 0.15 event/min. The effectiveness of our system was thus demonstrated.

Are we there yet? Evaluating commercial grade brain-computer interface for control of computer applications by individuals with cerebral palsy.

PubMed

Taherian, Sarvnaz; Selitskiy, Dmitry; Pau, James; Claire Davies, T

2017-02-01

Using a commercial electroencephalography (EEG)-based brain-computer interface (BCI), the training and testing protocol for six individuals with spastic quadriplegic cerebral palsy (GMFCS and MACS IV and V) was evaluated. A customised, gamified training paradigm was employed. Over three weeks, the participants spent two sessions exploring the system, and up to six sessions playing the game which focussed on EEG feedback of left and right arm motor imagery. The participants showed variable inconclusive results in the ability to produce two distinct EEG patterns. Participant performance was influenced by physical illness, motivation, fatigue and concentration. The results from this case study highlight the infancy of BCIs as a form of assistive technology for people with cerebral palsy. Existing commercial BCIs are not designed according to the needs of end-users. Implications for Rehabilitation Mood, fatigue, physical illness and motivation influence the usability of a brain-computer interface. Commercial brain-computer interfaces are not designed for practical assistive technology use for people with cerebral palsy. Practical brain-computer interface assistive technologies may need to be flexible to suit individual needs.

Brain-Computer Interfaces in Medicine

PubMed Central

Shih, Jerry J.; Krusienski, Dean J.; Wolpaw, Jonathan R.

2012-01-01

Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function. PMID:22325364

Review of wireless and wearable electroencephalogram systems and brain-computer interfaces--a mini-review.

PubMed

Lin, Chin-Teng; Ko, Li-Wei; Chang, Meng-Hsiu; Duann, Jeng-Ren; Chen, Jing-Ying; Su, Tung-Ping; Jung, Tzyy-Ping

2010-01-01

Biomedical signal monitoring systems have rapidly advanced in recent years, propelled by significant advances in electronic and information technologies. Brain-computer interface (BCI) is one of the important research branches and has become a hot topic in the study of neural engineering, rehabilitation, and brain science. Traditionally, most BCI systems use bulky, wired laboratory-oriented sensing equipments to measure brain activity under well-controlled conditions within a confined space. Using bulky sensing equipments not only is uncomfortable and inconvenient for users, but also impedes their ability to perform routine tasks in daily operational environments. Furthermore, owing to large data volumes, signal processing of BCI systems is often performed off-line using high-end personal computers, hindering the applications of BCI in real-world environments. To be practical for routine use by unconstrained, freely-moving users, BCI systems must be noninvasive, nonintrusive, lightweight and capable of online signal processing. This work reviews recent online BCI systems, focusing especially on wearable, wireless and real-time systems. Copyright 2009 S. Karger AG, Basel.

Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.

PubMed

Hiremath, Shivayogi V; Chen, Weidong; Wang, Wei; Foldes, Stephen; Yang, Ying; Tyler-Kabara, Elizabeth C; Collinger, Jennifer L; Boninger, Michael L

2015-01-01

A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.

PubMed

Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo

2013-01-01

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.

Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface

PubMed Central

LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

2013-01-01

Objective At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional physical space using noninvasive scalp EEG in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that operation of a real world device has on subjects’ control with comparison to a two-dimensional virtual cursor task. Approach Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a three-dimensional physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m/s. Significance Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user’s ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in the three-dimensional physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG based BCI systems to accomplish complex control in three-dimensional physical space. The present study may serve as a framework for the investigation of multidimensional non-invasive brain-computer interface control in a physical environment using telepresence robotics. PMID:23735712

Neuroengineering Tools/Applications for Bidirectional Interfaces, Brain–Computer Interfaces, and Neuroprosthetic Implants – A Review of Recent Progress

PubMed Central

Rothschild, Ryan Mark

2010-01-01

The main focus of this review is to provide a holistic amalgamated overview of the most recent human in vivo techniques for implementing brain–computer interfaces (BCIs), bidirectional interfaces, and neuroprosthetics. Neuroengineering is providing new methods for tackling current difficulties; however neuroprosthetics have been studied for decades. Recent progresses are permitting the design of better systems with higher accuracies, repeatability, and system robustness. Bidirectional interfaces integrate recording and the relaying of information from and to the brain for the development of BCIs. The concepts of non-invasive and invasive recording of brain activity are introduced. This includes classical and innovative techniques like electroencephalography and near-infrared spectroscopy. Then the problem of gliosis and solutions for (semi-) permanent implant biocompatibility such as innovative implant coatings, materials, and shapes are discussed. Implant power and the transmission of their data through implanted pulse generators and wireless telemetry are taken into account. How sensation can be relayed back to the brain to increase integration of the neuroengineered systems with the body by methods such as micro-stimulation and transcranial magnetic stimulation are then addressed. The neuroprosthetic section discusses some of the various types and how they operate. Visual prosthetics are discussed and the three types, dependant on implant location, are examined. Auditory prosthetics, being cochlear or cortical, are then addressed. Replacement hand and limb prosthetics are then considered. These are followed by sections concentrating on the control of wheelchairs, computers and robotics directly from brain activity as recorded by non-invasive and invasive techniques. PMID:21060801

Light on! Real world evaluation of a P300-based brain-computer interface (BCI) for environment control in a smart home.

PubMed

Carabalona, Roberta; Grossi, Ferdinando; Tessadri, Adam; Castiglioni, Paolo; Caracciolo, Antonio; de Munari, Ilaria

2012-01-01

Brain-computer interface (BCI) systems aim to enable interaction with other people and the environment without muscular activation by the exploitation of changes in brain signals due to the execution of cognitive tasks. In this context, the visual P300 potential appears suited to control smart homes through BCI spellers. The aim of this work is to evaluate whether the widely used character-speller is more sustainable than an icon-based one, designed to operate smart home environment or to communicate moods and needs. Nine subjects with neurodegenerative diseases and no BCI experience used both speller types in a real smart home environment. User experience during BCI tasks was evaluated recording concurrent physiological signals. Usability was assessed for each speller type immediately after use. Classification accuracy was lower for the icon-speller, which was also more attention demanding. However, in subjective evaluations, the effect of a real feedback partially counterbalanced the difficulty in BCI use. Since inclusive BCIs require to consider interface sustainability, we evaluated different ergonomic aspects of the interaction of disabled users with a character-speller (goal: word spelling) and an icon-speller (goal: operating a real smart home). We found the first one as more sustainable in terms of accuracy and cognitive effort.

Brain-computer interface technology: a review of the Second International Meeting.

PubMed

Vaughan, Theresa M; Heetderks, William J; Trejo, Leonard J; Rymer, William Z; Weinrich, Michael; Moore, Melody M; Kübler, Andrea; Dobkin, Bruce H; Birbaumer, Niels; Donchin, Emanuel; Wolpaw, Elizabeth Winter; Wolpaw, Jonathan R

2003-06-01

This paper summarizes the Brain-Computer Interfaces for Communication and Control, The Second International Meeting, held in Rensselaerville, NY, in June 2002. Sponsored by the National Institutes of Health and organized by the Wadsworth Center of the New York State Department of Health, the meeting addressed current work and future plans in brain-computer interface (BCI) research. Ninety-two researchers representing 38 different research groups from the United States, Canada, Europe, and China participated. The BCIs discussed at the meeting use electroencephalographic activity recorded from the scalp or single-neuron activity recorded within cortex to control cursor movement, select letters or icons, or operate neuroprostheses. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI that recognizes the commands contained in the input and expresses them in device control. Current BCIs have maximum information transfer rates of up to 25 b/min. Achievement of greater speed and accuracy requires improvements in signal acquisition and processing, in translation algorithms, and in user training. These improvements depend on interdisciplinary cooperation among neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective criteria for evaluating alternative methods. The practical use of BCI technology will be determined by the development of appropriate applications and identification of appropriate user groups, and will require careful attention to the needs and desires of individual users.

Brain-computer interfaces in medicine.

PubMed

Shih, Jerry J; Krusienski, Dean J; Wolpaw, Jonathan R

2012-03-01

Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function. Copyright © 2012 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

Engineering brain-computer interfaces: past, present and future.

PubMed

Hughes, M A

2014-06-01

Electricity governs the function of both nervous systems and computers. Whilst ions move in polar fluids to depolarize neuronal membranes, electrons move in the solid-state lattices of microelectronic semiconductors. Joining these two systems together, to create an iono-electric brain-computer interface, is an immense challenge. However, such interfaces offer (and in select clinical contexts have already delivered) a method of overcoming disability caused by neurological or musculoskeletal pathology. To fulfill their theoretical promise, several specific challenges demand consideration. Rate-limiting steps cover a diverse range of disciplines including microelectronics, neuro-informatics, engineering, and materials science. As those who work at the tangible interface between brain and outside world, neurosurgeons are well placed to contribute to, and inform, this cutting edge area of translational research. This article explores the historical background, status quo, and future of brain-computer interfaces; and outlines the challenges to progress and opportunities available to the clinical neurosciences community.

Multimodality Instrument for Tissue Characterization

NASA Technical Reports Server (NTRS)

Mah, Robert W. (Inventor); Andrews, Russell J. (Inventor)

2000-01-01

A system with multimodality instrument for tissue identification includes a computer-controlled motor driven heuristic probe with a multisensory tip is discussed. For neurosurgical applications, the instrument is mounted on a stereotactic frame for the probe to penetrate the brain in a precisely controlled fashion. The resistance of the brain tissue being penetrated is continually monitored by a miniaturized strain gauge attached to the probe tip. Other modality sensors may be mounted near the probe tip to provide real-time tissue characterizations and the ability to detect the proximity of blood vessels, thus eliminating errors normally associated with registration of pre-operative scans, tissue swelling, elastic tissue deformation, human judgement, etc., and rendering surgical procedures safer, more accurate, and efficient. A neural network, program adaptively learns the information on resistance and other characteristic features of normal brain tissue during the surgery and provides near real-time modeling. A fuzzy logic interface to the neural network program incorporates expert medical knowledge in the learning process. Identification of abnormal brain tissue is determined by the detection of change and comparison with previously learned models of abnormal brain tissues. The operation of the instrument is controlled through a user friendly graphical interface. Patient data is presented in a 3D stereographics display. Acoustic feedback of selected information may optionally be provided. Upon detection of the close proximity to blood vessels or abnormal brain tissue, the computer-controlled motor immediately stops probe penetration.

Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future.

PubMed

Huggins, Jane E; Guger, Christoph; Allison, Brendan; Anderson, Charles W; Batista, Aaron; Brouwer, Anne-Marie A-M; Brunner, Clemens; Chavarriaga, Ricardo; Fried-Oken, Melanie; Gunduz, Aysegul; Gupta, Disha; Kübler, Andrea; Leeb, Robert; Lotte, Fabien; Miller, Lee E; Müller-Putz, Gernot; Rutkowski, Tomasz; Tangermann, Michael; Thompson, David Edward

2014-01-01

The Fifth International Brain-Computer Interface (BCI) Meeting met June 3-7 th , 2013 at the Asilomar Conference Grounds, Pacific Grove, California. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and high-lighting important issues for future research and development.

Gaze-independent brain-computer interfaces based on covert attention and feature attention

NASA Astrophysics Data System (ADS)

Treder, M. S.; Schmidt, N. M.; Blankertz, B.

2011-10-01

There is evidence that conventional visual brain-computer interfaces (BCIs) based on event-related potentials cannot be operated efficiently when eye movements are not allowed. To overcome this limitation, the aim of this study was to develop a visual speller that does not require eye movements. Three different variants of a two-stage visual speller based on covert spatial attention and non-spatial feature attention (i.e. attention to colour and form) were tested in an online experiment with 13 healthy participants. All participants achieved highly accurate BCI control. They could select one out of thirty symbols (chance level 3.3%) with mean accuracies of 88%-97% for the different spellers. The best results were obtained for a speller that was operated using non-spatial feature attention only. These results show that, using feature attention, it is possible to realize high-accuracy, fast-paced visual spellers that have a large vocabulary and are independent of eye gaze.

Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future

PubMed Central

Huggins, Jane E.; Guger, Christoph; Allison, Brendan; Anderson, Charles W.; Batista, Aaron; Brouwer, Anne-Marie (A.-M.); Brunner, Clemens; Chavarriaga, Ricardo; Fried-Oken, Melanie; Gunduz, Aysegul; Gupta, Disha; Kübler, Andrea; Leeb, Robert; Lotte, Fabien; Miller, Lee E.; Müller-Putz, Gernot; Rutkowski, Tomasz; Tangermann, Michael; Thompson, David Edward

2014-01-01

The Fifth International Brain-Computer Interface (BCI) Meeting met June 3–7th, 2013 at the Asilomar Conference Grounds, Pacific Grove, California. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and high-lighting important issues for future research and development. PMID:25485284

Ownership and Agency of an Independent Supernumerary Hand Induced by an Imitation Brain-Computer Interface.

PubMed

Bashford, Luke; Mehring, Carsten

2016-01-01

To study body ownership and control, illusions that elicit these feelings in non-body objects are widely used. Classically introduced with the Rubber Hand Illusion, these illusions have been replicated more recently in virtual reality and by using brain-computer interfaces. Traditionally these illusions investigate the replacement of a body part by an artificial counterpart, however as brain-computer interface research develops it offers us the possibility to explore the case where non-body objects are controlled in addition to movements of our own limbs. Therefore we propose a new illusion designed to test the feeling of ownership and control of an independent supernumerary hand. Subjects are under the impression they control a virtual reality hand via a brain-computer interface, but in reality there is no causal connection between brain activity and virtual hand movement but correct movements are observed with 80% probability. These imitation brain-computer interface trials are interspersed with movements in both the subjects' real hands, which are in view throughout the experiment. We show that subjects develop strong feelings of ownership and control over the third hand, despite only receiving visual feedback with no causal link to the actual brain signals. Our illusion is crucially different from previously reported studies as we demonstrate independent ownership and control of the third hand without loss of ownership in the real hands.

[The P300-based brain-computer interface: presentation of the complex "flash + movement" stimuli].

PubMed

Ganin, I P; Kaplan, A Ia

2014-01-01

The P300 based brain-computer interface requires the detection of P300 wave of brain event-related potentials. Most of its users learn the BCI control in several minutes and after the short classifier training they can type a text on the computer screen or assemble an image of separate fragments in simple BCI-based video games. Nevertheless, insufficient attractiveness for users and conservative stimuli organization in this BCI may restrict its integration into real information processes control. At the same time initial movement of object (motion-onset stimuli) may be an independent factor that induces P300 wave. In current work we checked the hypothesis that complex "flash + movement" stimuli together with drastic and compact stimuli organization on the computer screen may be much more attractive for user while operating in P300 BCI. In 20 subjects research we showed the effectiveness of our interface. Both accuracy and P300 amplitude were higher for flashing stimuli and complex "flash + movement" stimuli compared to motion-onset stimuli. N200 amplitude was maximal for flashing stimuli, while for "flash + movement" stimuli and motion-onset stimuli it was only a half of it. Similar BCI with complex stimuli may be embedded into compact control systems requiring high level of user attention under impact of negative external effects obstructing the BCI control.

A Multi-purpose Brain-Computer Interface Output Device

PubMed Central

Thompson, David E; Huggins, Jane E

2012-01-01

While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as standalone communication and control systems, rather than as interfaces to existing systems built for these purposes. While an individual communication and control system may be powerful or flexible, no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCIs could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e. without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems. PMID:22208120

A multi-purpose brain-computer interface output device.

PubMed

Thompson, David E; Huggins, Jane E

2011-10-01

While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as stand-alone communication and control systems, rather than as interfaces to existing systems built for these purposes. An individual communication and control system may be powerful or flexible, but no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCls could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e., without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems.

Simultaneous detection of P300 and steady-state visually evoked potentials for hybrid brain-computer interface.

PubMed

Combaz, Adrien; Van Hulle, Marc M

2015-01-01

We study the feasibility of a hybrid Brain-Computer Interface (BCI) combining simultaneous visual oddball and Steady-State Visually Evoked Potential (SSVEP) paradigms, where both types of stimuli are superimposed on a computer screen. Potentially, such a combination could result in a system being able to operate faster than a purely P300-based BCI and encode more targets than a purely SSVEP-based BCI. We analyse the interactions between the brain responses of the two paradigms, and assess the possibility to detect simultaneously the brain activity evoked by both paradigms, in a series of 3 experiments where EEG data are analysed offline. Despite differences in the shape of the P300 response between pure oddball and hybrid condition, we observe that the classification accuracy of this P300 response is not affected by the SSVEP stimulation. We do not observe either any effect of the oddball stimulation on the power of the SSVEP response in the frequency of stimulation. Finally results from the last experiment show the possibility of detecting both types of brain responses simultaneously and suggest not only the feasibility of such hybrid BCI but also a gain over pure oddball- and pure SSVEP-based BCIs in terms of communication rate.

Using the Electrocorticographic Speech Network to Control a Brain-Computer Interface in Humans

PubMed Central

Leuthardt, Eric C.; Gaona, Charles; Sharma, Mohit; Szrama, Nicholas; Roland, Jarod; Freudenberg, Zac; Solis, Jamie; Breshears, Jonathan; Schalk, Gerwin

2013-01-01

Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68 and 91% within 15 minutes. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive. PMID:21471638

Spatial Brain Control Interface using Optical and Electrophysiological Measures

DTIC Science & Technology

2013-08-27

appropriate for implementing a reliable brain-computer interface ( BCI ). The LSVM method 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 27-08-2013 13...Machine (LSVM) was the most appropriate for implementing a reliable brain-computer interface ( BCI ). The LSVM method was applied to the imaging data...local field potentials proved to be fast and strongly tuned for the spatial parameters of the task. Thus, a reliable BCI that can predict upcoming

Towards Intelligent Environments: An Augmented Reality–Brain–Machine Interface Operated with a See-Through Head-Mount Display

PubMed Central

Takano, Kouji; Hata, Naoki; Kansaku, Kenji

2011-01-01

The brain–machine interface (BMI) or brain–computer interface is a new interface technology that uses neurophysiological signals from the brain to control external machines or computers. This technology is expected to support daily activities, especially for persons with disabilities. To expand the range of activities enabled by this type of interface, here, we added augmented reality (AR) to a P300-based BMI. In this new system, we used a see-through head-mount display (HMD) to create control panels with flicker visual stimuli to support the user in areas close to controllable devices. When the attached camera detects an AR marker, the position and orientation of the marker are calculated, and the control panel for the pre-assigned appliance is created by the AR system and superimposed on the HMD. The participants were required to control system-compatible devices, and they successfully operated them without significant training. Online performance with the HMD was not different from that using an LCD monitor. Posterior and lateral (right or left) channel selections contributed to operation of the AR–BMI with both the HMD and LCD monitor. Our results indicate that AR–BMI systems operated with a see-through HMD may be useful in building advanced intelligent environments. PMID:21541307

A chronic generalized bi-directional brain-machine interface.

PubMed

Rouse, A G; Stanslaski, S R; Cong, P; Jensen, R M; Afshar, P; Ullestad, D; Gupta, R; Molnar, G F; Moran, D W; Denison, T J

2011-06-01

A bi-directional neural interface (NI) system was designed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an existing neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain-machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson's disease). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.

A P300-based brain-computer interface aimed at operating electronic devices at home for severely disabled people.

PubMed

Corralejo, Rebeca; Nicolás-Alonso, Luis F; Alvarez, Daniel; Hornero, Roberto

2014-10-01

The present study aims at developing and assessing an assistive tool for operating electronic devices at home by means of a P300-based brain-computer interface (BCI). Fifteen severely impaired subjects participated in the study. The developed tool allows users to interact with their usual environment fulfilling their main needs. It allows for navigation through ten menus and to manage up to 113 control commands from eight electronic devices. Ten out of the fifteen subjects were able to operate the proposed tool with accuracy above 77 %. Eight out of them reached accuracies higher than 95 %. Moreover, bitrates up to 20.1 bit/min were achieved. The novelty of this study lies in the use of an environment control application in a real scenario: real devices managed by potential BCI end-users. Although impaired users might not be able to set up this system without aid of others, this study takes a significant step to evaluate the degree to which such populations could eventually operate a stand-alone system. Our results suggest that neither the type nor the degree of disability is a relevant issue to suitably operate a P300-based BCI. Hence, it could be useful to assist disabled people at home improving their personal autonomy.

A Review of EEG-Based Brain-Computer Interfaces as Access Pathways for Individuals with Severe Disabilities

ERIC Educational Resources Information Center

Moghimi, Saba; Kushki, Azadeh; Guerguerian, Anne Marie; Chau, Tom

2013-01-01

Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals.…

Biosensor Technologies for Augmented Brain-Computer Interfaces in the Next Decades

DTIC Science & Technology

2012-05-13

Research Triangle Park, NC 27709-2211 Augmented brain–computer interface (ABCI);biosensor; cognitive-state monitoring; electroencephalogram( EEG ); human...biosensor; cognitive-state monitoring; electroencephalogram ( EEG ); human brain imaging Manuscript received November 28, 2011; accepted December 20...magnetic reso- nance imaging (fMRI) [1], positron emission tomography (PET) [2], electroencephalograms ( EEGs ) and optical brain imaging techniques (i.e

A hybrid brain-computer interface-based mail client.

PubMed

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Li, Feng

2013-01-01

Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). With this BCI mail client, users can receive, read, write, and attach files to their mail. Using a BCI mouse that utilizes hybrid brain signals, that is, motor imagery and P300 potential, the user can select and activate the function keys and links on the mail client graphical user interface (GUI). An adaptive P300 speller is employed for text input. The system has been tested with 6 subjects, and the experimental results validate the efficacy of the proposed method.

A Hybrid Brain-Computer Interface-Based Mail Client

PubMed Central

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Li, Feng

2013-01-01

Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). With this BCI mail client, users can receive, read, write, and attach files to their mail. Using a BCI mouse that utilizes hybrid brain signals, that is, motor imagery and P300 potential, the user can select and activate the function keys and links on the mail client graphical user interface (GUI). An adaptive P300 speller is employed for text input. The system has been tested with 6 subjects, and the experimental results validate the efficacy of the proposed method. PMID:23690880

Task-dependent signal variations in EEG error-related potentials for brain-computer interfaces.

PubMed

Iturrate, I; Montesano, L; Minguez, J

2013-04-01

A major difficulty of brain-computer interface (BCI) technology is dealing with the noise of EEG and its signal variations. Previous works studied time-dependent non-stationarities for BCIs in which the user's mental task was independent of the device operation (e.g., the mental task was motor imagery and the operational task was a speller). However, there are some BCIs, such as those based on error-related potentials, where the mental and operational tasks are dependent (e.g., the mental task is to assess the device action and the operational task is the device action itself). The dependence between the mental task and the device operation could introduce a new source of signal variations when the operational task changes, which has not been studied yet. The aim of this study is to analyse task-dependent signal variations and their effect on EEG error-related potentials. The work analyses the EEG variations on the three design steps of BCIs: an electrophysiology study to characterize the existence of these variations, a feature distribution analysis and a single-trial classification analysis to measure the impact on the final BCI performance. The results demonstrate that a change in the operational task produces variations in the potentials, even when EEG activity exclusively originated in brain areas related to error processing is considered. Consequently, the extracted features from the signals vary, and a classifier trained with one operational task presents a significant loss of performance for other tasks, requiring calibration or adaptation for each new task. In addition, a new calibration for each of the studied tasks rapidly outperforms adaptive techniques designed in the literature to mitigate the EEG time-dependent non-stationarities.

Task-dependent signal variations in EEG error-related potentials for brain-computer interfaces

NASA Astrophysics Data System (ADS)

Iturrate, I.; Montesano, L.; Minguez, J.

2013-04-01

Objective. A major difficulty of brain-computer interface (BCI) technology is dealing with the noise of EEG and its signal variations. Previous works studied time-dependent non-stationarities for BCIs in which the user’s mental task was independent of the device operation (e.g., the mental task was motor imagery and the operational task was a speller). However, there are some BCIs, such as those based on error-related potentials, where the mental and operational tasks are dependent (e.g., the mental task is to assess the device action and the operational task is the device action itself). The dependence between the mental task and the device operation could introduce a new source of signal variations when the operational task changes, which has not been studied yet. The aim of this study is to analyse task-dependent signal variations and their effect on EEG error-related potentials.Approach. The work analyses the EEG variations on the three design steps of BCIs: an electrophysiology study to characterize the existence of these variations, a feature distribution analysis and a single-trial classification analysis to measure the impact on the final BCI performance.Results and significance. The results demonstrate that a change in the operational task produces variations in the potentials, even when EEG activity exclusively originated in brain areas related to error processing is considered. Consequently, the extracted features from the signals vary, and a classifier trained with one operational task presents a significant loss of performance for other tasks, requiring calibration or adaptation for each new task. In addition, a new calibration for each of the studied tasks rapidly outperforms adaptive techniques designed in the literature to mitigate the EEG time-dependent non-stationarities.

Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models.

PubMed

Lee, Giljae; Matsunaga, Andréa; Dura-Bernal, Salvador; Zhang, Wenjie; Lytton, William W; Francis, Joseph T; Fortes, José Ab

2014-11-01

Development of more sophisticated implantable brain-machine interface (BMI) will require both interpretation of the neurophysiological data being measured and subsequent determination of signals to be delivered back to the brain. Computational models are the heart of the machine of BMI and therefore an essential tool in both of these processes. One approach is to utilize brain biomimetic models (BMMs) to develop and instantiate these algorithms. These then must be connected as hybrid systems in order to interface the BMM with in vivo data acquisition devices and prosthetic devices. The combined system then provides a test bed for neuroprosthetic rehabilitative solutions and medical devices for the repair and enhancement of damaged brain. We propose here a computer network-based design for this purpose, detailing its internal modules and data flows. We describe a prototype implementation of the design, enabling interaction between the Plexon Multichannel Acquisition Processor (MAP) server, a commercial tool to collect signals from microelectrodes implanted in a live subject and a BMM, a NEURON-based model of sensorimotor cortex capable of controlling a virtual arm. The prototype implementation supports an online mode for real-time simulations, as well as an offline mode for data analysis and simulations without real-time constraints, and provides binning operations to discretize continuous input to the BMM and filtering operations for dealing with noise. Evaluation demonstrated that the implementation successfully delivered monkey spiking activity to the BMM through LAN environments, respecting real-time constraints.

Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future.

PubMed

Huggins, Jane E; Guger, Christoph; Ziat, Mounia; Zander, Thorsten O; Taylor, Denise; Tangermann, Michael; Soria-Frisch, Aureli; Simeral, John; Scherer, Reinhold; Rupp, Rüdiger; Ruffini, Giulio; Robinson, Douglas K R; Ramsey, Nick F; Nijholt, Anton; Müller-Putz, Gernot; McFarland, Dennis J; Mattia, Donatella; Lance, Brent J; Kindermans, Pieter-Jan; Iturrate, Iñaki; Herff, Christian; Gupta, Disha; Do, An H; Collinger, Jennifer L; Chavarriaga, Ricardo; Chase, Steven M; Bleichner, Martin G; Batista, Aaron; Anderson, Charles W; Aarnoutse, Erik J

2017-01-01

The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

Eyes-closed hybrid brain-computer interface employing frontal brain activation.

PubMed

Shin, Jaeyoung; Müller, Klaus-Robert; Hwang, Han-Jeong

2018-01-01

Brain-computer interfaces (BCIs) have been studied extensively in order to establish a non-muscular communication channel mainly for patients with impaired motor functions. However, many limitations remain for BCIs in clinical use. In this study, we propose a hybrid BCI that is based on only frontal brain areas and can be operated in an eyes-closed state for end users with impaired motor and declining visual functions. In our experiment, electroencephalography (EEG) and near-infrared spectroscopy (NIRS) were simultaneously measured while 12 participants performed mental arithmetic (MA) and remained relaxed (baseline state: BL). To evaluate the feasibility of the hybrid BCI, we classified MA- from BL-related brain activation. We then compared classification accuracies using two unimodal BCIs (EEG and NIRS) and the hybrid BCI in an offline mode. The classification accuracy of the hybrid BCI (83.9 ± 10.3%) was shown to be significantly higher than those of unimodal EEG-based (77.3 ± 15.9%) and NIRS-based BCI (75.9 ± 6.3%). The analytical results confirmed performance improvement with the hybrid BCI, particularly for only frontal brain areas. Our study shows that an eyes-closed hybrid BCI approach based on frontal areas could be applied to neurodegenerative patients who lost their motor functions, including oculomotor functions.

A comparative study: use of a Brain-computer Interface (BCI) device by people with cerebral palsy in interaction with computers.

PubMed

Heidrich, Regina O; Jensen, Emely; Rebelo, Francisco; Oliveira, Tiago

2015-01-01

This article presents a comparative study among people with cerebral palsy and healthy controls, of various ages, using a Brain-computer Interface (BCI) device. The research is qualitative in its approach. Researchers worked with Observational Case Studies. People with cerebral palsy and healthy controls were evaluated in Portugal and in Brazil. The study aimed to develop a study for product evaluation in order to perceive whether people with cerebral palsy could interact with the computer and compare whether their performance is similar to that of healthy controls when using the Brain-computer Interface. Ultimately, it was found that there are no significant differences between people with cerebral palsy in the two countries, as well as between populations without cerebral palsy (healthy controls).

Mushu, a free- and open source BCI signal acquisition, written in Python.

PubMed

Venthur, Bastian; Blankertz, Benjamin

2012-01-01

The following paper describes Mushu, a signal acquisition software for retrieval and online streaming of Electroencephalography (EEG) data. It is written, but not limited, to the needs of Brain Computer Interfacing (BCI). It's main goal is to provide a unified interface to EEG data regardless of the amplifiers used. It runs under all major operating systems, like Windows, Mac OS and Linux, is written in Python and is free- and open source software licensed under the terms of the GNU General Public License.

Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness

NASA Astrophysics Data System (ADS)

Pichiorri, F.; De Vico Fallani, F.; Cincotti, F.; Babiloni, F.; Molinari, M.; Kleih, S. C.; Neuper, C.; Kübler, A.; Mattia, D.

2011-04-01

The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.

Modulation of Posterior Alpha Activity by Spatial Attention Allows for Controlling A Continuous Brain-Computer Interface.

PubMed

Horschig, Jörn M; Oosterheert, Wouter; Oostenveld, Robert; Jensen, Ole

2015-11-01

Here we report that the modulation of alpha activity by covert attention can be used as a control signal in an online brain-computer interface, that it is reliable, and that it is robust. Subjects were instructed to orient covert visual attention to the left or right hemifield. We decoded the direction of attention from the magnetoencephalogram by a template matching classifier and provided the classification outcome to the subject in real-time using a novel graphical user interface. Training data for the templates were obtained from a Posner-cueing task conducted just before the BCI task. Eleven subjects participated in four sessions each. Eight of the subjects achieved classification rates significantly above chance level. Subjects were able to significantly increase their performance from the first to the second session. Individual patterns of posterior alpha power remained stable throughout the four sessions and did not change with increased performance. We conclude that posterior alpha power can successfully be used as a control signal in brain-computer interfaces. We also discuss several ideas for further improving the setup and propose future research based on solid hypotheses about behavioral consequences of modulating neuronal oscillations by brain computer interfacing.

Workload measurement in a communication application operated through a P300-based brain-computer interface

NASA Astrophysics Data System (ADS)

Riccio, A.; Leotta, F.; Bianchi, L.; Aloise, F.; Zickler, C.; Hoogerwerf, E.-J.; Kübler, A.; Mattia, D.; Cincotti, F.

2011-04-01

Advancing the brain-computer interface (BCI) towards practical applications in technology-based assistive solutions for people with disabilities requires coping with problems of accessibility and usability to increase user acceptance and satisfaction. The main objective of this study was to introduce a usability-oriented approach in the assessment of BCI technology development by focusing on evaluation of the user's subjective workload and satisfaction. The secondary aim was to compare two applications for a P300-based BCI. Eight healthy subjects were asked to use an assistive technology solution which integrates the P300-based BCI with commercially available software under two conditions—visual stimuli needed to evoke the P300 response were either overlaid onto the application's graphical user interface or presented on a separate screen. The two conditions were compared for effectiveness (level of performance), efficiency (subjective workload measured by means of NASA-TXL) and satisfaction of the user. Although no significant difference in usability could be detected between the two conditions, the methodology proved to be an effective tool to highlight weaknesses in the technical solution.

Treatment effectiveness of brain-computer interface training for patients with focal hand dystonia: A double-case study.

PubMed

Hashimoto, Yasunari; Ota, Tetsuo; Mukaino, Masahiko; Ushiba, Junichi

2013-01-01

Neuronal mechanism underlying dystonia is poorly understood. Dystonia can be treated with botulinum toxin injections or deep brain stimulation but these methods are not available for every patient therefore we need to consider other methods Our study aimed to develop a novel rehabilitation training using brain-computer interface system that decreases neural overexcitation in the sensorimotor cortex by bypassing brain and external world without the normal neuromuscular pathway. To achieve this purpose, we recorded electroencephalograms (10 channels) and forearm electromyograms (3 channels) from 2 patients with the diagnosis of writer's cramp and healthy control participants as a preliminary experiment. The patients were trained to control amplitude of their electroencephalographic signal using feedback from the brain-computer interface for 1 hour a day and then continued the training twice a month. After the 5-month training, a patient clearly showed reduction of dystonic movement during writing.

[The current state of the brain-computer interface problem].

PubMed

Shurkhay, V A; Aleksandrova, E V; Potapov, A A; Goryainov, S A

2015-01-01

It was only 40 years ago that the first PC appeared. Over this period, rather short in historical terms, we have witnessed the revolutionary changes in lives of individuals and the entire society. Computer technologies are tightly connected with any field, either directly or indirectly. We can currently claim that computers are manifold superior to a human mind in terms of a number of parameters; however, machines lack the key feature: they are incapable of independent thinking (like a human). However, the key to successful development of humankind is collaboration between the brain and the computer rather than competition. Such collaboration when a computer broadens, supplements, or replaces some brain functions is known as the brain-computer interface. Our review focuses on real-life implementation of this collaboration.

Decoding of intended saccade direction in an oculomotor brain-computer interface

NASA Astrophysics Data System (ADS)

Jia, Nan; Brincat, Scott L.; Salazar-Gómez, Andrés F.; Panko, Mikhail; Guenther, Frank H.; Miller, Earl K.

2017-08-01

Objective. To date, invasive brain-computer interface (BCI) research has largely focused on replacing lost limb functions using signals from the hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication (AAC) application. Here we aimed to demonstrate the feasibility of a BCI utilizing the oculomotor system. Approach. We developed a chronic intracortical BCI in monkeys to decode intended saccadic eye movement direction using activity from multiple frontal cortical areas. Main results. Intended saccade direction could be decoded in real time with high accuracy, particularly at contralateral locations. Accurate decoding was evident even at the beginning of the BCI session; no extensive BCI experience was necessary. High-frequency (80-500 Hz) local field potential magnitude provided the best performance, even over spiking activity, thus simplifying future BCI applications. Most of the information came from the frontal and supplementary eye fields, with relatively little contribution from dorsolateral prefrontal cortex. Significance. Our results support the feasibility of high-accuracy intracortical oculomotor BCIs that require little or no practice to operate and may be ideally suited for ‘point and click’ computer operation as used in most current AAC systems.

Analysis of User Interaction with a Brain-Computer Interface Based on Steady-State Visually Evoked Potentials: Case Study of a Game

PubMed Central

de Carvalho, Sarah Negreiros; Costa, Thiago Bulhões da Silva; Attux, Romis; Hornung, Heiko Horst; Arantes, Dalton Soares

2018-01-01

This paper presents a systematic analysis of a game controlled by a Brain-Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEP). The objective is to understand BCI systems from the Human-Computer Interface (HCI) point of view, by observing how the users interact with the game and evaluating how the interface elements influence the system performance. The interactions of 30 volunteers with our computer game, named “Get Coins,” through a BCI based on SSVEP, have generated a database of brain signals and the corresponding responses to a questionnaire about various perceptual parameters, such as visual stimulation, acoustic feedback, background music, visual contrast, and visual fatigue. Each one of the volunteers played one match using the keyboard and four matches using the BCI, for comparison. In all matches using the BCI, the volunteers achieved the goals of the game. Eight of them achieved a perfect score in at least one of the four matches, showing the feasibility of the direct communication between the brain and the computer. Despite this successful experiment, adaptations and improvements should be implemented to make this innovative technology accessible to the end user. PMID:29849549

Analysis of User Interaction with a Brain-Computer Interface Based on Steady-State Visually Evoked Potentials: Case Study of a Game.

PubMed

Leite, Harlei Miguel de Arruda; de Carvalho, Sarah Negreiros; Costa, Thiago Bulhões da Silva; Attux, Romis; Hornung, Heiko Horst; Arantes, Dalton Soares

2018-01-01

This paper presents a systematic analysis of a game controlled by a Brain-Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEP). The objective is to understand BCI systems from the Human-Computer Interface (HCI) point of view, by observing how the users interact with the game and evaluating how the interface elements influence the system performance. The interactions of 30 volunteers with our computer game, named "Get Coins," through a BCI based on SSVEP, have generated a database of brain signals and the corresponding responses to a questionnaire about various perceptual parameters, such as visual stimulation, acoustic feedback, background music, visual contrast, and visual fatigue. Each one of the volunteers played one match using the keyboard and four matches using the BCI, for comparison. In all matches using the BCI, the volunteers achieved the goals of the game. Eight of them achieved a perfect score in at least one of the four matches, showing the feasibility of the direct communication between the brain and the computer. Despite this successful experiment, adaptations and improvements should be implemented to make this innovative technology accessible to the end user.

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment.

PubMed

Aricò, Pietro; Borghini, Gianluca; Di Flumeri, Gianluca; Colosimo, Alfredo; Bonelli, Stefano; Golfetti, Alessia; Pozzi, Simone; Imbert, Jean-Paul; Granger, Géraud; Benhacene, Raïlane; Babiloni, Fabio

2016-01-01

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under - and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (É cole Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

PubMed Central

Aricò, Pietro; Borghini, Gianluca; Di Flumeri, Gianluca; Colosimo, Alfredo; Bonelli, Stefano; Golfetti, Alessia; Pozzi, Simone; Imbert, Jean-Paul; Granger, Géraud; Benhacene, Raïlane; Babiloni, Fabio

2016-01-01

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload. PMID:27833542

Brain-Computer Interface with Inhibitory Neurons Reveals Subtype-Specific Strategies.

PubMed

Mitani, Akinori; Dong, Mingyuan; Komiyama, Takaki

2018-01-08

Brain-computer interfaces have seen an increase in popularity due to their potential for direct neuroprosthetic applications for amputees and disabled individuals. Supporting this promise, animals-including humans-can learn even arbitrary mapping between the activity of cortical neurons and movement of prosthetic devices [1-4]. However, the performance of neuroprosthetic device control has been nowhere near that of limb control in healthy individuals, presenting a dire need to improve the performance. One potential limitation is the fact that previous work has not distinguished diverse cell types in the neocortex, even though different cell types possess distinct functions in cortical computations [5-7] and likely distinct capacities to control brain-computer interfaces. Here, we made a first step in addressing this issue by tracking the plastic changes of three major types of cortical inhibitory neurons (INs) during a neuron-pair operant conditioning task using two-photon imaging of IN subtypes expressing GCaMP6f. Mice were rewarded when the activity of the positive target neuron (N+) exceeded that of the negative target neuron (N-) beyond a set threshold. Mice improved performance with all subtypes, but the strategies were subtype specific. When parvalbumin (PV)-expressing INs were targeted, the activity of N- decreased. However, targeting of somatostatin (SOM)- and vasoactive intestinal peptide (VIP)-expressing INs led to an increase of the N+ activity. These results demonstrate that INs can be individually modulated in a subtype-specific manner and highlight the versatility of neural circuits in adapting to new demands by using cell-type-specific strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Robot Control Through Brain Computer Interface For Patterns Generation

NASA Astrophysics Data System (ADS)

Belluomo, P.; Bucolo, M.; Fortuna, L.; Frasca, M.

2011-09-01

A Brain Computer Interface (BCI) system processes and translates neuronal signals, that mainly comes from EEG instruments, into commands for controlling electronic devices. This system can allow people with motor disabilities to control external devices through the real-time modulation of their brain waves. In this context an EEG-based BCI system that allows creative luminous artistic representations is here presented. The system that has been designed and realized in our laboratory interfaces the BCI2000 platform performing real-time analysis of EEG signals with a couple of moving luminescent twin robots. Experiments are also presented.

TiD-Introducing and Benchmarking an Event-Delivery System for Brain-Computer Interfaces.

PubMed

Breitwieser, Christian; Tavella, Michele; Schreuder, Martijn; Cincotti, Febo; Leeb, Robert; Muller-Putz, Gernot R

2017-12-01

In this paper, we present and analyze an event distribution system for brain-computer interfaces. Events are commonly used to mark and describe incidents during an experiment and are therefore critical for later data analysis or immediate real-time processing. The presented approach, called Tools for brain-computer interaction interface D (TiD), delivers messages in XML format via a buslike system using transmission control protocol connections or shared memory. A dedicated server dispatches TiD messages to distributed or local clients. The TiD message is designed to be flexible and contains time stamps for event synchronization, whereas events describe incidents, which occur during an experiment. TiD was tested extensively toward stability and latency. The effect of an occurring event jitter was analyzed and benchmarked on a reference implementation under different conditions as gigabit and 100-Mb Ethernet or Wi-Fi with a different number of event receivers. A 3-dB signal attenuation, which occurs when averaging jitter influenced trials aligned by events, is starting to become visible at around 1-2 kHz in the case of a gigabit connection. Mean event distribution times across operating systems are ranging from 0.3 to 0.5ms for a gigabit network connection for 10 6 events. Results for other environmental conditions are available in this paper. References already using TiD for event distribution are provided showing the applicability of TiD for event delivery with distributed or local clients.

A review of classification algorithms for EEG-based brain-computer interfaces.

PubMed

Lotte, F; Congedo, M; Lécuyer, A; Lamarche, F; Arnaldi, B

2007-06-01

In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

Robotics, stem cells, and brain-computer interfaces in rehabilitation and recovery from stroke: updates and advances.

PubMed

Boninger, Michael L; Wechsler, Lawrence R; Stein, Joel

2014-11-01

The aim of this study was to describe the current state and latest advances in robotics, stem cells, and brain-computer interfaces in rehabilitation and recovery for stroke. The authors of this summary recently reviewed this work as part of a national presentation. The article represents the information included in each area. Each area has seen great advances and challenges as products move to market and experiments are ongoing. Robotics, stem cells, and brain-computer interfaces all have tremendous potential to reduce disability and lead to better outcomes for patients with stroke. Continued research and investment will be needed as the field moves forward. With this investment, the potential for recovery of function is likely substantial.

Robotics, Stem Cells and Brain Computer Interfaces in Rehabilitation and Recovery from Stroke; Updates and Advances

PubMed Central

Boninger, Michael L; Wechsler, Lawrence R.; Stein, Joel

2014-01-01

Objective To describe the current state and latest advances in robotics, stem cells, and brain computer interfaces in rehabilitation and recovery for stroke. Design The authors of this summary recently reviewed this work as part of a national presentation. The paper represents the information included in each area. Results Each area has seen great advances and challenges as products move to market and experiments are ongoing. Conclusion Robotics, stem cells, and brain computer interfaces all have tremendous potential to reduce disability and lead to better outcomes for patients with stroke. Continued research and investment will be needed as the field moves forward. With this investment, the potential for recovery of function is likely substantial PMID:25313662

Multimodality instrument for tissue characterization

NASA Technical Reports Server (NTRS)

Mah, Robert W. (Inventor); Andrews, Russell J. (Inventor)

2004-01-01

A system with multimodality instrument for tissue identification includes a computer-controlled motor driven heuristic probe with a multisensory tip. For neurosurgical applications, the instrument is mounted on a stereotactic frame for the probe to penetrate the brain in a precisely controlled fashion. The resistance of the brain tissue being penetrated is continually monitored by a miniaturized strain gauge attached to the probe tip. Other modality sensors may be mounted near the probe tip to provide real-time tissue characterizations and the ability to detect the proximity of blood vessels, thus eliminating errors normally associated with registration of pre-operative scans, tissue swelling, elastic tissue deformation, human judgement, etc., and rendering surgical procedures safer, more accurate, and efficient. A neural network program adaptively learns the information on resistance and other characteristic features of normal brain tissue during the surgery and provides near real-time modeling. A fuzzy logic interface to the neural network program incorporates expert medical knowledge in the learning process. Identification of abnormal brain tissue is determined by the detection of change and comparison with previously learned models of abnormal brain tissues. The operation of the instrument is controlled through a user friendly graphical interface. Patient data is presented in a 3D stereographics display. Acoustic feedback of selected information may optionally be provided. Upon detection of the close proximity to blood vessels or abnormal brain tissue, the computer-controlled motor immediately stops probe penetration. The use of this system will make surgical procedures safer, more accurate, and more efficient. Other applications of this system include the detection, prognosis and treatment of breast cancer, prostate cancer, spinal diseases, and use in general exploratory surgery.

Brain Computer Interfaces for Enhanced Interaction with Mobile Robot Agents

DTIC Science & Technology

2016-07-27

synergistic and complementary way. This project focused on acquiring a mobile robotic agent platform that can be used to explore these interfaces...providing a test environment where the human control of a robot agent can be experimentally validated in 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...Distribution Unlimited UU UU UU UU 27-07-2016 17-Sep-2013 16-Sep-2014 Final Report: Brain Computer Interfaces for Enhanced Interactions with Mobile Robot

Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS.

PubMed

Vansteensel, Mariska J; Pels, Elmar G M; Bleichner, Martin G; Branco, Mariana P; Denison, Timothy; Freudenburg, Zachary V; Gosselaar, Peter; Leinders, Sacha; Ottens, Thomas H; Van Den Boom, Max A; Van Rijen, Peter C; Aarnoutse, Erik J; Ramsey, Nick F

2016-11-24

Options for people with severe paralysis who have lost the ability to communicate orally are limited. We describe a method for communication in a patient with late-stage amyotrophic lateral sclerosis (ALS), involving a fully implanted brain-computer interface that consists of subdural electrodes placed over the motor cortex and a transmitter placed subcutaneously in the left side of the thorax. By attempting to move the hand on the side opposite the implanted electrodes, the patient accurately and independently controlled a computer typing program 28 weeks after electrode placement, at the equivalent of two letters per minute. The brain-computer interface offered autonomous communication that supplemented and at times supplanted the patient's eye-tracking device. (Funded by the Government of the Netherlands and the European Union; ClinicalTrials.gov number, NCT02224469 .).

An innovative workstation

NASA Technical Reports Server (NTRS)

Villarreal, James

1987-01-01

A workstation was developed which uses the operator's eye movements and position to determine the placement of the cursor on a computer screen. A brainwave sensing technology overview and an introduction to the known rhythms or signals generated by the brain are given. This is followed by a descriptive explanation of the Ocular Attention Interface System (OASIS) and its intended integration into the proposed testbed.

A passive brain-computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks.

PubMed

Aricò, P; Borghini, G; Di Flumeri, G; Colosimo, A; Pozzi, S; Babiloni, F

2016-01-01

In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states. The objective of the proposed study was to provide an example of p-BCIs used to evaluate the users' mental workload in a real operational environment. For this purpose, through the facilities provided by the École Nationale de l'Aviation Civile of Toulouse (France), the cerebral activity of 12 professional air traffic control officers (ATCOs) has been recorded while performing high realistic air traffic management scenarios. By the analysis of the ATCOs' brain activity (electroencephalographic signal-EEG) and the subjective workload perception (instantaneous self-assessment) provided by both the examined ATCOs and external air traffic control experts, it has been possible to estimate and evaluate the variation of the mental workload under which the controllers were operating. The results showed (i) a high significant correlation between the neurophysiological and the subjective workload assessment, and (ii) a high reliability over time (up to a month) of the proposed algorithm that was also able to maintain high discrimination accuracies by using a low number of EEG electrodes (~3 EEG channels). In conclusion, the proposed methodology demonstrated the suitability of p-BCI systems in operational environments and the advantages of the neurophysiological measures with respect to the subjective ones. © 2016 Elsevier B.V. All rights reserved.

Adaptive Offset Correction for Intracortical Brain Computer Interfaces

PubMed Central

Homer, Mark L.; Perge, János A.; Black, Michael J.; Harrison, Matthew T.; Cash, Sydney S.; Hochberg, Leigh R.

2014-01-01

Intracortical brain computer interfaces (iBCIs) decode intended movement from neural activity for the control of external devices such as a robotic arm. Standard approaches include a calibration phase to estimate decoding parameters. During iBCI operation, the statistical properties of the neural activity can depart from those observed during calibration, sometimes hindering a user’s ability to control the iBCI. To address this problem, we adaptively correct the offset terms within a Kalman filter decoder via penalized maximum likelihood estimation. The approach can handle rapid shifts in neural signal behavior (on the order of seconds) and requires no knowledge of the intended movement. The algorithm, called MOCA, was tested using simulated neural activity and evaluated retrospectively using data collected from two people with tetraplegia operating an iBCI. In 19 clinical research test cases, where a nonadaptive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors (10.6 ±10.1%; p<0.05, pairwise t-test). MOCA did not significantly change the error in the remaining 23 cases where a nonadaptive Kalman filter already performed well. These results suggest that MOCA provides more robust decoding than the standard Kalman filter for iBCIs. PMID:24196868

Adaptive offset correction for intracortical brain-computer interfaces.

PubMed

Homer, Mark L; Perge, Janos A; Black, Michael J; Harrison, Matthew T; Cash, Sydney S; Hochberg, Leigh R

2014-03-01

Intracortical brain-computer interfaces (iBCIs) decode intended movement from neural activity for the control of external devices such as a robotic arm. Standard approaches include a calibration phase to estimate decoding parameters. During iBCI operation, the statistical properties of the neural activity can depart from those observed during calibration, sometimes hindering a user's ability to control the iBCI. To address this problem, we adaptively correct the offset terms within a Kalman filter decoder via penalized maximum likelihood estimation. The approach can handle rapid shifts in neural signal behavior (on the order of seconds) and requires no knowledge of the intended movement. The algorithm, called multiple offset correction algorithm (MOCA), was tested using simulated neural activity and evaluated retrospectively using data collected from two people with tetraplegia operating an iBCI. In 19 clinical research test cases, where a nonadaptive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors ( 10.6 ± 10.1% ; p < 0.05, pairwise t-test). MOCA did not significantly change the error in the remaining 23 cases where a nonadaptive Kalman filter already performed well. These results suggest that MOCA provides more robust decoding than the standard Kalman filter for iBCIs.

User Experience May be Producing Greater Heart Rate Variability than Motor Imagery Related Control Tasks during the User-System Adaptation in Brain-Computer Interfaces

PubMed Central

Alonso-Valerdi, Luz M.; Gutiérrez-Begovich, David A.; Argüello-García, Janet; Sepulveda, Francisco; Ramírez-Mendoza, Ricardo A.

2016-01-01

Brain-computer interface (BCI) is technology that is developing fast, but it remains inaccurate, unreliable and slow due to the difficulty to obtain precise information from the brain. Consequently, the involvement of other biosignals to decode the user control tasks has risen in importance. A traditional way to operate a BCI system is via motor imagery (MI) tasks. As imaginary movements activate similar cortical structures and vegetative mechanisms as a voluntary movement does, heart rate variability (HRV) has been proposed as a parameter to improve the detection of MI related control tasks. However, HR is very susceptible to body needs and environmental demands, and as BCI systems require high levels of attention, perceptual processing and mental workload, it is important to assess the practical effectiveness of HRV. The present study aimed to determine if brain and heart electrical signals (HRV) are modulated by MI activity used to control a BCI system, or if HRV is modulated by the user perceptions and responses that result from the operation of a BCI system (i.e., user experience). For this purpose, a database of 11 participants who were exposed to eight different situations was used. The sensory-cognitive load (intake and rejection tasks) was controlled in those situations. Two electrophysiological signals were utilized: electroencephalography and electrocardiography. From those biosignals, event-related (de-)synchronization maps and event-related HR changes were respectively estimated. The maps and the HR changes were cross-correlated in order to verify if both biosignals were modulated due to MI activity. The results suggest that HR varies according to the experience undergone by the user in a BCI working environment, and not because of the MI activity used to operate the system. PMID:27458384

Rapid prototyping of an EEG-based brain-computer interface (BCI).

PubMed

Guger, C; Schlögl, A; Neuper, C; Walterspacher, D; Strein, T; Pfurtscheller, G

2001-03-01

The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).

Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface

NASA Astrophysics Data System (ADS)

Blankertz, Benjamin; Tangermann, Michael; Vidaurre, Carmen; Dickhaus, Thorsten; Sannelli, Claudia; Popescu, Florin; Fazli, Siamac; Danóczy, Márton; Curio, Gabriel; Müller, Klaus-Robert

The Berlin Brain-Computer Interface Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2-5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.

Bigger data for big data: from Twitter to brain-computer interfaces.

PubMed

Roesch, Etienne B; Stahl, Frederic; Gaber, Mohamed Medhat

2014-02-01

We are sympathetic with Bentley et al.'s attempt to encompass the wisdom of crowds in a generative model, but posit that a successful attempt at using big data will include more sensitive measurements, more varied sources of information, and will also build from the indirect information available through technology, from ancillary technical features to data from brain-computer interfaces.

Training to use a commercial brain-computer interface as access technology: a case study.

PubMed

Taherian, Sarvnaz; Selitskiy, Dmitry; Pau, James; Davies, T Claire; Owens, R Glynn

2016-01-01

This case study describes how an individual with spastic quadriplegic cerebral palsy was trained over a period of four weeks to use a commercial electroencephalography (EEG)-based brain-computer interface (BCI). The participant spent three sessions exploring the system, and seven sessions playing a game focused on EEG feedback training of left and right arm motor imagery and a customised, training game paradigm was employed. The participant showed improvement in the production of two distinct EEG patterns. The participant's performance was influenced by motivation, fatigue and concentration. Six weeks post-training the participant could still control the BCI and used this to type a sentence using an augmentative and alternative communication application on a wirelessly linked device. The results from this case study highlight the importance of creating a dynamic, relevant and engaging training environment for BCIs. Implications for Rehabilitation Customising a training paradigm to suit the users' interests can influence adherence to assistive technology training. Mood, fatigue, physical illness and motivation influence the usability of a brain-computer interface. Commercial brain-computer interfaces, which require little set up time, may be used as access technology for individuals with severe disabilities.

Brain-computer interface using P300 and virtual reality: a gaming approach for treating ADHD.

PubMed

Rohani, Darius Adam; Sorensen, Helge B D; Puthusserypady, Sadasivan

2014-01-01

This paper presents a novel brain-computer interface (BCI) system aiming at the rehabilitation of attention-deficit/hyperactive disorder in children. It uses the P300 potential in a series of feedback games to improve the subjects' attention. We applied a support vector machine (SVM) using temporal and template-based features to detect these P300 responses. In an experimental setup using five subjects, an average error below 30% was achieved. To make it more challenging the BCI system has been embedded inside an immersive 3D virtual reality (VR) classroom with simulated distractions, which was created by combining a low-cost infrared camera and an "off-axis perspective projection" algorithm. This system is intended for kids by operating with four electrodes, as well as a non-intrusive VR setting. With the promising results, and considering the simplicity of the scheme, we hope to encourage future studies to adapt the techniques presented in this study.

Effects of brain-computer interface-based functional electrical stimulation on brain activation in stroke patients: a pilot randomized controlled trial.

PubMed

Chung, EunJung; Kim, Jung-Hee; Park, Dae-Sung; Lee, Byoung-Hee

2015-03-01

[Purpose] This study sought to determine the effects of brain-computer interface-based functional electrical stimulation (BCI-FES) on brain activation in patients with stroke. [Subjects] The subjects were randomized to in a BCI-FES group (n=5) and a functional electrical stimulation (FES) group (n=5). [Methods] Patients in the BCI-FES group received ankle dorsiflexion training with FES for 30 minutes per day, 5 times under the brain-computer interface-based program. The FES group received ankle dorsiflexion training with FES for the same amount of time. [Results] The BCI-FES group demonstrated significant differences in the frontopolar regions 1 and 2 attention indexes, and frontopolar 1 activation index. The FES group demonstrated no significant differences. There were significant differences in the frontopolar 1 region activation index between the two groups after the interventions. [Conclusion] The results of this study suggest that BCI-FES training may be more effective in stimulating brain activation than only FES training in patients recovering from stroke.

Student teaching and research laboratory focusing on brain-computer interface paradigms--A creative environment for computer science students.

PubMed

Rutkowski, Tomasz M

2015-08-01

This paper presents an applied concept of a brain-computer interface (BCI) student research laboratory (BCI-LAB) at the Life Science Center of TARA, University of Tsukuba, Japan. Several successful case studies of the student projects are reviewed together with the BCI Research Award 2014 winner case. The BCI-LAB design and project-based teaching philosophy is also explained. Future teaching and research directions summarize the review.

Researching and Reducing the Health Burden of Stroke

MedlinePlus

... the result of continuing research to map the brain and interface it with a computer to enable stroke patients to regain function. How important is the new effort to map the human brain? The brain is more complex than any computer ...

Soft brain-machine interfaces for assistive robotics: A novel control approach.

PubMed

Schiatti, Lucia; Tessadori, Jacopo; Barresi, Giacinto; Mattos, Leonardo S; Ajoudani, Arash

2017-07-01

Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients. The interface is composed of an eye-tracking system, for an intuitive and reliable control of a robotic arm system's trajectories, and a Brain-Computer Interface (BCI) unit, for the control of the robot Cartesian stiffness, which determines the interaction forces between the robot and environment. The latter control is achieved by estimating in real-time a unidimensional index from user's electroencephalographic (EEG) signals, which provides the probability of a neutral or active state. This estimated state is then translated into a stiffness value for the robotic arm, allowing a reliable modulation of the robot's impedance. A preliminary evaluation of this hybrid interface concept provided evidence on the effective execution of tasks with dynamic uncertainties, demonstrating the great potential of this control method in BMI applications for self-service and clinical care.

Near infrared spectroscopy based brain-computer interface

NASA Astrophysics Data System (ADS)

Ranganatha, Sitaram; Hoshi, Yoko; Guan, Cuntai

2005-04-01

A brain-computer interface (BCI) provides users with an alternative output channel other than the normal output path of the brain. BCI is being given much attention recently as an alternate mode of communication and control for the disabled, such as patients suffering from Amyotrophic Lateral Sclerosis (ALS) or "locked-in". BCI may also find applications in military, education and entertainment. Most of the existing BCI systems which rely on the brain's electrical activity use scalp EEG signals. The scalp EEG is an inherently noisy and non-linear signal. The signal is detrimentally affected by various artifacts such as the EOG, EMG, ECG and so forth. EEG is cumbersome to use in practice, because of the need for applying conductive gel, and the need for the subject to be immobile. There is an urgent need for a more accessible interface that uses a more direct measure of cognitive function to control an output device. The optical response of Near Infrared Spectroscopy (NIRS) denoting brain activation can be used as an alternative to electrical signals, with the intention of developing a more practical and user-friendly BCI. In this paper, a new method of brain-computer interface (BCI) based on NIRS is proposed. Preliminary results of our experiments towards developing this system are reported.

Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury

PubMed Central

Rupp, Rüdiger

2014-01-01

Brain computer interfaces (BCIs) are devices that measure brain activities and translate them into control signals used for a variety of applications. Among them are systems for communication, environmental control, neuroprostheses, exoskeletons, or restorative therapies. Over the last years the technology of BCIs has reached a level of matureness allowing them to be used not only in research experiments supervised by scientists, but also in clinical routine with patients with neurological impairments supervised by clinical personnel or caregivers. However, clinicians and patients face many challenges in the application of BCIs. This particularly applies to high spinal cord injured patients, in whom artificial ventilation, autonomic dysfunctions, neuropathic pain, or the inability to achieve a sufficient level of control during a short-term training may limit the successful use of a BCI. Additionally, spasmolytic medication and the acute stress reaction with associated episodes of depression may have a negative influence on the modulation of brain waves and therefore the ability to concentrate over an extended period of time. Although BCIs seem to be a promising assistive technology for individuals with high spinal cord injury systematic investigations are highly needed to obtain realistic estimates of the percentage of users that for any reason may not be able to operate a BCI in a clinical setting. PMID:25309420

Brain-computer interfaces in neurological rehabilitation.

PubMed

Daly, Janis J; Wolpaw, Jonathan R

2008-11-01

Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface

NASA Astrophysics Data System (ADS)

LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

2013-08-01

Objective. At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s-1. Significance. Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface.

PubMed

LaFleur, Karl; Cassady, Kaitlin; Doud, Alexander; Shades, Kaleb; Rogin, Eitan; He, Bin

2013-08-01

At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s(-1). Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

Prediction of brain deformations and risk of traumatic brain injury due to closed-head impact: quantitative analysis of the effects of boundary conditions and brain tissue constitutive model.

PubMed

Wang, Fang; Han, Yong; Wang, Bingyu; Peng, Qian; Huang, Xiaoqun; Miller, Karol; Wittek, Adam

2018-05-12

In this study, we investigate the effects of modelling choices for the brain-skull interface (layers of tissues between the brain and skull that determine boundary conditions for the brain) and the constitutive model of brain parenchyma on the brain responses under violent impact as predicted using computational biomechanics model. We used the head/brain model from Total HUman Model for Safety (THUMS)-extensively validated finite element model of the human body that has been applied in numerous injury biomechanics studies. The computations were conducted using a well-established nonlinear explicit dynamics finite element code LS-DYNA. We employed four approaches for modelling the brain-skull interface and four constitutive models for the brain tissue in the numerical simulations of the experiments on post-mortem human subjects exposed to violent impacts reported in the literature. The brain-skull interface models included direct representation of the brain meninges and cerebrospinal fluid, outer brain surface rigidly attached to the skull, frictionless sliding contact between the brain and skull, and a layer of spring-type cohesive elements between the brain and skull. We considered Ogden hyperviscoelastic, Mooney-Rivlin hyperviscoelastic, neo-Hookean hyperviscoelastic and linear viscoelastic constitutive models of the brain tissue. Our study indicates that the predicted deformations within the brain and related brain injury criteria are strongly affected by both the approach of modelling the brain-skull interface and the constitutive model of the brain parenchyma tissues. The results suggest that accurate prediction of deformations within the brain and risk of brain injury due to violent impact using computational biomechanics models may require representation of the meninges and subarachnoidal space with cerebrospinal fluid in the model and application of hyperviscoelastic (preferably Ogden-type) constitutive model for the brain tissue.

EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone

PubMed Central

Debener, Stefan; Emkes, Reiner; Volkening, Nils; Fudickar, Sebastian; Bleichner, Martin G.

2017-01-01

Objective Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. Approach In order to implement a closed-loop brain-computer interface (BCI) on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user. Main Results We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback. Significance We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms. PMID:29349070

EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone.

PubMed

Blum, Sarah; Debener, Stefan; Emkes, Reiner; Volkening, Nils; Fudickar, Sebastian; Bleichner, Martin G

2017-01-01

Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. In order to implement a closed-loop brain-computer interface (BCI) on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user. We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback. We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms.

Plug&Play Brain-Computer Interfaces for effective Active and Assisted Living control.

PubMed

Mora, Niccolò; De Munari, Ilaria; Ciampolini, Paolo; Del R Millán, José

2017-08-01

Brain-Computer Interfaces (BCI) rely on the interpretation of brain activity to provide people with disabilities with an alternative/augmentative interaction path. In light of this, BCI could be considered as enabling technology in many fields, including Active and Assisted Living (AAL) systems control. Interaction barriers could be removed indeed, enabling user with severe motor impairments to gain control over a wide range of AAL features. In this paper, a cost-effective BCI solution, targeted (but not limited) to AAL system control is presented. A custom hardware module is briefly reviewed, while signal processing techniques are covered in more depth. Steady-state visual evoked potentials (SSVEP) are exploited in this work as operating BCI protocol. In contrast with most common SSVEP-BCI approaches, we propose the definition of a prediction confidence indicator, which is shown to improve overall classification accuracy. The confidence indicator is derived without any subject-specific approach and is stable across users: it can thus be defined once and then shared between different persons. This allows some kind of Plug&Play interaction. Furthermore, by modelling rest/idle periods with the confidence indicator, it is possible to detect active control periods and separate them from "background activity": this is capital for real-time, self-paced operation. Finally, the indicator also allows to dynamically choose the most appropriate observation window length, improving system's responsiveness and user's comfort. Good results are achieved under such operating conditions, achieving, for instance, a false positive rate of 0.16 min -1 , which outperform current literature findings.

Co-Adaptive Aiding and Automation Enhance Operator Performance

DTIC Science & Technology

2013-03-01

activation system. There is a close relation between physiologically activated adaptive aiding and brain- computer interfaces ( BCI ). BCI here refers...classification of EEG signals (Farwell & Donchin, 1988). Physiologically activated adaptive aiding is, in a sense, a special case of BCI wherein the...as passive BCI , e.g. Zander, Kothe, Jatzev, & 3 Distribution A: Approved for public release; distribution unlimited. 88 ABW Cleared 05/13/2013

Passive BCI in Operational Environments: Insights, Recent Advances, and Future Trends.

PubMed

Arico, Pietro; Borghini, Gianluca; Di Flumeri, Gianluca; Sciaraffa, Nicolina; Colosimo, Alfredo; Babiloni, Fabio

2017-07-01

This minireview aims to highlight recent important aspects to consider and evaluate when passive brain-computer interface (pBCI) systems would be developed and used in operational environments, and remarks future directions of their applications. Electroencephalography (EEG) based pBCI has become an important tool for real-time analysis of brain activity since it could potentially provide covertly-without distracting the user from the main task-and objectively-not affected by the subjective judgment of an observer or the user itself-information about the operator cognitive state. Different examples of pBCI applications in operational environments and new adaptive interface solutions have been presented and described. In addition, a general overview regarding the correct use of machine learning techniques (e.g., which algorithm to use, common pitfalls to avoid, etc.) in the pBCI field has been provided. Despite recent innovations on algorithms and neurotechnology, pBCI systems are not completely ready to enter the market yet, mainly due to limitations of the EEG electrodes technology, and algorithms reliability and capability in real settings. High complexity and safety critical systems (e.g., airplanes, ATM interfaces) should adapt their behaviors and functionality accordingly to the user' actual mental state. Thus, technologies (i.e., pBCIs) able to measure in real time the user's mental states would result very useful in such "high risk" environments to enhance human machine interaction, and so increase the overall safety.

The PennBMBI: Design of a General Purpose Wireless Brain-Machine-Brain Interface System.

PubMed

Liu, Xilin; Zhang, Milin; Subei, Basheer; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

2015-04-01

In this paper, a general purpose wireless Brain-Machine-Brain Interface (BMBI) system is presented. The system integrates four battery-powered wireless devices for the implementation of a closed-loop sensorimotor neural interface, including a neural signal analyzer, a neural stimulator, a body-area sensor node and a graphic user interface implemented on the PC end. The neural signal analyzer features a four channel analog front-end with configurable bandpass filter, gain stage, digitization resolution, and sampling rate. The target frequency band is configurable from EEG to single unit activity. A noise floor of 4.69 μVrms is achieved over a bandwidth from 0.05 Hz to 6 kHz. Digital filtering, neural feature extraction, spike detection, sensing-stimulating modulation, and compressed sensing measurement are realized in a central processing unit integrated in the analyzer. A flash memory card is also integrated in the analyzer. A 2-channel neural stimulator with a compliance voltage up to ± 12 V is included. The stimulator is capable of delivering unipolar or bipolar, charge-balanced current pulses with programmable pulse shape, amplitude, width, pulse train frequency and latency. A multi-functional sensor node, including an accelerometer, a temperature sensor, a flexiforce sensor and a general sensor extension port has been designed. A computer interface is designed to monitor, control and configure all aforementioned devices via a wireless link, according to a custom designed communication protocol. Wireless closed-loop operation between the sensory devices, neural stimulator, and neural signal analyzer can be configured. The proposed system was designed to link two sites in the brain, bridging the brain and external hardware, as well as creating new sensory and motor pathways for clinical practice. Bench test and in vivo experiments are performed to verify the functions and performances of the system.

Feasibility of a Hybrid Brain-Computer Interface for Advanced Functional Electrical Therapy

PubMed Central

Savić, Andrej M.; Malešević, Nebojša M.; Popović, Mirjana B.

2014-01-01

We present a feasibility study of a novel hybrid brain-computer interface (BCI) system for advanced functional electrical therapy (FET) of grasp. FET procedure is improved with both automated stimulation pattern selection and stimulation triggering. The proposed hybrid BCI comprises the two BCI control signals: steady-state visual evoked potentials (SSVEP) and event-related desynchronization (ERD). The sequence of the two stages, SSVEP-BCI and ERD-BCI, runs in a closed-loop architecture. The first stage, SSVEP-BCI, acts as a selector of electrical stimulation pattern that corresponds to one of the three basic types of grasp: palmar, lateral, or precision. In the second stage, ERD-BCI operates as a brain switch which activates the stimulation pattern selected in the previous stage. The system was tested in 6 healthy subjects who were all able to control the device with accuracy in a range of 0.64–0.96. The results provided the reference data needed for the planned clinical study. This novel BCI may promote further restoration of the impaired motor function by closing the loop between the “will to move” and contingent temporally synchronized sensory feedback. PMID:24616644

Probabilistic co-adaptive brain-computer interfacing

NASA Astrophysics Data System (ADS)

Bryan, Matthew J.; Martin, Stefan A.; Cheung, Willy; Rao, Rajesh P. N.

2013-12-01

Objective. Brain-computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach. We introduce a new approach to brain-computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions (‘beliefs’) over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP’s reward function can be updated over time to allow for co-adaptive behaviour. Main results. We illustrate our approach using a simple non-invasive BCI which optimizes the speed-accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user’s control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward. Significance. Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user’s brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user’s changing circumstances.

Virtual reality and brain computer interface in neurorehabilitation

PubMed Central

Dahdah, Marie; Driver, Simon; Parsons, Thomas D.; Richter, Kathleen M.

2016-01-01

The potential benefit of technology to enhance recovery after central nervous system injuries is an area of increasing interest and exploration. The primary emphasis to date has been motor recovery/augmentation and communication. This paper introduces two original studies to demonstrate how advanced technology may be integrated into subacute rehabilitation. The first study addresses the feasibility of brain computer interface with patients on an inpatient spinal cord injury unit. The second study explores the validity of two virtual environments with acquired brain injury as part of an intensive outpatient neurorehabilitation program. These preliminary studies support the feasibility of advanced technologies in the subacute stage of neurorehabilitation. These modalities were well tolerated by participants and could be incorporated into patients' inpatient and outpatient rehabilitation regimens without schedule disruptions. This paper expands the limited literature base regarding the use of advanced technologies in the early stages of recovery for neurorehabilitation populations and speaks favorably to the potential integration of brain computer interface and virtual reality technologies as part of a multidisciplinary treatment program. PMID:27034541

Brain–computer interfaces: communication and restoration of movement in paralysis

PubMed Central

Birbaumer, Niels; Cohen, Leonardo G

2007-01-01

The review describes the status of brain–computer or brain–machine interface research. We focus on non-invasive brain–computer interfaces (BCIs) and their clinical utility for direct brain communication in paralysis and motor restoration in stroke. A large gap between the promises of invasive animal and human BCI preparations and the clinical reality characterizes the literature: while intact monkeys learn to execute more or less complex upper limb movements with spike patterns from motor brain regions alone without concomitant peripheral motor activity usually after extensive training, clinical applications in human diseases such as amyotrophic lateral sclerosis and paralysis from stroke or spinal cord lesions show only limited success, with the exception of verbal communication in paralysed and locked-in patients. BCIs based on electroencephalographic potentials or oscillations are ready to undergo large clinical studies and commercial production as an adjunct or a major assisted communication device for paralysed and locked-in patients. However, attempts to train completely locked-in patients with BCI communication after entering the complete locked-in state with no remaining eye movement failed. We propose that a lack of contingencies between goal directed thoughts and intentions may be at the heart of this problem. Experiments with chronically curarized rats support our hypothesis; operant conditioning and voluntary control of autonomic physiological functions turned out to be impossible in this preparation. In addition to assisted communication, BCIs consisting of operant learning of EEG slow cortical potentials and sensorimotor rhythm were demonstrated to be successful in drug resistant focal epilepsy and attention deficit disorder. First studies of non-invasive BCIs using sensorimotor rhythm of the EEG and MEG in restoration of paralysed hand movements in chronic stroke and single cases of high spinal cord lesions show some promise, but need extensive evaluation in well-controlled experiments. Invasive BMIs based on neuronal spike patterns, local field potentials or electrocorticogram may constitute the strategy of choice in severe cases of stroke and spinal cord paralysis. Future directions of BCI research should include the regulation of brain metabolism and blood flow and electrical and magnetic stimulation of the human brain (invasive and non-invasive). A series of studies using BOLD response regulation with functional magnetic resonance imaging (fMRI) and near infrared spectroscopy demonstrated a tight correlation between voluntary changes in brain metabolism and behaviour. PMID:17234696

A qualitative study adopting a user-centered approach to design and validate a brain computer interface for cognitive rehabilitation for people with brain injury.

PubMed

Martin, Suzanne; Armstrong, Elaine; Thomson, Eileen; Vargiu, Eloisa; Solà, Marc; Dauwalder, Stefan; Miralles, Felip; Daly Lynn, Jean

2017-07-14

Cognitive rehabilitation is established as a core intervention within rehabilitation programs following a traumatic brain injury (TBI). Digitally enabled assistive technologies offer opportunities for clinicians to increase remote access to rehabilitation supporting transition into home. Brain Computer Interface (BCI) systems can harness the residual abilities of individuals with limited function to gain control over computers through their brain waves. This paper presents an online cognitive rehabilitation application developed with therapists, to work remotely with people who have TBI, who will use BCI at home to engage in the therapy. A qualitative research study was completed with people who are community dwellers post brain injury (end users), and a cohort of therapists involved in cognitive rehabilitation. A user-centered approach over three phases in the development, design and feasibility testing of this cognitive rehabilitation application included two tasks (Find-a-Category and a Memory Card task). The therapist could remotely prescribe activity with different levels of difficulty. The service user had a home interface which would present the therapy activities. This novel work was achieved by an international consortium of academics, business partners and service users.

Paralyzed subject controls telepresence mobile robot using novel sEMG brain-computer interface: case study.

PubMed

Lyons, Kenneth R; Joshi, Sanjay S

2013-06-01

Here we demonstrate the use of a new singlesignal surface electromyography (sEMG) brain-computer interface (BCI) to control a mobile robot in a remote location. Previous work on this BCI has shown that users are able to perform cursor-to-target tasks in two-dimensional space using only a single sEMG signal by continuously modulating the signal power in two frequency bands. Using the cursor-to-target paradigm, targets are shown on the screen of a tablet computer so that the user can select them, commanding the robot to move in different directions for a fixed distance/angle. A Wifi-enabled camera transmits video from the robot's perspective, giving the user feedback about robot motion. Current results show a case study with a C3-C4 spinal cord injury (SCI) subject using a single auricularis posterior muscle site to navigate a simple obstacle course. Performance metrics for operation of the BCI as well as completion of the telerobotic command task are developed. It is anticipated that this noninvasive and mobile system will open communication opportunities for the severely paralyzed, possibly using only a single sensor.

Multi-brain fusion and applications to intelligence analysis

NASA Astrophysics Data System (ADS)

Stoica, A.; Matran-Fernandez, A.; Andreou, D.; Poli, R.; Cinel, C.; Iwashita, Y.; Padgett, C.

2013-05-01

In a rapid serial visual presentation (RSVP) images are shown at an extremely rapid pace. Yet, the images can still be parsed by the visual system to some extent. In fact, the detection of specific targets in a stream of pictures triggers a characteristic electroencephalography (EEG) response that can be recognized by a brain-computer interface (BCI) and exploited for automatic target detection. Research funded by DARPA's Neurotechnology for Intelligence Analysts program has achieved speed-ups in sifting through satellite images when adopting this approach. This paper extends the use of BCI technology from individual analysts to collaborative BCIs. We show that the integration of information in EEGs collected from multiple operators results in performance improvements compared to the single-operator case.

An Investment Behavior Analysis using by Brain Computer Interface

NASA Astrophysics Data System (ADS)

Suzuki, Kyoko; Kinoshita, Kanta; Miyagawa, Kazuhiro; Shiomi, Shinichi; Misawa, Tadanobu; Shimokawa, Tetsuya

In this paper, we will construct a new Brain Computer Interface (BCI), for the purpose of analyzing human's investment decision makings. The BCI is made up of three functional parts which take roles of, measuring brain information, determining market price in an artificial market, and specifying investment decision model, respectively. When subjects make decisions, their brain information is conveyed to the part of specifying investment decision model through the part of measuring brain information, whereas, their decisions of investment order are sent to the part of artificial market to form market prices. Both the support vector machine and the 3 layered perceptron are used to assess the investment decision model. In order to evaluate our BCI, we conduct an experiment in which subjects and a computer trader agent trade shares of stock in the artificial market and test how the computer trader agent can forecast market price formation and investment decision makings from the brain information of subjects. The result of the experiment shows that the brain information can improve the accuracy of forecasts, and so the computer trader agent can supply market liquidity to stabilize market volatility without his loss.

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

NASA Astrophysics Data System (ADS)

Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

2017-08-01

Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

NASA Astrophysics Data System (ADS)

Meng, Jianjun; Zhang, Shuying; Bekyo, Angeliki; Olsoe, Jaron; Baxter, Bryan; He, Bin

2016-12-01

Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.

Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.

PubMed

Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

2016-09-01

The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

Improved Targeting Through Collaborative Decision-Making and Brain Computer Interfaces

NASA Technical Reports Server (NTRS)

Stoica, Adrian; Barrero, David F.; McDonald-Maier, Klaus

2013-01-01

This paper reports a first step toward a brain-computer interface (BCI) for collaborative targeting. Specifically, we explore, from a broad perspective, how the collaboration of a group of people can increase the performance on a simple target identification task. To this end, we requested a group of people to identify the location and color of a sequence of targets appearing on the screen and measured the time and accuracy of the response. The individual results are compared to a collective identification result determined by simple majority voting, with random choice in case of drawn. The results are promising, as the identification becomes significantly more reliable even with this simple voting and a small number of people (either odd or even number) involved in the decision. In addition, the paper briefly analyzes the role of brain-computer interfaces in collaborative targeting, extending the targeting task by using a BCI instead of a mechanical response.

A brain-computer interface controlled mail client.

PubMed

Yu, Tianyou; Li, Yuanqing; Long, Jinyi; Wang, Cong

2013-01-01

In this paper, we propose a brain-computer interface (BCI) based mail client. This system is controlled by hybrid features extracted from scalp-recorded electroencephalographic (EEG). We emulate the computer mouse by the motor imagery-based mu rhythm and the P300 potential. Furthermore, an adaptive P300 speller is included to provide text input function. With this BCI mail client, users can receive, read, write mails, as well as attach files in mail writing. The system has been tested on 3 subjects. Experimental results show that mail communication with this system is feasible.

Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general.

PubMed

Zander, Thorsten O; Kothe, Christian

2011-04-01

Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.

Towards Zero Training for Brain-Computer Interfacing

PubMed Central

Krauledat, Matthias; Tangermann, Michael; Blankertz, Benjamin; Müller, Klaus-Robert

2008-01-01

Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neurofeedback to produce fixed stereotypical patterns of brain activity. In the machine learning approach, a widely adapted method for dealing with those variances is to record a so called calibration measurement on the beginning of each session in order to optimize spatial filters and classifiers specifically for each subject and each day. This adaptation of the system to the individual brain signature of each user relieves from the need of extensive user training. In this paper we suggest a new method that overcomes the requirement of these time-consuming calibration recordings for long-term BCI users. The method takes advantage of knowledge collected in previous sessions: By a novel technique, prototypical spatial filters are determined which have better generalization properties compared to single-session filters. In particular, they can be used in follow-up sessions without the need to recalibrate the system. This way the calibration periods can be dramatically shortened or even completely omitted for these ‘experienced’ BCI users. The feasibility of our novel approach is demonstrated with a series of online BCI experiments. Although performed without any calibration measurement at all, no loss of classification performance was observed. PMID:18698427

[The P300 based brain-computer interface: effect of stimulus position in a stimulus train].

PubMed

Ganin, I P; Shishkin, S L; Kochetova, A G; Kaplan, A Ia

2012-01-01

The P300 brain-computer interface (BCI) is currently the most efficient BCI. This interface is based on detection of the P300 wave of the brain potentials evoked when a symbol related to the intended input is highlighted. To increase operation speed of the P300 BCI, reduction of the number of stimuli repetitions is needed. This reduction leads to increase of the relative contribution to the input symbol detection from the reaction to the first target stimulus. It is known that the event-related potentials (ERP) to the first stimulus presentations can be different from the ERP to stimuli presented latter. In particular, the amplitude of responses to the first stimulus presentations is often increased, which is beneficial for their recognition by the BCI. However, this effect was not studied within the BCI framework. The current study examined the ERP obtained from healthy participants (n = 14) in the standard P300 BCI paradigm using 10 trials, as well as in the modified P300 BCI with stimuli presented on moving objects in triple-trial (n = 6) and single-trial (n = 6) stimulation modes. Increased ERP amplitude was observed in response to the first target stimuli in both conditions, as well as in the single-trial mode comparing to triple-trial. We discuss the prospects of using the specific features of the ERP to first stimuli and the single-trial ERP for optimizing the high-speed modes in the P300 BCIs.

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

PubMed

Lajoie, Guillaume; Krouchev, Nedialko I; Kalaska, John F; Fairhall, Adrienne L; Fetz, Eberhard E

2017-02-01

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface

PubMed Central

Lajoie, Guillaume; Kalaska, John F.; Fairhall, Adrienne L.; Fetz, Eberhard E.

2017-01-01

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity. PMID:28151957

A Brain-Computer Interface Project Applied in Computer Engineering

ERIC Educational Resources Information Center

Katona, Jozsef; Kovari, Attila

2016-01-01

Keeping up with novel methods and keeping abreast of new applications are crucial issues in engineering education. In brain research, one of the most significant research areas in recent decades, many developments have application in both modern engineering technology and education. New measurement methods in the observation of brain activity open…

Geometry aware Stationary Subspace Analysis

DTIC Science & Technology

2016-11-22

approach to handling non-stationarity is to remove or minimize it before attempting to analyze the data. In the context of brain computer interface ( BCI ...context of brain computer interface ( BCI ) data analysis, two such note-worthy methods are stationary subspace analysis (SSA) (von Bünau et al., 2009a... BCI systems, is sCSP. Its goal is to project the data onto a subspace in which the various data classes are more separable. The sCSP method directs

Brain-computer interface for alertness estimation and improving

NASA Astrophysics Data System (ADS)

Hramov, Alexander; Maksimenko, Vladimir; Hramova, Marina

2018-02-01

Using wavelet analysis of the signals of electrical brain activity (EEG), we study the processes of neural activity, associated with perception of visual stimuli. We demonstrate that the brain can process visual stimuli in two scenarios: (i) perception is characterized by destruction of the alpha-waves and increase in the high-frequency (beta) activity, (ii) the beta-rhythm is not well pronounced, while the alpha-wave energy remains unchanged. The special experiments show that the motivation factor initiates the first scenario, explained by the increasing alertness. Based on the obtained results we build the brain-computer interface and demonstrate how the degree of the alertness can be estimated and controlled in real experiment.

Examining sensory ability, feature matching and assessment-based adaptation for a brain-computer interface using the steady-state visually evoked potential.

PubMed

Brumberg, Jonathan S; Nguyen, Anh; Pitt, Kevin M; Lorenz, Sean D

2018-01-31

We investigated how overt visual attention and oculomotor control influence successful use of a visual feedback brain-computer interface (BCI) for accessing augmentative and alternative communication (AAC) devices in a heterogeneous population of individuals with profound neuromotor impairments. BCIs are often tested within a single patient population limiting generalization of results. This study focuses on examining individual sensory abilities with an eye toward possible interface adaptations to improve device performance. Five individuals with a range of neuromotor disorders participated in four-choice BCI control task involving the steady state visually evoked potential. The BCI graphical interface was designed to simulate a commercial AAC device to examine whether an integrated device could be used successfully by individuals with neuromotor impairment. All participants were able to interact with the BCI and highest performance was found for participants able to employ an overt visual attention strategy. For participants with visual deficits to due to impaired oculomotor control, effective performance increased after accounting for mismatches between the graphical layout and participant visual capabilities. As BCIs are translated from research environments to clinical applications, the assessment of BCI-related skills will help facilitate proper device selection and provide individuals who use BCI the greatest likelihood of immediate and long term communicative success. Overall, our results indicate that adaptations can be an effective strategy to reduce barriers and increase access to BCI technology. These efforts should be directed by comprehensive assessments for matching individuals to the most appropriate device to support their complex communication needs. Implications for Rehabilitation Brain computer interfaces using the steady state visually evoked potential can be integrated with an augmentative and alternative communication device to provide access to language and literacy for individuals with neuromotor impairment. Comprehensive assessments are needed to fully understand the sensory, motor, and cognitive abilities of individuals who may use brain-computer interfaces for proper feature matching as selection of the most appropriate device including optimization device layouts and control paradigms. Oculomotor impairments negatively impact brain-computer interfaces that use the steady state visually evoked potential, but modifications to place interface stimuli and communication items in the intact visual field can improve successful outcomes.

A Semisupervised Support Vector Machines Algorithm for BCI Systems

PubMed Central

Qin, Jianzhao; Li, Yuanqing; Sun, Wei

2007-01-01

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm. PMID:18368141

The Role of Visual Noise in Influencing Mental Load and Fatigue in a Steady-State Motion Visual Evoked Potential-Based Brain-Computer Interface.

PubMed

Xie, Jun; Xu, Guanghua; Luo, Ailing; Li, Min; Zhang, Sicong; Han, Chengcheng; Yan, Wenqiang

2017-08-14

As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. However, its benefits come at the cost of mental load and fatigue occurring in the concentration on the visual stimuli. Noise, as a ubiquitous random perturbation with the power of randomness, may be exploited by the human visual system to enhance higher-level brain functions. In this study, a novel steady-state motion visual evoked potential (SSMVEP, i.e., one kind of SSVEP)-based BCI paradigm with spatiotemporal visual noise was used to investigate the influence of noise on the compensation of mental load and fatigue deterioration during prolonged attention tasks. Changes in α , θ , θ + α powers, θ / α ratio, and electroencephalography (EEG) properties of amplitude, signal-to-noise ratio (SNR), and online accuracy, were used to evaluate mental load and fatigue. We showed that presenting a moderate visual noise to participants could reliably alleviate the mental load and fatigue during online operation of visual BCI that places demands on the attentional processes. This demonstrated that noise could provide a superior solution to the implementation of visual attention controlling-based BCI applications.

Conscious brain-to-brain communication in humans using non-invasive technologies.

PubMed

Grau, Carles; Ginhoux, Romuald; Riera, Alejandro; Nguyen, Thanh Lam; Chauvat, Hubert; Berg, Michel; Amengual, Julià L; Pascual-Leone, Alvaro; Ruffini, Giulio

2014-01-01

Human sensory and motor systems provide the natural means for the exchange of information between individuals, and, hence, the basis for human civilization. The recent development of brain-computer interfaces (BCI) has provided an important element for the creation of brain-to-brain communication systems, and precise brain stimulation techniques are now available for the realization of non-invasive computer-brain interfaces (CBI). These technologies, BCI and CBI, can be combined to realize the vision of non-invasive, computer-mediated brain-to-brain (B2B) communication between subjects (hyperinteraction). Here we demonstrate the conscious transmission of information between human brains through the intact scalp and without intervention of motor or peripheral sensory systems. Pseudo-random binary streams encoding words were transmitted between the minds of emitter and receiver subjects separated by great distances, representing the realization of the first human brain-to-brain interface. In a series of experiments, we established internet-mediated B2B communication by combining a BCI based on voluntary motor imagery-controlled electroencephalographic (EEG) changes with a CBI inducing the conscious perception of phosphenes (light flashes) through neuronavigated, robotized transcranial magnetic stimulation (TMS), with special care taken to block sensory (tactile, visual or auditory) cues. Our results provide a critical proof-of-principle demonstration for the development of conscious B2B communication technologies. More fully developed, related implementations will open new research venues in cognitive, social and clinical neuroscience and the scientific study of consciousness. We envision that hyperinteraction technologies will eventually have a profound impact on the social structure of our civilization and raise important ethical issues.

Conscious Brain-to-Brain Communication in Humans Using Non-Invasive Technologies

PubMed Central

Grau, Carles; Ginhoux, Romuald; Riera, Alejandro; Nguyen, Thanh Lam; Chauvat, Hubert; Berg, Michel; Amengual, Julià L.; Pascual-Leone, Alvaro; Ruffini, Giulio

2014-01-01

Human sensory and motor systems provide the natural means for the exchange of information between individuals, and, hence, the basis for human civilization. The recent development of brain-computer interfaces (BCI) has provided an important element for the creation of brain-to-brain communication systems, and precise brain stimulation techniques are now available for the realization of non-invasive computer-brain interfaces (CBI). These technologies, BCI and CBI, can be combined to realize the vision of non-invasive, computer-mediated brain-to-brain (B2B) communication between subjects (hyperinteraction). Here we demonstrate the conscious transmission of information between human brains through the intact scalp and without intervention of motor or peripheral sensory systems. Pseudo-random binary streams encoding words were transmitted between the minds of emitter and receiver subjects separated by great distances, representing the realization of the first human brain-to-brain interface. In a series of experiments, we established internet-mediated B2B communication by combining a BCI based on voluntary motor imagery-controlled electroencephalographic (EEG) changes with a CBI inducing the conscious perception of phosphenes (light flashes) through neuronavigated, robotized transcranial magnetic stimulation (TMS), with special care taken to block sensory (tactile, visual or auditory) cues. Our results provide a critical proof-of-principle demonstration for the development of conscious B2B communication technologies. More fully developed, related implementations will open new research venues in cognitive, social and clinical neuroscience and the scientific study of consciousness. We envision that hyperinteraction technologies will eventually have a profound impact on the social structure of our civilization and raise important ethical issues. PMID:25137064

Designing a hands-on brain computer interface laboratory course.

PubMed

Khalighinejad, Bahar; Long, Laura Kathleen; Mesgarani, Nima

2016-08-01

Devices and systems that interact with the brain have become a growing field of research and development in recent years. Engineering students are well positioned to contribute to both hardware development and signal analysis techniques in this field. However, this area has been left out of most engineering curricula. We developed an electroencephalography (EEG) based brain computer interface (BCI) laboratory course to educate students through hands-on experiments. The course is offered jointly by the Biomedical Engineering, Electrical Engineering, and Computer Science Departments of Columbia University in the City of New York and is open to senior undergraduate and graduate students. The course provides an effective introduction to the experimental design, neuroscience concepts, data analysis techniques, and technical skills required in the field of BCI.

A Procedure for Measuring Latencies in Brain-Computer Interfaces

PubMed Central

Wilson, J. Adam; Mellinger, Jürgen; Schalk, Gerwin; Williams, Justin

2011-01-01

Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware, software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration. PMID:20403781

Implantable brain computer interface: challenges to neurotechnology translation.

PubMed

Konrad, Peter; Shanks, Todd

2010-06-01

This article reviews three concepts related to implantable brain computer interface (BCI) devices being designed for human use: neural signal extraction primarily for motor commands, signal insertion to restore sensation, and technological challenges that remain. A significant body of literature has occurred over the past four decades regarding motor cortex signal extraction for upper extremity movement or computer interface. However, little is discussed regarding postural or ambulation command signaling. Auditory prosthesis research continues to represent the majority of literature on BCI signal insertion. Significant hurdles continue in the technological translation of BCI implants. These include developing a stable neural interface, significantly increasing signal processing capabilities, and methods of data transfer throughout the human body. The past few years, however, have provided extraordinary human examples of BCI implant potential. Despite technological hurdles, proof-of-concept animal and human studies provide significant encouragement that BCI implants may well find their way into mainstream medical practice in the foreseeable future.

A Direct Brain-to-Brain Interface in Humans

PubMed Central

Rao, Rajesh P. N.; Stocco, Andrea; Bryan, Matthew; Sarma, Devapratim; Youngquist, Tiffany M.; Wu, Joseph; Prat, Chantel S.

2014-01-01

We describe the first direct brain-to-brain interface in humans and present results from experiments involving six different subjects. Our non-invasive interface, demonstrated originally in August 2013, combines electroencephalography (EEG) for recording brain signals with transcranial magnetic stimulation (TMS) for delivering information to the brain. We illustrate our method using a visuomotor task in which two humans must cooperate through direct brain-to-brain communication to achieve a desired goal in a computer game. The brain-to-brain interface detects motor imagery in EEG signals recorded from one subject (the “sender”) and transmits this information over the internet to the motor cortex region of a second subject (the “receiver”). This allows the sender to cause a desired motor response in the receiver (a press on a touchpad) via TMS. We quantify the performance of the brain-to-brain interface in terms of the amount of information transmitted as well as the accuracies attained in (1) decoding the sender’s signals, (2) generating a motor response from the receiver upon stimulation, and (3) achieving the overall goal in the cooperative visuomotor task. Our results provide evidence for a rudimentary form of direct information transmission from one human brain to another using non-invasive means. PMID:25372285

Brain-computer interface design using alpha wave

NASA Astrophysics Data System (ADS)

Zhao, Hai-bin; Wang, Hong; Liu, Chong; Li, Chun-sheng

2010-01-01

A brain-computer interface (BCI) is a novel communication system that translates brain activity into commands for a computer or other electronic devices. BCI system based on non-invasive scalp electroencephalogram (EEG) has become a hot research area in recent years. BCI technology can help improve the quality of life and restore function for people with severe motor disabilities. In this study, we design a real-time asynchronous BCI system using Alpha wave. The basic theory of this BCI system is alpha wave-block phenomenon. Alpha wave is the most prominent wave in the whole realm of brain activity. This system includes data acquisition, feature selection and classification. The subject can use this system easily and freely choose anyone of four commands with only short-time training. The results of the experiment show that this BCI system has high classification accuracy, and has potential application for clinical engineering and is valuable for further research.

Soft drink effects on sensorimotor rhythm brain computer interface performance and resting-state spectral power.

PubMed

Mundahl, John; Jianjun Meng; He, Jeffrey; Bin He

2016-08-01

Brain-computer interface (BCI) systems allow users to directly control computers and other machines by modulating their brain waves. In the present study, we investigated the effect of soft drinks on resting state (RS) EEG signals and BCI control. Eight healthy human volunteers each participated in three sessions of BCI cursor tasks and resting state EEG. During each session, the subjects drank an unlabeled soft drink with either sugar, caffeine, or neither ingredient. A comparison of resting state spectral power shows a substantial decrease in alpha and beta power after caffeine consumption relative to control. Despite attenuation of the frequency range used for the control signal, caffeine average BCI performance was the same as control. Our work provides a useful characterization of caffeine, the world's most popular stimulant, on brain signal frequencies and their effect on BCI performance.

Soft, curved electrode systems capable of integration on the auricle as a persistent brain-computer interface.

PubMed

Norton, James J S; Lee, Dong Sup; Lee, Jung Woo; Lee, Woosik; Kwon, Ohjin; Won, Phillip; Jung, Sung-Young; Cheng, Huanyu; Jeong, Jae-Woong; Akce, Abdullah; Umunna, Stephen; Na, Ilyoun; Kwon, Yong Ho; Wang, Xiao-Qi; Liu, ZhuangJian; Paik, Ungyu; Huang, Yonggang; Bretl, Timothy; Yeo, Woon-Hong; Rogers, John A

2015-03-31

Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain-computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain-computer interface and elicitation of an event-related potential (P300 wave).

Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials

PubMed Central

2014-01-01

Background People with severe disabilities, e.g. due to neurodegenerative disease, depend on technology that allows for accurate wheelchair control. For those who cannot operate a wheelchair with a joystick, brain-computer interfaces (BCI) may offer a valuable option. Technology depending on visual or auditory input may not be feasible as these modalities are dedicated to processing of environmental stimuli (e.g. recognition of obstacles, ambient noise). Herein we thus validated the feasibility of a BCI based on tactually-evoked event-related potentials (ERP) for wheelchair control. Furthermore, we investigated use of a dynamic stopping method to improve speed of the tactile BCI system. Methods Positions of four tactile stimulators represented navigation directions (left thigh: move left; right thigh: move right; abdomen: move forward; lower neck: move backward) and N = 15 participants delivered navigation commands by focusing their attention on the desired tactile stimulus in an oddball-paradigm. Results Participants navigated a virtual wheelchair through a building and eleven participants successfully completed the task of reaching 4 checkpoints in the building. The virtual wheelchair was equipped with simulated shared-control sensors (collision avoidance), yet these sensors were rarely needed. Conclusion We conclude that most participants achieved tactile ERP-BCI control sufficient to reliably operate a wheelchair and dynamic stopping was of high value for tactile ERP classification. Finally, this paper discusses feasibility of tactile ERPs for BCI based wheelchair control. PMID:24428900

A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

PubMed

Lo, Chi-Chun; Chien, Tsung-Yi; Chen, Yu-Chun; Tsai, Shang-Ho; Fang, Wai-Chi; Lin, Bor-Shyh

2016-02-06

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.

Optimizing the Usability of Brain-Computer Interfaces.

PubMed

Zhang, Yin; Chase, Steve M

2018-05-01

Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.

Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives

PubMed Central

Yuan, Han; He, Bin

2014-01-01

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e. the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g. electroencephalography (EEG), and have demonstrated the capability of multi-dimensional prosthesis control. This article reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications are reviewed. Lastly, limitations of SMR-BCIs and future outlooks are also discussed. PMID:24759276

Evolution of brain-computer interfaces: going beyond classic motor physiology

PubMed Central

Leuthardt, Eric C.; Schalk, Gerwin; Roland, Jarod; Rouse, Adam; Moran, Daniel W.

2010-01-01

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future. PMID:19569892

Designing a Hands-On Brain Computer Interface Laboratory Course

PubMed Central

Khalighinejad, Bahar; Long, Laura Kathleen; Mesgarani, Nima

2017-01-01

Devices and systems that interact with the brain have become a growing field of research and development in recent years. Engineering students are well positioned to contribute to both hardware development and signal analysis techniques in this field. However, this area has been left out of most engineering curricula. We developed an electroencephalography (EEG) based brain computer interface (BCI) laboratory course to educate students through hands-on experiments. The course is offered jointly by the Biomedical Engineering, Electrical Engineering, and Computer Science Departments of Columbia University in the City of New York and is open to senior undergraduate and graduate students. The course provides an effective introduction to the experimental design, neuroscience concepts, data analysis techniques, and technical skills required in the field of BCI. PMID:28268946

Non-invasive brain-computer interface system: towards its application as assistive technology.

PubMed

Cincotti, Febo; Mattia, Donatella; Aloise, Fabio; Bufalari, Simona; Schalk, Gerwin; Oriolo, Giuseppe; Cherubini, Andrea; Marciani, Maria Grazia; Babiloni, Fabio

2008-04-15

The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain-computer interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI.

Non invasive Brain-Computer Interface system: towards its application as assistive technology

PubMed Central

Cincotti, Febo; Mattia, Donatella; Aloise, Fabio; Bufalari, Simona; Schalk, Gerwin; Oriolo, Giuseppe; Cherubini, Andrea; Marciani, Maria Grazia; Babiloni, Fabio

2010-01-01

The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain Computer Interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI. PMID:18394526

Brain-computer interface devices for patients with paralysis and amputation: a meeting report

NASA Astrophysics Data System (ADS)

Bowsher, K.; Civillico, E. F.; Coburn, J.; Collinger, J.; Contreras-Vidal, J. L.; Denison, T.; Donoghue, J.; French, J.; Getzoff, N.; Hochberg, L. R.; Hoffmann, M.; Judy, J.; Kleitman, N.; Knaack, G.; Krauthamer, V.; Ludwig, K.; Moynahan, M.; Pancrazio, J. J.; Peckham, P. H.; Pena, C.; Pinto, V.; Ryan, T.; Saha, D.; Scharen, H.; Shermer, S.; Skodacek, K.; Takmakov, P.; Tyler, D.; Vasudevan, S.; Wachrathit, K.; Weber, D.; Welle, C. G.; Ye, M.

2016-04-01

Objective. The Food and Drug Administration’s (FDA) Center for Devices and Radiological Health (CDRH) believes it is important to help stakeholders (e.g., manufacturers, health-care professionals, patients, patient advocates, academia, and other government agencies) navigate the regulatory landscape for medical devices. For innovative devices involving brain-computer interfaces, this is particularly important. Approach. Towards this goal, on 21 November, 2014, CDRH held an open public workshop on its White Oak, MD campus with the aim of fostering an open discussion on the scientific and clinical considerations associated with the development of brain-computer interface (BCI) devices, defined for the purposes of this workshop as neuroprostheses that interface with the central or peripheral nervous system to restore lost motor or sensory capabilities. Main results. This paper summarizes the presentations and discussions from that workshop. Significance. CDRH plans to use this information to develop regulatory considerations that will promote innovation while maintaining appropriate patient protections. FDA plans to build on advances in regulatory science and input provided in this workshop to develop guidance that provides recommendations for premarket submissions for BCI devices. These proceedings will be a resource for the BCI community during the development of medical devices for consumers.

Brain-computer interface devices for patients with paralysis and amputation: a meeting report.

PubMed

Bowsher, K; Civillico, E F; Coburn, J; Collinger, J; Contreras-Vidal, J L; Denison, T; Donoghue, J; French, J; Getzoff, N; Hochberg, L R; Hoffmann, M; Judy, J; Kleitman, N; Knaack, G; Krauthamer, V; Ludwig, K; Moynahan, M; Pancrazio, J J; Peckham, P H; Pena, C; Pinto, V; Ryan, T; Saha, D; Scharen, H; Shermer, S; Skodacek, K; Takmakov, P; Tyler, D; Vasudevan, S; Wachrathit, K; Weber, D; Welle, C G; Ye, M

2016-04-01

The Food and Drug Administration's (FDA) Center for Devices and Radiological Health (CDRH) believes it is important to help stakeholders (e.g., manufacturers, health-care professionals, patients, patient advocates, academia, and other government agencies) navigate the regulatory landscape for medical devices. For innovative devices involving brain-computer interfaces, this is particularly important. Towards this goal, on 21 November, 2014, CDRH held an open public workshop on its White Oak, MD campus with the aim of fostering an open discussion on the scientific and clinical considerations associated with the development of brain-computer interface (BCI) devices, defined for the purposes of this workshop as neuroprostheses that interface with the central or peripheral nervous system to restore lost motor or sensory capabilities. This paper summarizes the presentations and discussions from that workshop. CDRH plans to use this information to develop regulatory considerations that will promote innovation while maintaining appropriate patient protections. FDA plans to build on advances in regulatory science and input provided in this workshop to develop guidance that provides recommendations for premarket submissions for BCI devices. These proceedings will be a resource for the BCI community during the development of medical devices for consumers.

Tactile event-related potentials in amyotrophic lateral sclerosis (ALS): Implications for brain-computer interface.

PubMed

Silvoni, S; Konicar, L; Prats-Sedano, M A; Garcia-Cossio, E; Genna, C; Volpato, C; Cavinato, M; Paggiaro, A; Veser, S; De Massari, D; Birbaumer, N

2016-01-01

We investigated neurophysiological brain responses elicited by a tactile event-related potential paradigm in a sample of ALS patients. Underlying cognitive processes and neurophysiological signatures for brain-computer interface (BCI) are addressed. We stimulated the palm of the hand in a group of fourteen ALS patients and a control group of ten healthy participants and recorded electroencephalographic signals in eyes-closed condition. Target and non-target brain responses were analyzed and classified offline. Classification errors served as the basis for neurophysiological brain response sub-grouping. A combined behavioral and quantitative neurophysiological analysis of sub-grouped data showed neither significant between-group differences, nor significant correlations between classification performance and the ALS patients' clinical state. Taking sequential effects of stimuli presentation into account, analyses revealed mean classification errors of 19.4% and 24.3% in healthy participants and ALS patients respectively. Neurophysiological correlates of tactile stimuli presentation are not altered by ALS. Tactile event-related potentials can be used to monitor attention level and task performance in ALS and may constitute a viable basis for future BCIs. Implications for brain-computer interface implementation of the proposed method for patients in critical conditions, such as the late stage of ALS and the (completely) locked-in state, are discussed. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Unsupervised Decoding of Long-Term, Naturalistic Human Neural Recordings with Automated Video and Audio Annotations

PubMed Central

Wang, Nancy X. R.; Olson, Jared D.; Ojemann, Jeffrey G.; Rao, Rajesh P. N.; Brunton, Bingni W.

2016-01-01

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Implementing Brain Computer Interfaces (BCIs) outside carefully controlled experiments in laboratory settings requires adaptive and scalable strategies with minimal supervision. Here we describe an unsupervised approach to decoding neural states from naturalistic human brain recordings. We analyzed continuous, long-term electrocorticography (ECoG) data recorded over many days from the brain of subjects in a hospital room, with simultaneous audio and video recordings. We discovered coherent clusters in high-dimensional ECoG recordings using hierarchical clustering and automatically annotated them using speech and movement labels extracted from audio and video. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Interpretable behaviors were decoded from ECoG data, including moving, speaking and resting; the results were assessed by comparison with manual annotation. Discovered clusters were projected back onto the brain revealing features consistent with known functional areas, opening the door to automated functional brain mapping in natural settings. PMID:27148018

Software platform for rapid prototyping of NIRS brain computer interfacing techniques.

PubMed

Matthews, Fiachra; Soraghan, Christopher; Ward, Tomas E; Markham, Charles; Pearlmutter, Barak A

2008-01-01

This paper describes the control system of a next-generation optical brain-computer interface (BCI). Using functional near-infrared spectroscopy (fNIRS) as a BCI modality is a relatively new concept, and research has only begun to explore approaches for its implementation. It is necessary to have a system by which it is possible to investigate the signal processing and classification techniques available in the BCI community. Most importantly, these techniques must be easily testable in real-time applications. The system we describe was built using LABVIEW, a graphical programming language designed for interaction with National Instruments hardware. This platform allows complete configurability from hardware control and regulation, testing and filtering in a graphical interface environment.

Deep Space Network (DSN), Network Operations Control Center (NOCC) computer-human interfaces

NASA Technical Reports Server (NTRS)

Ellman, Alvin; Carlton, Magdi

1993-01-01

The Network Operations Control Center (NOCC) of the DSN is responsible for scheduling the resources of DSN, and monitoring all multi-mission spacecraft tracking activities in real-time. Operations performs this job with computer systems at JPL connected to over 100 computers at Goldstone, Australia and Spain. The old computer system became obsolete, and the first version of the new system was installed in 1991. Significant improvements for the computer-human interfaces became the dominant theme for the replacement project. Major issues required innovating problem solving. Among these issues were: How to present several thousand data elements on displays without overloading the operator? What is the best graphical representation of DSN end-to-end data flow? How to operate the system without memorizing mnemonics of hundreds of operator directives? Which computing environment will meet the competing performance requirements? This paper presents the technical challenges, engineering solutions, and results of the NOCC computer-human interface design.

A practical VEP-based brain-computer interface.

PubMed

Wang, Yijun; Wang, Ruiping; Gao, Xiaorong; Hong, Bo; Gao, Shangkai

2006-06-01

This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to > 90% of people with a high ITR in living environments.

On the use of interaction error potentials for adaptive brain computer interfaces.

PubMed

Llera, A; van Gerven, M A J; Gómez, V; Jensen, O; Kappen, H J

2011-12-01

We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance using artificial and MEG data. We show that the proposed adaptive framework significantly improves the static classification methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Portable non-invasive brain-computer interface: challenges and opportunities of optical modalities

NASA Astrophysics Data System (ADS)

Scholl, Clara A.; Hendrickson, Scott M.; Swett, Bruce A.; Fitch, Michael J.; Walter, Erich C.; McLoughlin, Michael P.; Chevillet, Mark A.; Blodgett, David W.; Hwang, Grace M.

2017-05-01

The development of portable non-invasive brain computer interface technologies with higher spatio-temporal resolution has been motivated by the tremendous success seen with implanted devices. This talk will discuss efforts to overcome several major obstacles to viability including approaches that promise to improve spatial and temporal resolution. Optical approaches in particular will be highlighted and the potential benefits of both Blood-Oxygen Level Dependent (BOLD) and Fast Optical Signal (FOS) will be discussed. Early-stage research into the correlations between neural activity and FOS will be explored.

Real-time functional magnetic imaging-brain-computer interface and virtual reality promising tools for the treatment of pedophilia.

PubMed

Renaud, Patrice; Joyal, Christian; Stoleru, Serge; Goyette, Mathieu; Weiskopf, Nikolaus; Birbaumer, Niels

2011-01-01

This chapter proposes a prospective view on using a real-time functional magnetic imaging (rt-fMRI) brain-computer interface (BCI) application as a new treatment for pedophilia. Neurofeedback mediated by interactive virtual stimuli is presented as the key process in this new BCI application. Results on the diagnostic discriminant power of virtual characters depicting sexual stimuli relevant to pedophilia are given. Finally, practical and ethical implications are briefly addressed. Copyright © 2011 Elsevier B.V. All rights reserved.

[Research of controlling of smart home system based on P300 brain-computer interface].

PubMed

Wang, Jinjia; Yang, Chengjie

2014-08-01

Using electroencephalogram (EEG) signal to control external devices has always been the research focus in the field of brain-computer interface (BCI). This is especially significant for those disabilities who have lost capacity of movements. In this paper, the P300-based BCI and the microcontroller-based wireless radio frequency (RF) technology are utilized to design a smart home control system, which can be used to control household appliances, lighting system, and security devices directly. Experiment results showed that the system was simple, reliable and easy to be populirised.

Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair.

PubMed

Ron-Angevin, Ricardo; Velasco-Álvarez, Francisco; Fernández-Rodríguez, Álvaro; Díaz-Estrella, Antonio; Blanca-Mena, María José; Vizcaíno-Martín, Francisco Javier

2017-05-30

Certain diseases affect brain areas that control the movements of the patients' body, thereby limiting their autonomy and communication capacity. Research in the field of Brain-Computer Interfaces aims to provide patients with an alternative communication channel not based on muscular activity, but on the processing of brain signals. Through these systems, subjects can control external devices such as spellers to communicate, robotic prostheses to restore limb movements, or domotic systems. The present work focus on the non-muscular control of a robotic wheelchair. A proposal to control a wheelchair through a Brain-Computer Interface based on the discrimination of only two mental tasks is presented in this study. The wheelchair displacement is performed with discrete movements. The control signals used are sensorimotor rhythms modulated through a right-hand motor imagery task or mental idle state. The peculiarity of the control system is that it is based on a serial auditory interface that provides the user with four navigation commands. The use of two mental tasks to select commands may facilitate control and reduce error rates compared to other endogenous control systems for wheelchairs. Seventeen subjects initially participated in the study; nine of them completed the three sessions of the proposed protocol. After the first calibration session, seven subjects were discarded due to a low control of their electroencephalographic signals; nine out of ten subjects controlled a virtual wheelchair during the second session; these same nine subjects achieved a medium accuracy level above 0.83 on the real wheelchair control session. The results suggest that more extensive training with the proposed control system can be an effective and safe option that will allow the displacement of a wheelchair in a controlled environment for potential users suffering from some types of motor neuron diseases.

Hardware enhance of brain computer interfaces

NASA Astrophysics Data System (ADS)

Wu, Jerry; Szu, Harold; Chen, Yuechen; Guo, Ran; Gu, Xixi

2015-05-01

The history of brain-computer interfaces (BCIs) starts with Hans Berger's discovery of the electrical activity of the human brain and the development of electroencephalography (EEG). Recent years, BCI researches are focused on Invasive, Partially invasive, and Non-invasive BCI. Furthermore, EEG can be also applied to telepathic communication which could provide the basis for brain-based communication using imagined speech. It is possible to use EEG signals to discriminate the vowels and consonants embedded in spoken and in imagined words and apply to military product. In this report, we begin with an example of using high density EEG with high electrode density and analysis the results by using BCIs. The BCIs in this work is enhanced by A field-programmable gate array (FPGA) board with optimized two dimension (2D) image Fast Fourier Transform (FFT) analysis.

A brain-computer interface with vibrotactile biofeedback for haptic information.

PubMed

Chatterjee, Aniruddha; Aggarwal, Vikram; Ramos, Ander; Acharya, Soumyadipta; Thakor, Nitish V

2007-10-17

It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy. A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance. Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.

Vibrotactile Feedback for Brain-Computer Interface Operation

PubMed Central

Cincotti, Febo; Kauhanen, Laura; Aloise, Fabio; Palomäki, Tapio; Caporusso, Nicholas; Jylänki, Pasi; Mattia, Donatella; Babiloni, Fabio; Vanacker, Gerolf; Nuttin, Marnix; Marciani, Maria Grazia; Millán, José del R.

2007-01-01

To be correctly mastered, brain-computer interfaces (BCIs) need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified. In three studies with 33 subjects (including 3 with spinal cord injury), we compared vibrotactile and visual feedback, addressing: (I) the feasibility of subjects' training to master their EEG rhythms using tactile feedback; (II) the compatibility of this form of feedback in presence of a visual distracter; (III) the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The stimulation protocol we developed supports a general usage of the tactors; preliminary experimentations. All studies indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task. In all experiments, vibrotactile feedback felt, after some training, more natural for both controls and SCI users. PMID:18354734

Investigating the effects of visual distractors on the performance of a motor imagery brain-computer interface.

PubMed

Emami, Zahra; Chau, Tom

2018-06-01

Brain-computer interfaces (BCIs) allow users to operate a device or application by means of cognitive activity. This technology will ultimately be used in real-world environments which include the presence of distractors. The purpose of the study was to determine the effect of visual distractors on BCI performance. Sixteen able-bodied participants underwent neurofeedback training to achieve motor imagery-guided BCI control in an online paradigm using electroencephalography (EEG) to measure neural signals. Participants then completed two sessions of the motor imagery EEG-BCI protocol in the presence of infrequent, small visual distractors. BCI performance was determined based on classification accuracy. The presence of distractors was found to affect motor imagery-specific patterns in mu and beta power. However, the distractors did not significantly affect the BCI classification accuracy; across participants, the mean classification accuracy was 81.5 ± 14% for non-distractor trials, and 78.3 ± 17% for distractor trials. This minimal consequence suggests that the BCI was robust to distractor effects, despite motor imagery-related brain activity being attenuated amid distractors. A BCI system that mitigates distraction-related effects may improve the ease of its use and ultimately facilitate the effective translation of the technology from the lab to the home. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.

PubMed

Chai, Rifai; Naik, Ganesh R; Ling, Sai Ho; Nguyen, Hung T

2017-01-07

One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.

Demonstration of a Semi-Autonomous Hybrid Brain-Machine Interface using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic

PubMed Central

McMullen, David P.; Hotson, Guy; Katyal, Kapil D.; Wester, Brock A.; Fifer, Matthew S.; McGee, Timothy G.; Harris, Andrew; Johannes, Matthew S.; Vogelstein, R. Jacob; Ravitz, Alan D.; Anderson, William S.; Thakor, Nitish V.; Crone, Nathan E.

2014-01-01

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 seconds for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs. PMID:24760914

Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.

PubMed

McMullen, David P; Hotson, Guy; Katyal, Kapil D; Wester, Brock A; Fifer, Matthew S; McGee, Timothy G; Harris, Andrew; Johannes, Matthew S; Vogelstein, R Jacob; Ravitz, Alan D; Anderson, William S; Thakor, Nitish V; Crone, Nathan E

2014-07-01

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

PubMed Central

Blankertz, Benjamin; Acqualagna, Laura; Dähne, Sven; Haufe, Stefan; Schultze-Kraft, Matthias; Sturm, Irene; Ušćumlic, Marija; Wenzel, Markus A.; Curio, Gabriel; Müller, Klaus-Robert

2016-01-01

The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world. PMID:27917107

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control.

PubMed

Blankertz, Benjamin; Acqualagna, Laura; Dähne, Sven; Haufe, Stefan; Schultze-Kraft, Matthias; Sturm, Irene; Ušćumlic, Marija; Wenzel, Markus A; Curio, Gabriel; Müller, Klaus-Robert

2016-01-01

The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.

Massively Parallel Signal Processing using the Graphics Processing Unit for Real-Time Brain-Computer Interface Feature Extraction.

PubMed

Wilson, J Adam; Williams, Justin C

2009-01-01

The clock speeds of modern computer processors have nearly plateaued in the past 5 years. Consequently, neural prosthetic systems that rely on processing large quantities of data in a short period of time face a bottleneck, in that it may not be possible to process all of the data recorded from an electrode array with high channel counts and bandwidth, such as electrocorticographic grids or other implantable systems. Therefore, in this study a method of using the processing capabilities of a graphics card [graphics processing unit (GPU)] was developed for real-time neural signal processing of a brain-computer interface (BCI). The NVIDIA CUDA system was used to offload processing to the GPU, which is capable of running many operations in parallel, potentially greatly increasing the speed of existing algorithms. The BCI system records many channels of data, which are processed and translated into a control signal, such as the movement of a computer cursor. This signal processing chain involves computing a matrix-matrix multiplication (i.e., a spatial filter), followed by calculating the power spectral density on every channel using an auto-regressive method, and finally classifying appropriate features for control. In this study, the first two computationally intensive steps were implemented on the GPU, and the speed was compared to both the current implementation and a central processing unit-based implementation that uses multi-threading. Significant performance gains were obtained with GPU processing: the current implementation processed 1000 channels of 250 ms in 933 ms, while the new GPU method took only 27 ms, an improvement of nearly 35 times.

Encoder-Decoder Optimization for Brain-Computer Interfaces

PubMed Central

Merel, Josh; Pianto, Donald M.; Cunningham, John P.; Paninski, Liam

2015-01-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages. PMID:26029919

Encoder-decoder optimization for brain-computer interfaces.

PubMed

Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam

2015-06-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

Neuroprosthetic Decoder Training as Imitation Learning.

PubMed

Merel, Josh; Carlson, David; Paninski, Liam; Cunningham, John P

2016-05-01

Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user's intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector.

A brain-computer interface to support functional recovery.

PubMed

Kjaer, Troels W; Sørensen, Helge B

2013-01-01

Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating communication in the rather few patients with locked-in syndrome, much interest is now devoted to the therapeutic use of BCI in rehabilitation. For this latter group of patients, the device is not intended to be a lifelong assistive companion but rather a 'teacher' during the rehabilitation period. Copyright © 2013 S. Karger AG, Basel.

Hermetic electronic packaging of an implantable brain-machine-interface with transcutaneous optical data communication.

PubMed

Schuettler, Martin; Kohler, Fabian; Ordonez, Juan S; Stieglitz, Thomas

2012-01-01

Future brain-computer-interfaces (BCIs) for severely impaired patients are implanted to electrically contact the brain tissue. Avoiding percutaneous cables requires amplifier and telemetry electronics to be implanted too. We developed a hermetic package that protects the electronic circuitry of a BCI from body moisture while permitting infrared communication through the package wall made from alumina ceramic. The ceramic package is casted in medical grade silicone adhesive, for which we identified MED2-4013 as a promising candidate.

Towards Effective Non-Invasive Brain-Computer Interfaces Dedicated to Gait Rehabilitation Systems

PubMed Central

Castermans, Thierry; Duvinage, Matthieu; Cheron, Guy; Dutoit, Thierry

2014-01-01

In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI) applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation. PMID:24961699

Brain-computer interface controlled functional electrical stimulation device for foot drop due to stroke.

PubMed

Do, An H; Wang, Po T; King, Christine E; Schombs, Andrew; Cramer, Steven C; Nenadic, Zoran

2012-01-01

Gait impairment due to foot drop is a common outcome of stroke, and current physiotherapy provides only limited restoration of gait function. Gait function can also be aided by orthoses, but these devices may be cumbersome and their benefits disappear upon removal. Hence, new neuro-rehabilitative therapies are being sought to generate permanent improvements in motor function beyond those of conventional physiotherapies through positive neural plasticity processes. Here, the authors describe an electroencephalogram (EEG) based brain-computer interface (BCI) controlled functional electrical stimulation (FES) system that enabled a stroke subject with foot drop to re-establish foot dorsiflexion. To this end, a prediction model was generated from EEG data collected as the subject alternated between periods of idling and attempted foot dorsiflexion. This prediction model was then used to classify online EEG data into either "idling" or "dorsiflexion" states, and this information was subsequently used to control an FES device to elicit effective foot dorsiflexion. The performance of the system was assessed in online sessions, where the subject was prompted by a computer to alternate between periods of idling and dorsiflexion. The subject demonstrated purposeful operation of the BCI-FES system, with an average cross-correlation between instructional cues and BCI-FES response of 0.60 over 3 sessions. In addition, analysis of the prediction model indicated that non-classical brain areas were activated in the process, suggesting post-stroke cortical re-organization. In the future, these systems may be explored as a potential therapeutic tool that can help promote positive plasticity and neural repair in chronic stroke patients.

Designing Guiding Systems for Brain-Computer Interfaces

PubMed Central

Kosmyna, Nataliya; Lécuyer, Anatole

2017-01-01

Brain–Computer Interface (BCI) community has focused the majority of its research efforts on signal processing and machine learning, mostly neglecting the human in the loop. Guiding users on how to use a BCI is crucial in order to teach them to produce stable brain patterns. In this work, we explore the instructions and feedback for BCIs in order to provide a systematic taxonomy to describe the BCI guiding systems. The purpose of our work is to give necessary clues to the researchers and designers in Human–Computer Interaction (HCI) in making the fusion between BCIs and HCI more fruitful but also to better understand the possibilities BCIs can provide to them. PMID:28824400

Brain-computer interface analysis of a dynamic visuo-motor task.

PubMed

Logar, Vito; Belič, Aleš

2011-01-01

The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain-computer interface. For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filtering, double phase demodulation and double principal component analyses (PCA), each with a separate set of parameters. For the movement-prediction model a fuzzy inference system was used. The results have shown that the EEG signals measured during the dVM tasks carry enough information about the subjects' wrist movements for them to be successfully decoded using the presented methodology. Reasonably high values of the correlation coefficients suggest that the validation of the proposed approach is satisfactory. Moreover, since the causality of the rhythm filtering and the PCA transformation has been achieved, we have shown that these methods can also be used in a real-time, brain-computer interface. The study revealed that using non-causal, optimized methods yields better prediction results in comparison with the causal, non-optimized methodology; however, taking into account that the causality of these methods allows real-time processing, the minor decrease in prediction quality is acceptable. The study suggests that the methodology that was proposed in our previous studies is also valid for identifying the EEG-coded content during dVM tasks, albeit with various modifications, which allow better prediction results and real-time data processing. The results have shown that wrist movements can be predicted in simulated or real time; however, the results of the non-causal, optimized methodology (simulated) are slightly better. Nevertheless, the study has revealed that these methods should be suitable for use in the development of a non-invasive, brain-computer interface. Copyright © 2010 Elsevier B.V. All rights reserved.

Key considerations in designing a speech brain-computer interface.

PubMed

Bocquelet, Florent; Hueber, Thomas; Girin, Laurent; Chabardès, Stéphan; Yvert, Blaise

2016-11-01

Restoring communication in case of aphasia is a key challenge for neurotechnologies. To this end, brain-computer strategies can be envisioned to allow artificial speech synthesis from the continuous decoding of neural signals underlying speech imagination. Such speech brain-computer interfaces do not exist yet and their design should consider three key choices that need to be made: the choice of appropriate brain regions to record neural activity from, the choice of an appropriate recording technique, and the choice of a neural decoding scheme in association with an appropriate speech synthesis method. These key considerations are discussed here in light of (1) the current understanding of the functional neuroanatomy of cortical areas underlying overt and covert speech production, (2) the available literature making use of a variety of brain recording techniques to better characterize and address the challenge of decoding cortical speech signals, and (3) the different speech synthesis approaches that can be considered depending on the level of speech representation (phonetic, acoustic or articulatory) envisioned to be decoded at the core of a speech BCI paradigm. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Concurrent EEG And NIRS Tomographic Imaging Based on Wearable Electro-Optodes

DTIC Science & Technology

2014-04-13

Interfaces   ( BCIs ),   and   other   systems   in   the   same   computational   framework.   Figure   11   below   shows...Improving  Brain-­‐Computer   Interfaces  Using   Independent  Component   Analysis,  In:  Towards  Future   BCIs ,  2012

A development architecture for serious games using BCI (brain computer interface) sensors.

PubMed

Sung, Yunsick; Cho, Kyungeun; Um, Kyhyun

2012-11-12

Games that use brainwaves via brain-computer interface (BCI) devices, to improve brain functions are known as BCI serious games. Due to the difficulty of developing BCI serious games, various BCI engines and authoring tools are required, and these reduce the development time and cost. However, it is desirable to reduce the amount of technical knowledge of brain functions and BCI devices needed by game developers. Moreover, a systematic BCI serious game development process is required. In this paper, we present a methodology for the development of BCI serious games. We describe an architecture, authoring tools, and development process of the proposed methodology, and apply it to a game development approach for patients with mild cognitive impairment as an example. This application demonstrates that BCI serious games can be developed on the basis of expert-verified theories.

The Unlock Project: a Python-based framework for practical brain-computer interface communication "app" development.

PubMed

Brumberg, Jonathan S; Lorenz, Sean D; Galbraith, Byron V; Guenther, Frank H

2012-01-01

In this paper we present a framework for reducing the development time needed for creating applications for use in non-invasive brain-computer interfaces (BCI). Our framework is primarily focused on facilitating rapid software "app" development akin to current efforts in consumer portable computing (e.g. smart phones and tablets). This is accomplished by handling intermodule communication without direct user or developer implementation, instead relying on a core subsystem for communication of standard, internal data formats. We also provide a library of hardware interfaces for common mobile EEG platforms for immediate use in BCI applications. A use-case example is described in which a user with amyotrophic lateral sclerosis participated in an electroencephalography-based BCI protocol developed using the proposed framework. We show that our software environment is capable of running in real-time with updates occurring 50-60 times per second with limited computational overhead (5 ms system lag) while providing accurate data acquisition and signal analysis.

Using real-time fMRI brain-computer interfacing to treat eating disorders.

PubMed

Sokunbi, Moses O

2018-05-15

Real-time functional magnetic resonance imaging based brain-computer interfacing (fMRI neurofeedback) has shown encouraging outcomes in the treatment of psychiatric and behavioural disorders. However, its use in the treatment of eating disorders is very limited. Here, we give a brief overview of how to design and implement fMRI neurofeedback intervention for the treatment of eating disorders, considering the basic and essential components. We also attempt to develop potential adaptations of fMRI neurofeedback intervention for the treatment of anorexia nervosa, bulimia nervosa and binge eating disorder. Copyright © 2018 Elsevier B.V. All rights reserved.

Hybrid EEG-EOG brain-computer interface system for practical machine control.

PubMed

Punsawad, Yunyong; Wongsawat, Yodchanan; Parnichkun, Manukid

2010-01-01

Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.

Brain-computer interface on the basis of EEG system Encephalan

NASA Astrophysics Data System (ADS)

Maksimenko, Vladimir; Badarin, Artem; Nedaivozov, Vladimir; Kirsanov, Daniil; Hramov, Alexander

2018-04-01

We have proposed brain-computer interface (BCI) for the estimation of the brain response on the presented visual tasks. Proposed BCI is based on the EEG recorder Encephalan-EEGR-19/26 (Medicom MTD, Russia) supplemented by a special home-made developed acquisition software. BCI is tested during experimental session while subject is perceiving the bistable visual stimuli and classifying them according to the interpretation. We have subjected the participant to the different external conditions and observed the significant decrease in the response, associated with the perceiving the bistable visual stimuli, during the presence of distraction. Based on the obtained results we have proposed possibility to use of BCI for estimation of the human alertness during solving the tasks required substantial visual attention.

Write, read and answer emails with a dry 'n' wireless brain-computer interface system.

PubMed

Pinegger, Andreas; Deckert, Lisa; Halder, Sebastian; Barry, Norbert; Faller, Josef; Käthner, Ivo; Hintermüller, Christoph; Wriessnegger, Selina C; Kübler, Andrea; Müller-Putz, Gernot R

2014-01-01

Brain-computer interface (BCI) users can control very complex applications such as multimedia players or even web browsers. Therefore, different biosignal acquisition systems are available to noninvasively measure the electrical activity of the brain, the electroencephalogram (EEG). To make BCIs more practical, hardware and software are nowadays designed more user centered and user friendly. In this paper we evaluated one of the latest innovations in the area of BCI: A wireless EEG amplifier with dry electrode technology combined with a web browser which enables BCI users to use standard webmail. With this system ten volunteers performed a daily life task: Write, read and answer an email. Experimental results of this study demonstrate the power of the introduced BCI system.

Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

PubMed

Hong, Keum-Shik; Khan, Muhammad Jawad

2017-01-01

In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system.

PubMed

Li, Yuanqing; Pan, Jiahui; He, Yanbin; Wang, Fei; Laureys, Steven; Xie, Qiuyou; Yu, Ronghao

2015-12-15

For patients with disorders of consciousness such as coma, a vegetative state or a minimally conscious state, one challenge is to detect and assess the residual cognitive functions in their brains. Number processing and mental calculation are important brain functions but are difficult to detect in patients with disorders of consciousness using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised due to the patients' motor impairments and inability to provide sufficient motor responses for number- and calculation-based communication. In this study, we presented a hybrid brain-computer interface that combines P300 and steady state visual evoked potentials to detect number processing and mental calculation in Han Chinese patients with disorders of consciousness. Eleven patients with disorders of consciousness who were in a vegetative state (n = 6) or in a minimally conscious state (n = 3) or who emerged from a minimally conscious state (n = 2) participated in the brain-computer interface-based experiment. During the experiment, the patients with disorders of consciousness were instructed to perform three tasks, i.e., number recognition, number comparison, and mental calculation, including addition and subtraction. In each experimental trial, an arithmetic problem was first presented. Next, two number buttons, only one of which was the correct answer to the problem, flickered at different frequencies to evoke steady state visual evoked potentials, while the frames of the two buttons flashed in a random order to evoke P300 potentials. The patients needed to focus on the target number button (the correct answer). Finally, the brain-computer interface system detected P300 and steady state visual evoked potentials to determine the button to which the patients attended, further presenting the results as feedback. Two of the six patients who were in a vegetative state, one of the three patients who were in a minimally conscious state, and the two patients that emerged from a minimally conscious state achieved accuracies significantly greater than the chance level. Furthermore, P300 potentials and steady state visual evoked potentials were observed in the electroencephalography signals from the five patients. Number processing and arithmetic abilities as well as command following were demonstrated in the five patients. Furthermore, our results suggested that through brain-computer interface systems, many cognitive experiments may be conducted in patients with disorders of consciousness, although they cannot provide sufficient behavioral responses.

Visual analytics of brain networks.

PubMed

Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

2012-05-15

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. Copyright © 2012 Elsevier Inc. All rights reserved.

Development of the Computer Interface Literacy Measure.

ERIC Educational Resources Information Center

Turner, G. Marc; Sweany, Noelle Wall; Husman, Jenefer

2000-01-01

Discussion of computer literacy and the rapidly changing face of technology focuses on a study that redefined computer literacy to include competencies for using graphical user interfaces for operating systems, hypermedia applications, and the Internet. Describes the development and testing of the Computer Interface Literacy Measure with…

Brain-computer interface technology: a review of the first international meeting.

PubMed

Wolpaw, J R; Birbaumer, N; Heetderks, W J; McFarland, D J; Peckham, P H; Schalk, G; Donchin, E; Quatrano, L A; Robinson, C J; Vaughan, T M

2000-06-01

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.

Neuroprosthetic Decoder Training as Imitation Learning

PubMed Central

Merel, Josh; Paninski, Liam; Cunningham, John P.

2016-01-01

Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user’s intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user’s intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector. PMID:27191387

Parallel multiscale simulations of a brain aneurysm

PubMed Central

Grinberg, Leopold; Fedosov, Dmitry A.; Karniadakis, George Em

2012-01-01

Cardiovascular pathologies, such as a brain aneurysm, are affected by the global blood circulation as well as by the local microrheology. Hence, developing computational models for such cases requires the coupling of disparate spatial and temporal scales often governed by diverse mathematical descriptions, e.g., by partial differential equations (continuum) and ordinary differential equations for discrete particles (atomistic). However, interfacing atomistic-based with continuum-based domain discretizations is a challenging problem that requires both mathematical and computational advances. We present here a hybrid methodology that enabled us to perform the first multi-scale simulations of platelet depositions on the wall of a brain aneurysm. The large scale flow features in the intracranial network are accurately resolved by using the high-order spectral element Navier-Stokes solver εκ αr. The blood rheology inside the aneurysm is modeled using a coarse-grained stochastic molecular dynamics approach (the dissipative particle dynamics method) implemented in the parallel code LAMMPS. The continuum and atomistic domains overlap with interface conditions provided by effective forces computed adaptively to ensure continuity of states across the interface boundary. A two-way interaction is allowed with the time-evolving boundary of the (deposited) platelet clusters tracked by an immersed boundary method. The corresponding heterogeneous solvers ( εκ αr and LAMMPS) are linked together by a computational multilevel message passing interface that facilitates modularity and high parallel efficiency. Results of multiscale simulations of clot formation inside the aneurysm in a patient-specific arterial tree are presented. We also discuss the computational challenges involved and present scalability results of our coupled solver on up to 300K computer processors. Validation of such coupled atomistic-continuum models is a main open issue that has to be addressed in future work. PMID:23734066

Parallel multiscale simulations of a brain aneurysm.

PubMed

Grinberg, Leopold; Fedosov, Dmitry A; Karniadakis, George Em

2013-07-01

Cardiovascular pathologies, such as a brain aneurysm, are affected by the global blood circulation as well as by the local microrheology. Hence, developing computational models for such cases requires the coupling of disparate spatial and temporal scales often governed by diverse mathematical descriptions, e.g., by partial differential equations (continuum) and ordinary differential equations for discrete particles (atomistic). However, interfacing atomistic-based with continuum-based domain discretizations is a challenging problem that requires both mathematical and computational advances. We present here a hybrid methodology that enabled us to perform the first multi-scale simulations of platelet depositions on the wall of a brain aneurysm. The large scale flow features in the intracranial network are accurately resolved by using the high-order spectral element Navier-Stokes solver εκ αr . The blood rheology inside the aneurysm is modeled using a coarse-grained stochastic molecular dynamics approach (the dissipative particle dynamics method) implemented in the parallel code LAMMPS. The continuum and atomistic domains overlap with interface conditions provided by effective forces computed adaptively to ensure continuity of states across the interface boundary. A two-way interaction is allowed with the time-evolving boundary of the (deposited) platelet clusters tracked by an immersed boundary method. The corresponding heterogeneous solvers ( εκ αr and LAMMPS) are linked together by a computational multilevel message passing interface that facilitates modularity and high parallel efficiency. Results of multiscale simulations of clot formation inside the aneurysm in a patient-specific arterial tree are presented. We also discuss the computational challenges involved and present scalability results of our coupled solver on up to 300K computer processors. Validation of such coupled atomistic-continuum models is a main open issue that has to be addressed in future work.

Parallel multiscale simulations of a brain aneurysm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grinberg, Leopold; Fedosov, Dmitry A.; Karniadakis, George Em, E-mail: george_karniadakis@brown.edu

2013-07-01

Cardiovascular pathologies, such as a brain aneurysm, are affected by the global blood circulation as well as by the local microrheology. Hence, developing computational models for such cases requires the coupling of disparate spatial and temporal scales often governed by diverse mathematical descriptions, e.g., by partial differential equations (continuum) and ordinary differential equations for discrete particles (atomistic). However, interfacing atomistic-based with continuum-based domain discretizations is a challenging problem that requires both mathematical and computational advances. We present here a hybrid methodology that enabled us to perform the first multiscale simulations of platelet depositions on the wall of a brain aneurysm.more » The large scale flow features in the intracranial network are accurately resolved by using the high-order spectral element Navier–Stokes solver NεκTαr. The blood rheology inside the aneurysm is modeled using a coarse-grained stochastic molecular dynamics approach (the dissipative particle dynamics method) implemented in the parallel code LAMMPS. The continuum and atomistic domains overlap with interface conditions provided by effective forces computed adaptively to ensure continuity of states across the interface boundary. A two-way interaction is allowed with the time-evolving boundary of the (deposited) platelet clusters tracked by an immersed boundary method. The corresponding heterogeneous solvers (NεκTαr and LAMMPS) are linked together by a computational multilevel message passing interface that facilitates modularity and high parallel efficiency. Results of multiscale simulations of clot formation inside the aneurysm in a patient-specific arterial tree are presented. We also discuss the computational challenges involved and present scalability results of our coupled solver on up to 300 K computer processors. Validation of such coupled atomistic-continuum models is a main open issue that has to be addressed in future work.« less

Orientation-modulated attention effect on visual evoked potential: Application for PIN system using brain-computer interface.

PubMed

Wilaiprasitporn, Theerawit; Yagi, Tohru

2015-01-01

This research demonstrates the orientation-modulated attention effect on visual evoked potential. We combined this finding with our previous findings about the motion-modulated attention effect and used the result to develop novel visual stimuli for a personal identification number (PIN) application based on a brain-computer interface (BCI) framework. An electroencephalography amplifier with a single electrode channel was sufficient for our application. A computationally inexpensive algorithm and small datasets were used in processing. Seven healthy volunteers participated in experiments to measure offline performance. Mean accuracy was 83.3% at 13.9 bits/min. Encouraged by these results, we plan to continue developing the BCI-based personal identification application toward real-time systems.

Distance-constrained orthogonal Latin squares for brain-computer interface.

PubMed

Luo, Gang; Min, Wanli

2012-02-01

The P300 brain-computer interface (BCI) using electroencephalogram (EEG) signals can allow amyotrophic lateral sclerosis (ALS) patients to instruct computers to perform tasks. To strengthen the P300 response and increase classification accuracy, we proposed an experimental design where characters are intensified according to orthogonal Latin square pairs. These orthogonal Latin square pairs satisfy certain distance constraint so that neighboring characters are not intensified simultaneously. However, it is unknown whether such distance-constrained, orthogonal Latin square pairs actually exist. In this paper, we show that for every matrix size commonly used in P300 BCI, thousands to millions of such distance-constrained, orthogonal Latin square pairs can be systematically and efficiently constructed and are sufficient for the purpose of being used in P300 BCI.

Investigation of different classifiers and channel configurations of a mobile P300-based brain-computer interface.

PubMed

Ludwig, Simone A; Kong, Jun

2017-12-01

Innovative methods and new technologies have significantly improved the quality of our daily life. However, disabled people, for example those that cannot use their arms and legs anymore, often cannot benefit from these developments, since they cannot use their hands to interact with traditional interaction methods (such as mouse or keyboard) to communicate with a computer system. A brain-computer interface (BCI) system allows such a disabled person to control an external device via brain waves. Past research mostly dealt with static interfaces, which limit users to a stationary location. However, since we are living in a world that is highly mobile, this paper evaluates a speller interface on a mobile phone used in a moving condition. The spelling experiments were conducted with 14 able-bodied subjects using visual flashes as the stimulus to spell 47 alphanumeric characters (38 letters and 9 numbers). This data was then used for the classification experiments. In par- ticular, two research directions are pursued. The first investigates the impact of different classification algorithms, and the second direction looks at the channel configuration, i.e., which channels are most beneficial in terms of achieving the highest classification accuracy. The evaluation results indicate that the Bayesian Linear Discriminant Analysis algorithm achieves the best accuracy. Also, the findings of the investigation on the channel configuration, which can potentially reduce the amount of data processing on a mobile device with limited computing capacity, is especially useful in mobile BCIs.

Electrophysiological CNS-processes related to associative learning in humans.

PubMed

Christoffersen, Gert R J; Schachtman, Todd R

2016-01-01

The neurophysiology of human associative memory has been studied with electroencephalographic techniques since the 1930s. This research has revealed that different types of electrophysiological processes in the human brain can be modified by conditioning: sensory evoked potentials, sensory induced gamma-band activity, periods of frequency-specific waves (alpha and beta waves, the sensorimotor rhythm and the mu-rhythm) and slow cortical potentials. Conditioning of these processes has been studied in experiments that either use operant conditioning or repeated contingent pairings of conditioned and unconditioned stimuli (classical conditioning). In operant conditioning, the appearance of a specific brain process is paired with an external stimulus (neurofeedback) and the feedback enables subjects to obtain varying degrees of control of the CNS-process. Such acquired self-regulation of brain activity has found practical uses for instance in the amelioration of epileptic seizures, Autism Spectrum Disorders (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). It has also provided communicative means of assistance for tetraplegic patients through the use of brain computer interfaces. Both extra and intracortically recorded signals have been coupled with contingent external feedback. It is the aim for this review to summarize essential results on all types of electromagnetic brain processes that have been modified by classical or operant conditioning. The results are organized according to type of conditioned EEG-process, type of conditioning, and sensory modalities of the conditioning stimuli. Copyright © 2015 Elsevier B.V. All rights reserved.

[Arm Motor Function Recovery during Rehabilitation with the Use of Hand Exoskeleton Controlled by Brain-Computer Interface: a Patient with Severe Brain Damage].

PubMed

Biryukova, E V; Pavlova, O G; Kurganskaya, M E; Bobrov, P D; Turbina, L G; Frolov, A A; Davydov, V I; Sil'tchenko, A V; Mokienko, O A

2016-01-01

We studied the dynamics of motor function recovery in a patient with severe brain damage in the course of neurorehabilitation using hand exoskeleton controlled by brain-computer interface. For estimating the motor function of paretic arm, we used the biomechanical analysis of movements registered during the course of rehabilitation. After 15 weekly sessions of hand exoskeleton control, the following results were obtained: a) the velocity profile of goal-directed movements of paretic hand became bell-shaped, b) the patient began to extend and abduct the hand which was flexed and adducted in the beginning of rehabilitation, and c) the patient began to supinate the forearm which was pronated in the beginning of rehabilitation. The first result is an evidence of the general improvement of the quality of motor control, while the second and third results prove that the spasticity of paretic arm has decreased.

fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

PubMed Central

Sitaram, Ranganatha; Caria, Andrea; Veit, Ralf; Gaber, Tilman; Rota, Giuseppina; Kuebler, Andrea; Birbaumer, Niels

2007-01-01

Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI) allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression) is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment. PMID:18274615

Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.

PubMed

Friedenberg, David A; Bouton, Chad E; Annetta, Nicholas V; Skomrock, Nicholas; Mingming Zhang; Schwemmer, Michael; Bockbrader, Marcia A; Mysiw, W Jerry; Rezai, Ali R; Bresler, Herbert S; Sharma, Gaurav

2016-08-01

Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.

Addition of visual noise boosts evoked potential-based brain-computer interface.

PubMed

Xie, Jun; Xu, Guanghua; Wang, Jing; Zhang, Sicong; Zhang, Feng; Li, Yeping; Han, Chengcheng; Li, Lili

2014-05-14

Although noise has a proven beneficial role in brain functions, there have not been any attempts on the dedication of stochastic resonance effect in neural engineering applications, especially in researches of brain-computer interfaces (BCIs). In our study, a steady-state motion visual evoked potential (SSMVEP)-based BCI with periodic visual stimulation plus moderate spatiotemporal noise can achieve better offline and online performance due to enhancement of periodic components in brain responses, which was accompanied by suppression of high harmonics. Offline results behaved with a bell-shaped resonance-like functionality and 7-36% online performance improvements can be achieved when identical visual noise was adopted for different stimulation frequencies. Using neural encoding modeling, these phenomena can be explained as noise-induced input-output synchronization in human sensory systems which commonly possess a low-pass property. Our work demonstrated that noise could boost BCIs in addressing human needs.

The Two-Brains Hypothesis: Towards a guide for brain-brain and brain-machine interfaces.

PubMed

Goodman, G; Poznanski, R R; Cacha, L; Bercovich, D

2015-09-01

Great advances have been made in signaling information on brain activity in individuals, or passing between an individual and a computer or robot. These include recording of natural activity using implants under the scalp or by external means or the reverse feeding of such data into the brain. In one recent example, noninvasive transcranial magnetic stimulation (TMS) allowed feeding of digitalized information into the central nervous system (CNS). Thus, noninvasive electroencephalography (EEG) recordings of motor signals at the scalp, representing specific motor intention of hand moving in individual humans, were fed as repetitive transcranial magnetic stimulation (rTMS) at a maximum intensity of 2.0[Formula: see text]T through a circular magnetic coil placed flush on each of the heads of subjects present at a different location. The TMS was said to induce an electric current influencing axons of the motor cortex causing the intended hand movement: the first example of the transfer of motor intention and its expression, between the brains of two remote humans. However, to date the mechanisms involved, not least that relating to the participation of magnetic induction, remain unclear. In general, in animal biology, magnetic fields are usually the poor relation of neuronal current: generally "unseen" and if apparent, disregarded or just given a nod. Niels Bohr searched for a biological parallel to complementary phenomena of physics. Pertinently, the two-brains hypothesis (TBH) proposed recently that advanced animals, especially man, have two brains i.e., the animal CNS evolved as two fundamentally different though interdependent, complementary organs: one electro-ionic (tangible, known and accessible), and the other, electromagnetic (intangible and difficult to access) - a stable, structured and functional 3D compendium of variously induced interacting electro-magnetic (EM) fields. Research on the CNS in health and disease progresses including that on brain-brain, brain-computer and brain-robot engineering. As they grow even closer, these disciplines involve their own unique complexities, including direction by the laws of inductive physics. So the novel TBH hypothesis has wide fundamental implications, including those related to TMS. These require rethinking and renewed research engaging the fully complementary equivalence of mutual magnetic and electric field induction in the CNS and, within this context, a new mathematics of the brain to decipher higher cognitive operations not possible with current brain-brain and brain-machine interfaces. Bohr may now rest.

Region based Brain Computer Interface for a home control application.

PubMed

Akman Aydin, Eda; Bay, Omer Faruk; Guler, Inan

2015-08-01

Environment control is one of the important challenges for disabled people who suffer from neuromuscular diseases. Brain Computer Interface (BCI) provides a communication channel between the human brain and the environment without requiring any muscular activation. The most important expectation for a home control application is high accuracy and reliable control. Region-based paradigm is a stimulus paradigm based on oddball principle and requires selection of a target at two levels. This paper presents an application of region based paradigm for a smart home control application for people with neuromuscular diseases. In this study, a region based stimulus interface containing 49 commands was designed. Five non-disabled subjects were attended to the experiments. Offline analysis results of the experiments yielded 95% accuracy for five flashes. This result showed that region based paradigm can be used to select commands of a smart home control application with high accuracy in the low number of repetitions successfully. Furthermore, a statistically significant difference was not observed between the level accuracies.

Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study Using a Brain-Computer Interface.

PubMed

Fernández-Soto, Alicia; Martínez-Rodrigo, Arturo; Moncho-Bogani, José; Latorre, José Miguel; Fernández-Caballero, Antonio

2018-06-01

For the sake of establishing the neural correlates of phrase quadrature perception in harmonic rhythm, a musical experiment has been designed to induce music-evoked stimuli related to one important aspect of harmonic rhythm, namely the phrase quadrature. Brain activity is translated to action through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. The results of processing the acquired signals are in line with previous studies that use different musical parameters to induce emotions. Indeed, our experiment shows statistical differences in theta and alpha bands between the fulfillment and break of phrase quadrature, an important cue of harmonic rhythm, in two classical sonatas.

Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors

PubMed Central

2012-01-01

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering. PMID:22284235

Interactions between pre-processing and classification methods for event-related-potential classification: best-practice guidelines for brain-computer interfacing.

PubMed

Farquhar, J; Hill, N J

2013-04-01

Detecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g., visual or tactile), ERP component (e.g., P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a "best-practice" method for ERP detection problems.

Brain-Computer Interfaces: A Neuroscience Paradigm of Social Interaction? A Matter of Perspective

PubMed Central

Mattout, Jérémie

2012-01-01

A number of recent studies have put human subjects in true social interactions, with the aim of better identifying the psychophysiological processes underlying social cognition. Interestingly, this emerging Neuroscience of Social Interactions (NSI) field brings up challenges which resemble important ones in the field of Brain-Computer Interfaces (BCI). Importantly, these challenges go beyond common objectives such as the eventual use of BCI and NSI protocols in the clinical domain or common interests pertaining to the use of online neurophysiological techniques and algorithms. Common fundamental challenges are now apparent and one can argue that a crucial one is to develop computational models of brain processes relevant to human interactions with an adaptive agent, whether human or artificial. Coupled with neuroimaging data, such models have proved promising in revealing the neural basis and mental processes behind social interactions. Similar models could help BCI to move from well-performing but offline static machines to reliable online adaptive agents. This emphasizes a social perspective to BCI, which is not limited to a computational challenge but extends to all questions that arise when studying the brain in interaction with its environment. PMID:22675291

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

PubMed Central

Khan, M. Jawad; Hong, Melissa Jiyoun; Hong, Keum-Shik

2014-01-01

The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology. PMID:24808844

The brain-computer interface cycle.

PubMed

van Gerven, Marcel; Farquhar, Jason; Schaefer, Rebecca; Vlek, Rutger; Geuze, Jeroen; Nijholt, Anton; Ramsey, Nick; Haselager, Pim; Vuurpijl, Louis; Gielen, Stan; Desain, Peter

2009-08-01

Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.

A small, portable, battery-powered brain-computer interface system for motor rehabilitation.

PubMed

McCrimmon, Colin M; Ming Wang; Silva Lopes, Lucas; Wang, Po T; Karimi-Bidhendi, Alireza; Liu, Charles Y; Heydari, Payam; Nenadic, Zoran; Do, An H

2016-08-01

Motor rehabilitation using brain-computer interface (BCI) systems may facilitate functional recovery in individuals after stroke or spinal cord injury. Nevertheless, these systems are typically ill-suited for widespread adoption due to their size, cost, and complexity. In this paper, a small, portable, and extremely cost-efficient (<;$200) BCI system has been developed using a custom electroencephalographic (EEG) amplifier array, and a commercial microcontroller and touchscreen. The system's performance was tested using a movement-related BCI task in 3 able-bodied subjects with minimal previous BCI experience. Specifically, subjects were instructed to alternate between relaxing and dorsiflexing their right foot, while their EEG was acquired and analyzed in real-time by the BCI system to decode their underlying movement state. The EEG signals acquired by the custom amplifier array were similar to those acquired by a commercial amplifier (maximum correlation coefficient ρ=0.85). During real-time BCI operation, the average correlation between instructional cues and decoded BCI states across all subjects (ρ=0.70) was comparable to that of full-size BCI systems. Small, portable, and inexpensive BCI systems such as the one reported here may promote a widespread adoption of BCI-based movement rehabilitation devices in stroke and spinal cord injury populations.

Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation

PubMed Central

Liu, Ju-Chi; Chou, Hung-Chyun; Chen, Chien-Hsiu; Lin, Yi-Tseng

2016-01-01

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints. PMID:27579033

Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation.

PubMed

Liu, Ju-Chi; Chou, Hung-Chyun; Chen, Chien-Hsiu; Lin, Yi-Tseng; Kuo, Chung-Hsien

2016-01-01

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

A Gaze Independent Brain-Computer Interface Based on Visual Stimulation through Closed Eyelids

NASA Astrophysics Data System (ADS)

Hwang, Han-Jeong; Ferreria, Valeria Y.; Ulrich, Daniel; Kilic, Tayfun; Chatziliadis, Xenofon; Blankertz, Benjamin; Treder, Matthias

2015-10-01

A classical brain-computer interface (BCI) based on visual event-related potentials (ERPs) is of limited application value for paralyzed patients with severe oculomotor impairments. In this study, we introduce a novel gaze independent BCI paradigm that can be potentially used for such end-users because visual stimuli are administered on closed eyelids. The paradigm involved verbally presented questions with 3 possible answers. Online BCI experiments were conducted with twelve healthy subjects, where they selected one option by attending to one of three different visual stimuli. It was confirmed that typical cognitive ERPs can be evidently modulated by the attention of a target stimulus in eyes-closed and gaze independent condition, and further classified with high accuracy during online operation (74.58% ± 17.85 s.d.; chance level 33.33%), demonstrating the effectiveness of the proposed novel visual ERP paradigm. Also, stimulus-specific eye movements observed during stimulation were verified as reflex responses to light stimuli, and they did not contribute to classification. To the best of our knowledge, this study is the first to show the possibility of using a gaze independent visual ERP paradigm in an eyes-closed condition, thereby providing another communication option for severely locked-in patients suffering from complex ocular dysfunctions.

Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic

PubMed Central

Shin, Jaeyoung; Müller, Klaus-R; Hwang, Han-Jeong

2016-01-01

We propose a near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) that can be operated in eyes-closed (EC) state. To evaluate the feasibility of NIRS-based EC BCIs, we compared the performance of an eye-open (EO) BCI paradigm and an EC BCI paradigm with respect to hemodynamic response and classification accuracy. To this end, subjects performed either mental arithmetic or imagined vocalization of the English alphabet as a baseline task with very low cognitive loading. The performances of two linear classifiers were compared; resulting in an advantage of shrinkage linear discriminant analysis (LDA). The classification accuracy of EC paradigm (75.6 ± 7.3%) was observed to be lower than that of EO paradigm (77.0 ± 9.2%), which was statistically insignificant (p = 0.5698). Subjects reported they felt it more comfortable (p = 0.057) and easier (p < 0.05) to perform the EC BCI tasks. The different task difficulty may become a cause of the slightly lower classification accuracy of EC data. From the analysis results, we could confirm the feasibility of NIRS-based EC BCIs, which can be a BCI option that may ultimately be of use for patients who cannot keep their eyes open consistently. PMID:27824089

Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic.

PubMed

Shin, Jaeyoung; Müller, Klaus-R; Hwang, Han-Jeong

2016-11-08

We propose a near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) that can be operated in eyes-closed (EC) state. To evaluate the feasibility of NIRS-based EC BCIs, we compared the performance of an eye-open (EO) BCI paradigm and an EC BCI paradigm with respect to hemodynamic response and classification accuracy. To this end, subjects performed either mental arithmetic or imagined vocalization of the English alphabet as a baseline task with very low cognitive loading. The performances of two linear classifiers were compared; resulting in an advantage of shrinkage linear discriminant analysis (LDA). The classification accuracy of EC paradigm (75.6 ± 7.3%) was observed to be lower than that of EO paradigm (77.0 ± 9.2%), which was statistically insignificant (p = 0.5698). Subjects reported they felt it more comfortable (p = 0.057) and easier (p < 0.05) to perform the EC BCI tasks. The different task difficulty may become a cause of the slightly lower classification accuracy of EC data. From the analysis results, we could confirm the feasibility of NIRS-based EC BCIs, which can be a BCI option that may ultimately be of use for patients who cannot keep their eyes open consistently.

Brain-computer interface after nervous system injury.

PubMed

Burns, Alexis; Adeli, Hojjat; Buford, John A

2014-12-01

Brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson's disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders. © The Author(s) 2014.

Low-power hardware implementation of movement decoding for brain computer interface with reduced-resolution discrete cosine transform.

PubMed

Minho Won; Albalawi, Hassan; Xin Li; Thomas, Donald E

2014-01-01

This paper describes a low-power hardware implementation for movement decoding of brain computer interface. Our proposed hardware design is facilitated by two novel ideas: (i) an efficient feature extraction method based on reduced-resolution discrete cosine transform (DCT), and (ii) a new hardware architecture of dual look-up table to perform discrete cosine transform without explicit multiplication. The proposed hardware implementation has been validated for movement decoding of electrocorticography (ECoG) signal by using a Xilinx FPGA Zynq-7000 board. It achieves more than 56× energy reduction over a reference design using band-pass filters for feature extraction.

A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface.

PubMed

Jiao, Yong; Zhang, Yu; Wang, Yu; Wang, Bei; Jin, Jing; Wang, Xingyu

2018-05-01

Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.

Quantum neural network-based EEG filtering for a brain-computer interface.

PubMed

Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

2014-02-01

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

An Efficient ERP-Based Brain-Computer Interface Using Random Set Presentation and Face Familiarity

PubMed Central

Müller, Klaus-Robert; Lee, Seong-Whan

2014-01-01

Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our approach that combines a random set presentation paradigm with (non-) self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup. PMID:25384045

Feasibility of Equivalent Dipole Models for Electroencephalogram-Based Brain Computer Interfaces.

PubMed

Schimpf, Paul H

2017-09-15

This article examines the localization errors of equivalent dipolar sources inverted from the surface electroencephalogram in order to determine the feasibility of using their location as classification parameters for non-invasive brain computer interfaces. Inverse localization errors are examined for two head models: a model represented by four concentric spheres and a realistic model based on medical imagery. It is shown that the spherical model results in localization ambiguity such that a number of dipolar sources, with different azimuths and varying orientations, provide a near match to the electroencephalogram of the best equivalent source. No such ambiguity exists for the elevation of inverted sources, indicating that for spherical head models, only the elevation of inverted sources (and not the azimuth) can be expected to provide meaningful classification parameters for brain-computer interfaces. In a realistic head model, all three parameters of the inverted source location are found to be reliable, providing a more robust set of parameters. In both cases, the residual error hypersurfaces demonstrate local minima, indicating that a search for the best-matching sources should be global. Source localization error vs. signal-to-noise ratio is also demonstrated for both head models.

An efficient ERP-based brain-computer interface using random set presentation and face familiarity.

PubMed

Yeom, Seul-Ki; Fazli, Siamac; Müller, Klaus-Robert; Lee, Seong-Whan

2014-01-01

Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our approach that combines a random set presentation paradigm with (non-) self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup.

Design of an online EEG based neurofeedback game for enhancing attention and memory.

PubMed

Thomas, Kavitha P; Vinod, A P; Guan, Cuntai

2013-01-01

Brain-Computer Interface (BCI) is an alternative communication and control channel between brain and computer which finds applications in neuroprosthetics, brain wave controlled computer games etc. This paper proposes an Electroencephalogram (EEG) based neurofeedback computer game that allows the player to control the game with the help of attention based brain signals. The proposed game protocol requires the player to memorize a set of numbers in a matrix, and to correctly fill the matrix using his attention. The attention level of the player is quantified using sample entropy features of EEG. The statistically significant performance improvement of five healthy subjects after playing a number of game sessions demonstrates the effectiveness of the proposed game in enhancing their concentration and memory skills.

A Brain-Based Communication and Orientation System

DTIC Science & Technology

2014-10-06

Review of the BCI Competition IV, Frontiers in Neuroscience, ( 2012): 0. doi: 10.3389/fnins.2012.00055 Eric C. Leuthardt, Xiao-Mei Pei, Jonathan...hardware and software for brain– computer interfaces ( BCIs ), Journal of Neural Engineering, (04 2011): 1. doi: 10.1088/1741-2560/8/2/025001...Cincotti, G. Schalk, Peter Brunner. Current Trends in Brain–Computer Interface ( BCI ) Research and Development, Journal of Neural Engineering, (3 2011

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface

PubMed Central

Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

2014-01-01

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them. PMID:24567704

Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.

PubMed

Sakurai, Yoshio; Song, Kichan; Tachibana, Shota; Takahashi, Susumu

2014-01-01

In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.

Wavelet multiresolution complex network for decoding brain fatigued behavior from P300 signals

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Wang, Zi-Bo; Yang, Yu-Xuan; Li, Shan; Dang, Wei-Dong; Mao, Xiao-Qian

2018-09-01

Brain-computer interface (BCI) enables users to interact with the environment without relying on neural pathways and muscles. P300 based BCI systems have been extensively used to achieve human-machine interaction. However, the appearance of fatigue symptoms during operation process leads to the decline in classification accuracy of P300. Characterizing brain cognitive process underlying normal and fatigue conditions constitutes a problem of vital importance in the field of brain science. We in this paper propose a novel wavelet decomposition based complex network method to efficiently analyze the P300 signals recorded in the image stimulus test based on classical 'Oddball' paradigm. Initially, multichannel EEG signals are decomposed into wavelet coefficient series. Then we construct complex network by treating electrodes as nodes and determining the connections according to the 2-norm distances between wavelet coefficient series. The analysis of topological structure and statistical index indicates that the properties of brain network demonstrate significant distinctions between normal status and fatigue status. More specifically, the brain network reconfiguration in response to the cognitive task in fatigue status is reflected as the enhancement of the small-worldness.

EDITORIAL: Focus on the neural interface Focus on the neural interface

NASA Astrophysics Data System (ADS)

Durand, Dominique M.

2009-10-01

The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes. The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain-computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain-computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that they can fail to record reliably neural signals for long periods of time. McConnell et al show that by measuring the impedance of the tissue, one can evaluate the extent of the tissue response to the presence of the electrode. Another problem with the neural interface is the mismatch of the mechanical properties between electrode and tissue. Basinger et al use finite element modeling to analyze this mismatch in retinal prostheses and guide the design of new implantable devices. Electrical stimulation has been the method of choice to activate externally the nervous system. However, Zhang et al show that a novel dual hybrid device integrating electrical and optical stimulation can provide an effective interface for simultaneous recording and stimulation. By interfacing an EMG recording system and a movement detection system, Johnson and Fuglevand develop a model capable of predicting muscle activity during movement that could be important for the development of motor prostheses. Sensory restoration is another unsolved problem in neural prostheses. By developing a novel interface between the dorsal root ganglia and electrodes arrays, Gaunt et al show that it is possible to recruit afferent fibers for sensory substitution. Finally, by interfacing directly with muscles, Jung and colleagues show that stimulation of muscles involved in locomotion following spinal cord damage in rats can provide an effective treatment modality for incomplete spinal cord injury. This series of articles clearly shows that the interface is indeed one of the keys to successful therapeutic neural devices. The next Neural Interfaces Conference will take place in Los Angeles, CA in June 2010 and one can expect to see new developments in neural engineering obtained by focusing on the neural interface.

Preprocessing and meta-classification for brain-computer interfaces.

PubMed

Hammon, Paul S; de Sa, Virginia R

2007-03-01

A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. Perhaps the most obvious way to improve classification performance is the selection of an advanced learning algorithm. However, it is now well known in the BCI community that careful selection of preprocessing steps is crucial to the success of any classification scheme. Furthermore, recent work indicates that combining the output of multiple classifiers (meta-classification) leads to improved classification rates relative to single classifiers (Dornhege et al., 2004). In this paper, we develop an automated approach which systematically analyzes the relative contributions of different preprocessing and meta-classification approaches. We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.

TheBrain Technologies Corporation: Collapsing the Time to Knowledge.

ERIC Educational Resources Information Center

Misek, Marla

2003-01-01

TheBrain was created to take advantage of the most powerful information processor in existence - the human mind. Explains products of TheBrain Technologies Corporation,, which has developed computer interfaces to help individual users and corporations organize information in ways that make sense to them in the proper context. Describes a…

Evolvix BEST Names for semantic reproducibility across code2brain interfaces

PubMed Central

Scheuer, Katherine S.; Keel, Seth A.; Vyas, Vaibhav; Liblit, Ben; Hanlon, Bret; Ferris, Michael C.; Yin, John; Dutra, Inês; Pietsch, Anthony; Javid, Christine G.; Moog, Cecilia L.; Meyer, Jocelyn; Dresel, Jerdon; McLoone, Brian; Loberger, Sonya; Movaghar, Arezoo; Gilchrist‐Scott, Morgaine; Sabri, Yazeed; Sescleifer, Dave; Pereda‐Zorrilla, Ivan; Zietlow, Andrew; Smith, Rodrigo; Pietenpol, Samantha; Goldfinger, Jacob; Atzen, Sarah L.; Freiberg, Erika; Waters, Noah P.; Nusbaum, Claire; Nolan, Erik; Hotz, Alyssa; Kliman, Richard M.; Mentewab, Ayalew; Fregien, Nathan; Loewe, Martha

2016-01-01

Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general‐purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long‐term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder‐brains to reader‐brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core. PMID:27918836

Ultra-low-cost 3D gaze estimation: an intuitive high information throughput compliment to direct brain-machine interfaces

NASA Astrophysics Data System (ADS)

Abbott, W. W.; Faisal, A. A.

2012-08-01

Eye movements are highly correlated with motor intentions and are often retained by patients with serious motor deficiencies. Despite this, eye tracking is not widely used as control interface for movement in impaired patients due to poor signal interpretation and lack of control flexibility. We propose that tracking the gaze position in 3D rather than 2D provides a considerably richer signal for human machine interfaces by allowing direct interaction with the environment rather than via computer displays. We demonstrate here that by using mass-produced video-game hardware, it is possible to produce an ultra-low-cost binocular eye-tracker with comparable performance to commercial systems, yet 800 times cheaper. Our head-mounted system has 30 USD material costs and operates at over 120 Hz sampling rate with a 0.5-1 degree of visual angle resolution. We perform 2D and 3D gaze estimation, controlling a real-time volumetric cursor essential for driving complex user interfaces. Our approach yields an information throughput of 43 bits s-1, more than ten times that of invasive and semi-invasive brain-machine interfaces (BMIs) that are vastly more expensive. Unlike many BMIs our system yields effective real-time closed loop control of devices (10 ms latency), after just ten minutes of training, which we demonstrate through a novel BMI benchmark—the control of the video arcade game ‘Pong’.

Brain-computer interface controlled functional electrical stimulation system for ankle movement.

PubMed

Do, An H; Wang, Po T; King, Christine E; Abiri, Ahmad; Nenadic, Zoran

2011-08-26

Many neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop. Current physiotherapy techniques provide only a limited degree of motor function recovery in these individuals, and therefore novel therapies are needed. Brain-computer interface (BCI) is a relatively novel technology with a potential to restore, substitute, or augment lost motor behaviors in patients with neurological injuries. Here, we describe the first successful integration of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) system that enables the direct brain control of foot dorsiflexion in able-bodied individuals. A noninvasive EEG-based BCI system was integrated with a noninvasive FES system for foot dorsiflexion. Subjects underwent computer-cued epochs of repetitive foot dorsiflexion and idling while their EEG signals were recorded and stored for offline analysis. The analysis generated a prediction model that allowed EEG data to be analyzed and classified in real time during online BCI operation. The real-time online performance of the integrated BCI-FES system was tested in a group of five able-bodied subjects who used repetitive foot dorsiflexion to elicit BCI-FES mediated dorsiflexion of the contralateral foot. Five able-bodied subjects performed 10 alternations of idling and repetitive foot dorsifiexion to trigger BCI-FES mediated dorsifiexion of the contralateral foot. The epochs of BCI-FES mediated foot dorsifiexion were highly correlated with the epochs of voluntary foot dorsifiexion (correlation coefficient ranged between 0.59 and 0.77) with latencies ranging from 1.4 sec to 3.1 sec. In addition, all subjects achieved a 100% BCI-FES response (no omissions), and one subject had a single false alarm. This study suggests that the integration of a noninvasive BCI with a lower-extremity FES system is feasible. With additional modifications, the proposed BCI-FES system may offer a novel and effective therapy in the neuro-rehabilitation of individuals with lower extremity paralysis due to neurological injuries.

Putting the brain to work: neuroergonomics past, present, and future.

PubMed

Parasuraman, Raja; Wilson, Glenn F

2008-06-01

The authors describe research and applications in prominent areas of neuroergonomics. Because human factors/ergonomics examines behavior and mind at work, it should include the study of brain mechanisms underlying human performance. Neuroergonomic studies are reviewed in four areas: workload and vigilance, adaptive automation, neuroengineering, and molecular genetics and individual differences. Neuroimaging studies have helped identify the components of mental workload, workload assessment in complex tasks, and resource depletion in vigilance. Furthermore, real-time neurocognitive assessment of workload can trigger adaptive automation. Neural measures can also drive brain-computer interfaces to provide disabled users new communication channels. Finally, variants of particular genes can be associated with individual differences in specific cognitive functions. Neuroergonomics shows that considering what makes work possible - the human brain - can enrich understanding of the use of technology by humans and can inform technological design. Applications of neuroergonomics include the assessment of operator workload and vigilance, implementation of real-time adaptive automation, neuroengineering for people with disabilities, and design of selection and training methods.

Computational Virtual Reality (VR) as a human-computer interface in the operation of telerobotic systems

NASA Technical Reports Server (NTRS)

Bejczy, Antal K.

1995-01-01

This presentation focuses on the application of computer graphics or 'virtual reality' (VR) techniques as a human-computer interface tool in the operation of telerobotic systems. VR techniques offer very valuable task realization aids for planning, previewing and predicting robotic actions, operator training, and for visual perception of non-visible events like contact forces in robotic tasks. The utility of computer graphics in telerobotic operation can be significantly enhanced by high-fidelity calibration of virtual reality images to actual TV camera images. This calibration will even permit the creation of artificial (synthetic) views of task scenes for which no TV camera views are available.

Non-invasive transmission of sensorimotor information in humans using an EEG/focused ultrasound brain-to-brain interface

PubMed Central

Lee, Wonhye; Kim, Suji; Kim, Byeongnam; Lee, Chungki; Chung, Yong An; Kim, Laehyun; Yoo, Seung-Schik

2017-01-01

We present non-invasive means that detect unilateral hand motor brain activity from one individual and subsequently stimulate the somatosensory area of another individual, thus, enabling the remote hemispheric link between each brain hemisphere in humans. Healthy participants were paired as a sender and a receiver. A sender performed a motor imagery task of either right or left hand, and associated changes in the electroencephalogram (EEG) mu rhythm (8–10 Hz) originating from either hemisphere were programmed to move a computer cursor to a target that appeared in either left or right of the computer screen. When the cursor reaches its target, the outcome was transmitted to another computer over the internet, and actuated the focused ultrasound (FUS) devices that selectively and non-invasively stimulated either the right or left hand somatosensory area of the receiver. Small FUS transducers effectively allowed for the independent administration of stimulatory ultrasonic waves to somatosensory areas. The stimulation elicited unilateral tactile sensation of the hand from the receiver, thus establishing the hemispheric brain-to-brain interface (BBI). Although there was a degree of variability in task accuracy, six pairs of volunteers performed the BBI task in high accuracy, transferring approximately eight commands per minute. Linkage between the hemispheric brain activities among individuals suggests the possibility for expansion of the information bandwidth in the context of BBI. PMID:28598972

[Brain-computer interfaces, Locked-In syndrome, and disorders of consciousness].

PubMed

Lesenfants, Damien; Chatelle, Camille; Laureys, Steven; Noirhomme, Quentin

2015-10-01

Detecting signs of consciousness in patients with severe brain injury constitutes a real challenge for clinicians. The current gold standard in clinical diagnosis is the behavioral scale relying on motor abilities, which are often impaired or nonexistent in these patients. In this context, brain-computer interfaces (BCIs) could offer a potential complementary tool to detect signs of consciousness whilst bypassing the usual motor pathway. In addition to complementing behavioral assessments and potentially reducing error rate, BCIs could also serve as a communication tool for paralyzed but conscious patients, e.g., suffering from Locked-In Syndrome. In this paper, we report on recent work conducted by the Coma Science Group on BCI technology, aiming to optimize diagnosis and communication in patients with disorders of consciousness and Locked-In syndrome. © 2015 médecine/sciences – Inserm.

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.

PubMed

Kamrunnahar, M; Schiff, S J

2011-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%-90% for the hand movements and 70%-90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.

A Development Architecture for Serious Games Using BCI (Brain Computer Interface) Sensors

PubMed Central

Sung, Yunsick; Cho, Kyungeun; Um, Kyhyun

2012-01-01

Games that use brainwaves via brain–computer interface (BCI) devices, to improve brain functions are known as BCI serious games. Due to the difficulty of developing BCI serious games, various BCI engines and authoring tools are required, and these reduce the development time and cost. However, it is desirable to reduce the amount of technical knowledge of brain functions and BCI devices needed by game developers. Moreover, a systematic BCI serious game development process is required. In this paper, we present a methodology for the development of BCI serious games. We describe an architecture, authoring tools, and development process of the proposed methodology, and apply it to a game development approach for patients with mild cognitive impairment as an example. This application demonstrates that BCI serious games can be developed on the basis of expert-verified theories. PMID:23202227

Development traumatic brain injury computer user interface for disaster area in Indonesia supported by emergency broadband access network.

PubMed

Sutiono, Agung Budi; Suwa, Hirohiko; Ohta, Toshizumi; Arifin, Muh Zafrullah; Kitamura, Yohei; Yoshida, Kazunari; Merdika, Daduk; Qiantori, Andri; Iskandar

2012-12-01

Disasters bring consequences of negative impacts on the environment and human life. One of the common cause of critical condition is traumatic brain injury (TBI), namely, epidural (EDH) and subdural hematoma (SDH), due to downfall hard things during earthquake. We proposed and analyzed the user response, namely neurosurgeon, general doctor/surgeon and nurse when they interacted with TBI computer interface. The communication systems was supported by TBI web based applications using emergency broadband access network with tethered balloon and simulated in the field trial to evaluate the coverage area. The interface consisted of demography data and multi tabs for anamnesis, treatment, follow up and teleconference interfaces. The interface allows neurosurgeon, surgeon/general doctors and nurses to entry the EDH and SDH patient's data during referring them on the emergency simulation and evaluated based on time needs and their understanding. The average time needed was obtained after simulated by Lenovo T500 notebook using mouse; 8-10 min for neurosurgeons, 12-15 min for surgeons/general doctors and 15-19 min for nurses. By using Think Pad X201 Tablet, the time needed for entry data was 5-7 min for neurosurgeon, 7-10 min for surgeons/general doctors and 12-16 min for nurses. We observed that the time difference was depending on the computer type and user literacy qualification as well as their understanding on traumatic brain injury, particularly for the nurses. In conclusion, there are five data classification for simply TBI GUI, namely, 1) demography, 2) specific anamnesis for EDH and SDH, 3) treatment action and medicine of TBI, 4) follow up data display and 5) teleneurosurgery for streaming video consultation. The type of computer, particularly tablet PC was more convenient and faster for entry data, compare to that computer mouse touched pad. Emergency broadband access network using tethered balloon is possible to be employed to cover the communications systems in disaster area.

Towards SSVEP-based, portable, responsive Brain-Computer Interface.

PubMed

Kaczmarek, Piotr; Salomon, Pawel

2015-08-01

A Brain-Computer Interface in motion control application requires high system responsiveness and accuracy. SSVEP interface consisted of 2-8 stimuli and 2 channel EEG amplifier was presented in this paper. The observed stimulus is recognized based on a canonical correlation calculated in 1 second window, ensuring high interface responsiveness. A threshold classifier with hysteresis (T-H) was proposed for recognition purposes. Obtained results suggest that T-H classifier enables to significantly increase classifier performance (resulting in accuracy of 76%, while maintaining average false positive detection rate of stimulus different then observed one between 2-13%, depending on stimulus frequency). It was shown that the parameters of T-H classifier, maximizing true positive rate, can be estimated by gradient-based search since the single maximum was observed. Moreover the preliminary results, performed on a test group (N=4), suggest that for T-H classifier exists a certain set of parameters for which the system accuracy is similar to accuracy obtained for user-trained classifier.

Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor.

PubMed

Alonso-Valerdi, Luz María

2016-01-01

A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary. The modulation of brain signals through mental tasks is furthermore a skill that requires training. Unfortunately, not all the users acquire such skill. A practical solution to this problem is to assess the user probability of controlling a BCI system. Another possible solution is to set the bandwidth of the brain oscillations, which is highly sensitive to the users' age, sex and anatomy. With this in mind, NeuroIndex, a Python executable script, estimates a neurophysiological prediction index and the individual alpha frequency (IAF) of the user in question. These two parameters are useful to characterize the user EEG signals, and decide how to go through the complex process of adapting the human brain and the computing system on the basis of previously proposed methods. NeuroIndeX is not only the implementation of those methods, but it also complements the methods each other and provides an alternative way to obtain the prediction parameter. However, an important limitation of this application is its dependency on the IAF value, and some results should be interpreted with caution. The script along with some electroencephalographic datasets are available on a GitHub repository in order to corroborate the functionality and usability of this application.

Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor

PubMed Central

Alonso-Valerdi, Luz María

2016-01-01

A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary. The modulation of brain signals through mental tasks is furthermore a skill that requires training. Unfortunately, not all the users acquire such skill. A practical solution to this problem is to assess the user probability of controlling a BCI system. Another possible solution is to set the bandwidth of the brain oscillations, which is highly sensitive to the users' age, sex and anatomy. With this in mind, NeuroIndex, a Python executable script, estimates a neurophysiological prediction index and the individual alpha frequency (IAF) of the user in question. These two parameters are useful to characterize the user EEG signals, and decide how to go through the complex process of adapting the human brain and the computing system on the basis of previously proposed methods. NeuroIndeX is not only the implementation of those methods, but it also complements the methods each other and provides an alternative way to obtain the prediction parameter. However, an important limitation of this application is its dependency on the IAF value, and some results should be interpreted with caution. The script along with some electroencephalographic datasets are available on a GitHub repository in order to corroborate the functionality and usability of this application. PMID:27445783

PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data.

PubMed

Du, Haixiao; Xia, Mingrui; Zhao, Kang; Liao, Xuhong; Yang, Huazhong; Wang, Yu; He, Yong

2018-05-01

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease. © 2018 Wiley Periodicals, Inc.

Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users.

PubMed

Rutkowski, Tomasz M; Mori, Hiromu

2015-04-15

The paper presents a report on the recently developed BCI alternative for users suffering from impaired vision (lack of focus or eye-movements) or from the so-called "ear-blocking-syndrome" (limited hearing). We report on our recent studies of the extents to which vibrotactile stimuli delivered to the head of a user can serve as a platform for a brain computer interface (BCI) paradigm. In the proposed tactile and bone-conduction auditory BCI novel multiple head positions are used to evoke combined somatosensory and auditory (via the bone conduction effect) P300 brain responses, in order to define a multimodal tactile and bone-conduction auditory brain computer interface (tbcaBCI). In order to further remove EEG interferences and to improve P300 response classification synchrosqueezing transform (SST) is applied. SST outperforms the classical time-frequency analysis methods of the non-linear and non-stationary signals such as EEG. The proposed method is also computationally more effective comparing to the empirical mode decomposition. The SST filtering allows for online EEG preprocessing application which is essential in the case of BCI. Experimental results with healthy BCI-naive users performing online tbcaBCI, validate the paradigm, while the feasibility of the concept is illuminated through information transfer rate case studies. We present a comparison of the proposed SST-based preprocessing method, combined with a logistic regression (LR) classifier, together with classical preprocessing and LDA-based classification BCI techniques. The proposed tbcaBCI paradigm together with data-driven preprocessing methods are a step forward in robust BCI applications research. Copyright © 2014 Elsevier B.V. All rights reserved.

Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features

PubMed Central

Song, Le; Epps, Julien

2007-01-01

Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit synchronization features from the dynamical system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach. PMID:18364986

Real time system design of motor imagery brain-computer interface based on multi band CSP and SVM

NASA Astrophysics Data System (ADS)

Zhao, Li; Li, Xiaoqin; Bian, Yan

2018-04-01

Motion imagery (MT) is an effective method to promote the recovery of limbs in patients after stroke. Though an online MT brain computer interface (BCT) system, which apply MT, can enhance the patient's participation and accelerate their recovery process. The traditional method deals with the electroencephalogram (EEG) induced by MT by common spatial pattern (CSP), which is used to extract information from a frequency band. Tn order to further improve the classification accuracy of the system, information of two characteristic frequency bands is extracted. The effectiveness of the proposed feature extraction method is verified by off-line analysis of competition data and the analysis of online system.

Modular, bluetooth enabled, wireless electroencephalograph (EEG) platform.

PubMed

Lovelace, Joseph A; Witt, Tyler S; Beyette, Fred R

2013-01-01

A design for a modular, compact, and accurate wireless electroencephalograph (EEG) system is proposed. EEG is the only non-invasive measure for neuronal function of the brain. Using a number of digital signal processing (DSP) techniques, this neuronal function can be acquired and processed into meaningful representations of brain activity. The system described here utilizes Bluetooth to wirelessly transmit the digitized brain signal for an end application use. In this way, the system is portable, and modular in terms of the device to which it can interface. Brain Computer Interface (BCI) has become a popular extension of EEG systems in modern research. This design serves as a platform for applications using BCI capability.

Control of a brain-computer interface using stereotactic depth electrodes in and adjacent to the hippocampus

NASA Astrophysics Data System (ADS)

Krusienski, D. J.; Shih, J. J.

2011-04-01

A brain-computer interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans has used scalp-recorded electroencephalography or intracranial electrocorticography. The use of brain signals obtained directly from stereotactic depth electrodes to control a BCI has not previously been explored. In this study, event-related potentials (ERPs) recorded from bilateral stereotactic depth electrodes implanted in and adjacent to the hippocampus were used to control a P300 Speller paradigm. The ERPs were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in the two subjects tested. Our results demonstrate that ERPs from hippocampal and hippocampal adjacent depth electrodes can be used to reliably control the P300 Speller BCI paradigm.

Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces

NASA Astrophysics Data System (ADS)

Gutiérrez, David

2008-08-01

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using electroencephalographic (EEG) and, more recently, magnetoencephalographic (MEG) measurements of the brain function. Most of the current implementations of BCIs rely on EEG/MEG data of motor activities as such neural processes are well characterized, while the use of data related to cognitive activities has been neglected due to its intrinsic complexity. However, cognitive data usually has larger amplitude, lasts longer and, in some cases, cognitive brain signals are easier to control at will than motor signals. This paper briefy reviews the use of EEG/MEG data of cognitive processes in the implementation of BCIs. Specifically, this paper reviews some of the neuromechanisms, signal features, and processing methods involved. This paper also refers to some of the author's work in the area of detection and classifcation of cognitive signals for BCIs using variability enhancement, parametric modeling, and spatial fltering, as well as recent developments in BCI performance evaluation.

Multimodal 2D Brain Computer Interface.

PubMed

Almajidy, Rand K; Boudria, Yacine; Hofmann, Ulrich G; Besio, Walter; Mankodiya, Kunal

2015-08-01

In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features.

Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gutierrez, David

2008-08-11

Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using electroencephalographic (EEG) and, more recently, magnetoencephalographic (MEG) measurements of the brain function. Most of the current implementations of BCIs rely on EEG/MEG data of motor activities as such neural processes are well characterized, while the use of data related to cognitive activities has been neglected due to its intrinsic complexity. However, cognitive data usually has larger amplitude, lasts longer and, in some cases, cognitive brain signals are easier to control at will than motor signals. This paper briefy reviews the use of EEG/MEGmore » data of cognitive processes in the implementation of BCIs. Specifically, this paper reviews some of the neuromechanisms, signal features, and processing methods involved. This paper also refers to some of the author's work in the area of detection and classifcation of cognitive signals for BCIs using variability enhancement, parametric modeling, and spatial fltering, as well as recent developments in BCI performance evaluation.« less

Mental workload during brain-computer interface training.

PubMed

Felton, Elizabeth A; Williams, Justin C; Vanderheiden, Gregg C; Radwin, Robert G

2012-01-01

It is not well understood how people perceive the difficulty of performing brain-computer interface (BCI) tasks, which specific aspects of mental workload contribute the most, and whether there is a difference in perceived workload between participants who are able-bodied and disabled. This study evaluated mental workload using the NASA Task Load Index (TLX), a multi-dimensional rating procedure with six subscales: Mental Demands, Physical Demands, Temporal Demands, Performance, Effort, and Frustration. Able-bodied and motor disabled participants completed the survey after performing EEG-based BCI Fitts' law target acquisition and phrase spelling tasks. The NASA-TLX scores were similar for able-bodied and disabled participants. For example, overall workload scores (range 0-100) for 1D horizontal tasks were 48.5 (SD = 17.7) and 46.6 (SD 10.3), respectively. The TLX can be used to inform the design of BCIs that will have greater usability by evaluating subjective workload between BCI tasks, participant groups, and control modalities. Mental workload of brain-computer interfaces (BCI) can be evaluated with the NASA Task Load Index (TLX). The TLX is an effective tool for comparing subjective workload between BCI tasks, participant groups (able-bodied and disabled), and control modalities. The data can inform the design of BCIs that will have greater usability.

Collaborative Brain-Computer Interface for Aiding Decision-Making

PubMed Central

Poli, Riccardo; Valeriani, Davide; Cinel, Caterina

2014-01-01

We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making. PMID:25072739

Visual gate for brain-computer interfaces.

PubMed

Dias, N S; Jacinto, L R; Mendes, P M; Correia, J H

2009-01-01

Brain-Computer Interfaces (BCI) based on event related potentials (ERP) have been successfully developed for applications like virtual spellers and navigation systems. This study tests the use of visual stimuli unbalanced in the subject's field of view to simultaneously cue mental imagery tasks (left vs. right hand movement) and detect subject attention. The responses to unbalanced cues were compared with the responses to balanced cues in terms of classification accuracy. Subject specific ERP spatial filters were calculated for optimal group separation. The unbalanced cues appear to enhance early ERPs related to cue visuospatial processing that improved the classification accuracy (as low as 6%) of ERPs in response to left vs. right cues soon (150-200 ms) after the cue presentation. This work suggests that such visual interface may be of interest in BCI applications as a gate mechanism for attention estimation and validation of control decisions.

Incorporating modern neuroscience findings to improve brain-computer interfaces: tracking auditory attention.

PubMed

Wronkiewicz, Mark; Larson, Eric; Lee, Adrian Kc

2016-10-01

Brain-computer interface (BCI) technology allows users to generate actions based solely on their brain signals. However, current non-invasive BCIs generally classify brain activity recorded from surface electroencephalography (EEG) electrodes, which can hinder the application of findings from modern neuroscience research. In this study, we use source imaging-a neuroimaging technique that projects EEG signals onto the surface of the brain-in a BCI classification framework. This allowed us to incorporate prior research from functional neuroimaging to target activity from a cortical region involved in auditory attention. Classifiers trained to detect attention switches performed better with source imaging projections than with EEG sensor signals. Within source imaging, including subject-specific anatomical MRI information (instead of using a generic head model) further improved classification performance. This source-based strategy also reduced accuracy variability across three dimensionality reduction techniques-a major design choice in most BCIs. Our work shows that source imaging provides clear quantitative and qualitative advantages to BCIs and highlights the value of incorporating modern neuroscience knowledge and methods into BCI systems.

A Novel Mu Rhythm-based Brain Computer Interface Design that uses a Programmable System on Chip.

PubMed

Joshi, Rohan; Saraswat, Prateek; Gajendran, Rudhram

2012-01-01

This paper describes the system design of a portable and economical mu rhythm based Brain Computer Interface which employs Cypress Semiconductors Programmable System on Chip (PSoC). By carrying out essential processing on the PSoC, the use of an extra computer is eliminated, resulting in considerable cost savings. Microsoft Visual Studio 2005 and PSoC Designer 5.01 are employed in developing the software for the system, the hardware being custom designed. In order to test the usability of the BCI, preliminary testing is carried out by training three subjects who were able to demonstrate control over their electroencephalogram by moving a cursor present at the center of the screen towards the indicated direction with an average accuracy greater than 70% and a bit communication rate of up to 7 bits/min.

Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games

NASA Astrophysics Data System (ADS)

Nijholt, Anton; Reuderink, Boris; Oude Bos, Danny

In recent years we have seen a rising interest in brain-computer interfacing for human-computer interaction and potential game applications. Until now, however, we have almost only seen attempts where BCI is used to measure the affective state of the user or in neurofeedback games. There have hardly been any attempts to design BCI games where BCI is considered to be one of the possible input modalities that can be used to control the game. One reason may be that research still follows the paradigms of the traditional, medically oriented, BCI approaches. In this paper we discuss current BCI research from the viewpoint of games and game design. It is hoped that this survey will make clear that we need to design different games than we used to, but that such games can nevertheless be interesting and exciting.

A Novel Mu Rhythm-based Brain Computer Interface Design that uses a Programmable System on Chip

PubMed Central

Joshi, Rohan; Saraswat, Prateek; Gajendran, Rudhram

2012-01-01

This paper describes the system design of a portable and economical mu rhythm based Brain Computer Interface which employs Cypress Semiconductors Programmable System on Chip (PSoC). By carrying out essential processing on the PSoC, the use of an extra computer is eliminated, resulting in considerable cost savings. Microsoft Visual Studio 2005 and PSoC Designer 5.01 are employed in developing the software for the system, the hardware being custom designed. In order to test the usability of the BCI, preliminary testing is carried out by training three subjects who were able to demonstrate control over their electroencephalogram by moving a cursor present at the center of the screen towards the indicated direction with an average accuracy greater than 70% and a bit communication rate of up to 7 bits/min. PMID:23493871

Endogenous Sensory Discrimination and Selection by a Fast Brain Switch for a High Transfer Rate Brain-Computer Interface.

PubMed

Xu, Ren; Jiang, Ning; Dosen, Strahinja; Lin, Chuang; Mrachacz-Kersting, Natalie; Dremstrup, Kim; Farina, Dario

2016-08-01

In this study, we present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of  ∼ 80% and  ∼ 70%, and an information transfer rate of  ∼ 7 bits/min and  ∼ 5 bits/min, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.

A brain computer interface using electrocorticographic signals in humans

NASA Astrophysics Data System (ADS)

Leuthardt, Eric C.; Schalk, Gerwin; Wolpaw, Jonathan R.; Ojemann, Jeffrey G.; Moran, Daniel W.

2004-06-01

Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain. The authors declare that they have no competing financial interests.

Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain

PubMed Central

2016-01-01

An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain. PMID:26982717

Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain.

PubMed

Li, Guangye; Zhang, Dingguo

2016-01-01

An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain.

An improved asynchronous brain interface: making use of the temporal history of the LF-ASD feature vectors

NASA Astrophysics Data System (ADS)

Bashashati, Ali; Mason, Steve; Ward, Rabab K.; Birch, Gary E.

2006-06-01

The low-frequency asynchronous switch design (LF-ASD) has been introduced as a direct brain interface (BI) for asynchronous control applications. Asynchronous interfaces, as opposed to synchronous interfaces, have the advantage of being operational at all times and not only at specific system-defined periods. This paper modifies the LF-ASD design by incorporating into the system more knowledge about the attempted movements. Specifically, the history of feature values extracted from the EEG signal is used to detect a right index finger movement attempt. Using data collected from individuals with high-level spinal cord injuries and able-bodied subjects, it is shown that the error characteristics of the modified design are significantly better than the previous LF-ASD design. The true positive rate percentage increased by up to 15 which corresponds to 50% improvement when the system is operating with false positive rates in the 1-2% range.

Neuroanatomical correlates of brain-computer interface performance.

PubMed

Kasahara, Kazumi; DaSalla, Charles Sayo; Honda, Manabu; Hanakawa, Takashi

2015-04-15

Brain-computer interfaces (BCIs) offer a potential means to replace or restore lost motor function. However, BCI performance varies considerably between users, the reasons for which are poorly understood. Here we investigated the relationship between sensorimotor rhythm (SMR)-based BCI performance and brain structure. Participants were instructed to control a computer cursor using right- and left-hand motor imagery, which primarily modulated their left- and right-hemispheric SMR powers, respectively. Although most participants were able to control the BCI with success rates significantly above chance level even at the first encounter, they also showed substantial inter-individual variability in BCI success rate. Participants also underwent T1-weighted three-dimensional structural magnetic resonance imaging (MRI). The MRI data were subjected to voxel-based morphometry using BCI success rate as an independent variable. We found that BCI performance correlated with gray matter volume of the supplementary motor area, supplementary somatosensory area, and dorsal premotor cortex. We suggest that SMR-based BCI performance is associated with development of non-primary somatosensory and motor areas. Advancing our understanding of BCI performance in relation to its neuroanatomical correlates may lead to better customization of BCIs based on individual brain structure. Copyright © 2015 Elsevier Inc. All rights reserved.

An optical brain computer interface for environmental control.

PubMed

Ayaz, Hasan; Shewokis, Patricia A; Bunce, Scott; Onaral, Banu

2011-01-01

A brain computer interface (BCI) is a system that translates neurophysiological signals detected from the brain to supply input to a computer or to control a device. Volitional control of neural activity and its real-time detection through neuroimaging modalities are key constituents of BCI systems. The purpose of this study was to develop and test a new BCI design that utilizes intention-related cognitive activity within the dorsolateral prefrontal cortex using functional near infrared (fNIR) spectroscopy. fNIR is a noninvasive, safe, portable and affordable optical technique with which to monitor hemodynamic changes, in the brain's cerebral cortex. Because of its portability and ease of use, fNIR is amenable to deployment in ecologically valid natural working environments. We integrated a control paradigm in a computerized 3D virtual environment to augment interactivity. Ten healthy participants volunteered for a two day study in which they navigated a virtual environment with keyboard inputs, but were required to use the fNIR-BCI for interaction with virtual objects. Results showed that participants consistently utilized the fNIR-BCI with an overall success rate of 84% and volitionally increased their cerebral oxygenation level to trigger actions within the virtual environment.

Brain-computer interfaces for EEG neurofeedback: peculiarities and solutions.

PubMed

Huster, René J; Mokom, Zacharais N; Enriquez-Geppert, Stefanie; Herrmann, Christoph S

2014-01-01

Neurofeedback training procedures designed to alter a person's brain activity have been in use for nearly four decades now and represent one of the earliest applications of brain-computer interfaces (BCI). The majority of studies using neurofeedback technology relies on recordings of the electroencephalogram (EEG) and applies neurofeedback in clinical contexts, exploring its potential as treatment for psychopathological syndromes. This clinical focus significantly affects the technology behind neurofeedback BCIs. For example, in contrast to other BCI applications, neurofeedback BCIs usually rely on EEG-derived features with only a minimum of additional processing steps being employed. Here, we highlight the peculiarities of EEG-based neurofeedback BCIs and consider their relevance for software implementations. Having reviewed already existing packages for the implementation of BCIs, we introduce our own solution which specifically considers the relevance of multi-subject handling for experimental and clinical trials, for example by implementing ready-to-use solutions for pseudo-/sham-neurofeedback. © 2013.

Permanency analysis on human electroencephalogram signals for pervasive Brain-Computer Interface systems.

PubMed

Sadeghi, Koosha; Junghyo Lee; Banerjee, Ayan; Sohankar, Javad; Gupta, Sandeep K S

2017-07-01

Brain-Computer Interface (BCI) systems use some permanent features of brain signals to recognize their corresponding cognitive states with high accuracy. However, these features are not perfectly permanent, and BCI system should be continuously trained over time, which is tedious and time consuming. Thus, analyzing the permanency of signal features is essential in determining how often to repeat training. In this paper, we monitor electroencephalogram (EEG) signals, and analyze their behavior through continuous and relatively long period of time. In our experiment, we record EEG signals corresponding to rest state (eyes open and closed) from one subject everyday, for three and a half months. The results show that signal features such as auto-regression coefficients remain permanent through time, while others such as power spectral density specifically in 5-7 Hz frequency band are not permanent. In addition, eyes open EEG data shows more permanency than eyes closed data.

Performance variation in motor imagery brain-computer interface: a brief review.

PubMed

Ahn, Minkyu; Jun, Sung Chan

2015-03-30

Brain-computer interface (BCI) technology has attracted significant attention over recent decades, and has made remarkable progress. However, BCI still faces a critical hurdle, in that performance varies greatly across and even within subjects, an obstacle that degrades the reliability of BCI systems. Understanding the causes of these problems is important if we are to create more stable systems. In this short review, we report the most recent studies and findings on performance variation, especially in motor imagery-based BCI, which has found that low-performance groups have a less-developed brain network that is incapable of motor imagery. Further, psychological and physiological states influence performance variation within subjects. We propose a possible strategic approach to deal with this variation, which may contribute to improving the reliability of BCI. In addition, the limitations of current work and opportunities for future studies are discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface

PubMed Central

Kamrunnahar, M.; Schiff, S. J.

2017-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%–90% for the hand movements and 70%–90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models. PMID:22255799

Comparison of spike-sorting algorithms for future hardware implementation.

PubMed

Gibson, Sarah; Judy, Jack W; Markovic, Dejan

2008-01-01

Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.

Toward a brain-computer interface for Alzheimer's disease patients by combining classical conditioning and brain state classification.

PubMed

Liberati, Giulia; Dalboni da Rocha, Josué Luiz; van der Heiden, Linda; Raffone, Antonino; Birbaumer, Niels; Olivetti Belardinelli, Marta; Sitaram, Ranganatha

2012-01-01

Brain-computer interfaces (BCIs) provide alternative methods for communicating and acting on the world, since messages or commands are conveyed from the brain to an external device without using the normal output pathways of peripheral nerves and muscles. Alzheimer's disease (AD) patients in the most advanced stages, who have lost the ability to communicate verbally, could benefit from a BCI that may allow them to convey basic thoughts (e.g., "yes" and "no") and emotions. There is currently no report of such research, mostly because the cognitive deficits in AD patients pose serious limitations to the use of traditional BCIs, which are normally based on instrumental learning and require users to self-regulate their brain activation. Recent studies suggest that not only self-regulated brain signals, but also involuntary signals, for instance related to emotional states, may provide useful information about the user, opening up the path for so-called "affective BCIs". These interfaces do not necessarily require users to actively perform a cognitive task, and may therefore be used with patients who are cognitively challenged. In the present hypothesis paper, we propose a paradigm shift from instrumental learning to classical conditioning, with the aim of discriminating "yes" and "no" thoughts after associating them to positive and negative emotional stimuli respectively. This would represent a first step in the development of a BCI that could be used by AD patients, lending a new direction not only for communication, but also for rehabilitation and diagnosis.

Does arousal interfere with operant conditioning of spike-wave discharges in genetic epileptic rats?

PubMed

Osterhagen, Lasse; Breteler, Marinus; van Luijtelaar, Gilles

2010-06-01

One of the ways in which brain computer interfaces can be used is neurofeedback (NF). Subjects use their brain activation to control an external device, and with this technique it is also possible to learn to control aspects of the brain activity by operant conditioning. Beneficial effects of NF training on seizure occurrence have been described in epileptic patients. Little research has been done about differentiating NF effectiveness by type of epilepsy, particularly, whether idiopathic generalized seizures are susceptible to NF. In this experiment, seizures that manifest themselves as spike-wave discharges (SWDs) in the EEG were reinforced during 10 sessions in 6 rats of the WAG/Rij strain, an animal model for absence epilepsy. EEG's were recorded before and after the training sessions. Reinforcing SWDs let to decreased SWD occurrences during training; however, the changes during training were not persistent in the post-training sessions. Because behavioural states are known to have an influence on the occurrence of SWDs, it is proposed that the reinforcement situation increased arousal which resulted in fewer SWDs. Additional tests supported this hypothesis. The outcomes have implications for the possibility to train SWDs with operant learning techniques. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Developments in brain-machine interfaces from the perspective of robotics.

PubMed

Kim, Hyun K; Park, Shinsuk; Srinivasan, Mandayam A

2009-04-01

Many patients suffer from the loss of motor skills, resulting from traumatic brain and spinal cord injuries, stroke, and many other disabling conditions. Thanks to technological advances in measuring and decoding the electrical activity of cortical neurons, brain-machine interfaces (BMI) have become a promising technology that can aid paralyzed individuals. In recent studies on BMI, robotic manipulators have demonstrated their potential as neuroprostheses. Restoring motor skills through robot manipulators controlled by brain signals may improve the quality of life of people with disability. This article reviews current robotic technologies that are relevant to BMI and suggests strategies that could improve the effectiveness of a brain-operated neuroprosthesis through robotics.

[The Changes in the Hemodynamic Activity of the Brain during Moroe Imagery Training with the Use of Brain-Computer Interface].

PubMed

Frolov, A A; Husek, D; Silchenko, A V; Tintera, Y; Rydlo, J

2016-01-01

With the use of functional MRI (fMRI), we studied the changes in brain hemodynamic activity of healthy subjects during motor imagery training with the use brain-computer interface (BCI), which is based on the recognition of EEG patterns of imagined movements. ANOVA dispersion analysis showed there are 14 areas of the brain where statistically sgnificant changes were registered. Detailed analysis of the activity in these areas before and after training (Student's and Mann-Whitney tests) reduced the amount of areas with significantly changed activity to five; these are Brodmann areas 44 and 45, insula, middle frontal gyrus, and anterior cingulate gyrus. We suggest that these changes are caused by the formation of memory traces of those brain activity patterns which are most accurately recognized by BCI classifiers as correspondent with limb movements. We also observed a tendency of increase in the activity of motor imagery after training. The hemodynamic activity in all these 14 areas during real movements was either approximatly the same or significantly higher than during motor imagery; activity during imagined leg movements was higher that that during imagined arm movements, except for the areas of representation of arms.

Dry and noncontact EEG sensors for mobile brain-computer interfaces.

PubMed

Chi, Yu Mike; Wang, Yu-Te; Wang, Yijun; Maier, Christoph; Jung, Tzyy-Ping; Cauwenberghs, Gert

2012-03-01

Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.

Spelling is Just a Click Away - A User-Centered Brain-Computer Interface Including Auto-Calibration and Predictive Text Entry.

PubMed

Kaufmann, Tobias; Völker, Stefan; Gunesch, Laura; Kübler, Andrea

2012-01-01

Brain-computer interfaces (BCI) based on event-related potentials (ERP) allow for selection of characters from a visually presented character-matrix and thus provide a communication channel for users with neurodegenerative disease. Although they have been topic of research for more than 20 years and were multiply proven to be a reliable communication method, BCIs are almost exclusively used in experimental settings, handled by qualified experts. This study investigates if ERP-BCIs can be handled independently by laymen without expert support, which is inevitable for establishing BCIs in end-user's daily life situations. Furthermore we compared the classic character-by-character text entry against a predictive text entry (PTE) that directly incorporates predictive text into the character-matrix. N = 19 BCI novices handled a user-centered ERP-BCI application on their own without expert support. The software individually adjusted classifier weights and control parameters in the background, invisible to the user (auto-calibration). All participants were able to operate the software on their own and to twice correctly spell a sentence with the auto-calibrated classifier (once with PTE, once without). Our PTE increased spelling speed and, importantly, did not reduce accuracy. In sum, this study demonstrates feasibility of auto-calibrating ERP-BCI use, independently by laymen and the strong benefit of integrating predictive text directly into the character-matrix.

Fusion with Language Models Improves Spelling Accuracy for ERP-based Brain Computer Interface Spellers

PubMed Central

Orhan, Umut; Erdogmus, Deniz; Roark, Brian; Purwar, Shalini; Hild, Kenneth E.; Oken, Barry; Nezamfar, Hooman; Fried-Oken, Melanie

2013-01-01

Event related potentials (ERP) corresponding to a stimulus in electroencephalography (EEG) can be used to detect the intent of a person for brain computer interfaces (BCI). This paradigm is widely utilized to build letter-by-letter text input systems using BCI. Nevertheless using a BCI-typewriter depending only on EEG responses will not be sufficiently accurate for single-trial operation in general, and existing systems utilize many-trial schemes to achieve accuracy at the cost of speed. Hence incorporation of a language model based prior or additional evidence is vital to improve accuracy and speed. In this paper, we study the effects of Bayesian fusion of an n-gram language model with a regularized discriminant analysis ERP detector for EEG-based BCIs. The letter classification accuracies are rigorously evaluated for varying language model orders as well as number of ERP-inducing trials. The results demonstrate that the language models contribute significantly to letter classification accuracy. Specifically, we find that a BCI-speller supported by a 4-gram language model may achieve the same performance using 3-trial ERP classification for the initial letters of the words and using single trial ERP classification for the subsequent ones. Overall, fusion of evidence from EEG and language models yields a significant opportunity to increase the word rate of a BCI based typing system. PMID:22255652

EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis

NASA Astrophysics Data System (ADS)

Mak, Joseph N.; McFarland, Dennis J.; Vaughan, Theresa M.; McCane, Lynn M.; Tsui, Phillippa Z.; Zeitlin, Debra J.; Sellers, Eric W.; Wolpaw, Jonathan R.

2012-04-01

The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for >60% of the variance in copy-spelling performance (p < 0.001, mean R2 = 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.

Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning.

PubMed

Lin, Chin-Teng; Chen, Yu-Chieh; Huang, Teng-Yi; Chiu, Tien-Ting; Ko, Li-Wei; Liang, Sheng-Fu; Hsieh, Hung-Yi; Hsu, Shang-Hwa; Duann, Jeng-Ren

2008-05-01

Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

PubMed Central

Su, Yi; Routhu, Sudhamayee; Moon, Kee S.; Lee, Sung Q.; Youm, WooSub; Ozturk, Yusuf

2016-01-01

All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time. PMID:27669264

Decoding Onset and Direction of Movements Using Electrocorticographic (ECoG) Signals in Humans

DTIC Science & Technology

2012-08-08

Institute, Troy, NY, USA 2 J Crayton Pruitt Family Department of Biomed Engineering, University of Florida, Gainesville, FL, USA 3 BCI R&D Program...INTRODUCTION Brain-computer interfaces ( BCIs ) aim to translate a person’s intentions into meaningful computer commands using brain activity alone...applications for those suffering from neuromuscular disorders (Sejnowski et al., 2007; Tan and Nijholt, 2010). For example, a BCI that detects intended move

Design and Evaluation of Fusion Approach for Combining Brain and Gaze Inputs for Target Selection

PubMed Central

Évain, Andéol; Argelaguet, Ferran; Casiez, Géry; Roussel, Nicolas; Lécuyer, Anatole

2016-01-01

Gaze-based interfaces and Brain-Computer Interfaces (BCIs) allow for hands-free human–computer interaction. In this paper, we investigate the combination of gaze and BCIs. We propose a novel selection technique for 2D target acquisition based on input fusion. This new approach combines the probabilistic models for each input, in order to better estimate the intent of the user. We evaluated its performance against the existing gaze and brain–computer interaction techniques. Twelve participants took part in our study, in which they had to search and select 2D targets with each of the evaluated techniques. Our fusion-based hybrid interaction technique was found to be more reliable than the previous gaze and BCI hybrid interaction techniques for 10 participants over 12, while being 29% faster on average. However, similarly to what has been observed in hybrid gaze-and-speech interaction, gaze-only interaction technique still provides the best performance. Our results should encourage the use of input fusion, as opposed to sequential interaction, in order to design better hybrid interfaces. PMID:27774048

Brain-Congruent Instruction: Does the Computer Make It Feasible?

ERIC Educational Resources Information Center

Stewart, William J.

1984-01-01

Based on the premise that computers could translate brain research findings into classroom practice, this article presents discoveries concerning human brain development, organization, and operation, and describes brain activity monitoring devices, brain function and structure variables, and a procedure for monitoring and analyzing brain activity…

[Design and implementation of controlling smart car systems using P300 brain-computer interface].

PubMed

Wang, Jinjia; Yang, Chengjie; Hu, Bei

2013-04-01

Using human electroencephalogram (EEG) to control external devices in order to achieve a variety of functions has been focus of the field of brain-computer interface (BCI) research. P300 is experiments which stimulate the eye to produce EEG by using letters flashing, and then identify the corresponding letters. In this paper, some improvements based on the P300 experiments were made??. Firstly, the matrix of flashing letters were modified into words which represent a certain sense. Secondly, the BCI2000 procedures were added with the corresponding source code. Thirdly, the smart car systems were designed using the radiofrequency signal. Finally it was realized that the evoked potentials were used to control the state of the smart car.

Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

PubMed Central

Manyakov, Nikolay V.; Chumerin, Nikolay; Combaz, Adrien; Van Hulle, Marc M.

2011-01-01

We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects. PMID:21941530

Motor prediction in Brain-Computer Interfaces for controlling mobile robots.

PubMed

Geng, Tao; Gan, John Q

2008-01-01

EEG-based Brain-Computer Interface (BCI) can be regarded as a new channel for motor control except that it does not involve muscles. Normal neuromuscular motor control has two fundamental components: (1) to control the body, and (2) to predict the consequences of the control command, which is called motor prediction. In this study, after training with a specially designed BCI paradigm based on motor imagery, two subjects learnt to predict the time course of some features of the EEG signals. It is shown that, with this newly-obtained motor prediction skill, subjects can use motor imagery of feet to directly control a mobile robot to avoid obstacles and reach a small target in a time-critical scenario.

Paired Associative Stimulation Using Brain-Computer Interfaces for Stroke Rehabilitation: A Pilot Study.

PubMed

Cho, Woosang; Sabathiel, Nikolaus; Ortner, Rupert; Lechner, Alexander; Irimia, Danut C; Allison, Brendan Z; Edlinger, Guenter; Guger, Christoph

2016-06-13

Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. Paired associative stimulation (PAS) uses brain-computer interface (BCI) technology to monitor patients' movement imagery in real-time, and utilizes the information to control functional electrical stimulation (FES) and bar feedback for complete sensorimotor closed loop. To realize this approach, we introduce the recoveriX system, a hardware and software platform for PAS. After 10 sessions of recoveriX training, one stroke patient partially regained control of dorsiflexion in her paretic wrist. A controlled group study is planned with a new version of the recoveriX system, which will use a new FES system and an avatar instead of bar feedback.

Toward more versatile and intuitive cortical brain-machine interfaces.

PubMed

Andersen, Richard A; Kellis, Spencer; Klaes, Christian; Aflalo, Tyson

2014-09-22

Brain-machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain-machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics. Copyright © 2014 Elsevier Ltd. All rights reserved.

Graphene-Based Interfaces Do Not Alter Target Nerve Cells.

PubMed

Fabbro, Alessandra; Scaini, Denis; León, Verónica; Vázquez, Ester; Cellot, Giada; Privitera, Giulia; Lombardi, Lucia; Torrisi, Felice; Tomarchio, Flavia; Bonaccorso, Francesco; Bosi, Susanna; Ferrari, Andrea C; Ballerini, Laura; Prato, Maurizio

2016-01-26

Neural-interfaces rely on the ability of electrodes to transduce stimuli into electrical patterns delivered to the brain. In addition to sensitivity to the stimuli, stability in the operating conditions and efficient charge transfer to neurons, the electrodes should not alter the physiological properties of the target tissue. Graphene is emerging as a promising material for neuro-interfacing applications, given its outstanding physico-chemical properties. Here, we use graphene-based substrates (GBSs) to interface neuronal growth. We test our GBSs on brain cell cultures by measuring functional and synaptic integrity of the emerging neuronal networks. We show that GBSs are permissive interfaces, even when uncoated by cell adhesion layers, retaining unaltered neuronal signaling properties, thus being suitable for carbon-based neural prosthetic devices.

Language evolution and human-computer interaction

NASA Technical Reports Server (NTRS)

Grudin, Jonathan; Norman, Donald A.

1991-01-01

Many of the issues that confront designers of interactive computer systems also appear in natural language evolution. Natural languages and human-computer interfaces share as their primary mission the support of extended 'dialogues' between responsive entities. Because in each case one participant is a human being, some of the pressures operating on natural languages, causing them to evolve in order to better support such dialogue, also operate on human-computer 'languages' or interfaces. This does not necessarily push interfaces in the direction of natural language - since one entity in this dialogue is not a human, this is not to be expected. Nonetheless, by discerning where the pressures that guide natural language evolution also appear in human-computer interaction, we can contribute to the design of computer systems and obtain a new perspective on natural languages.

Cyber-workstation for computational neuroscience.

PubMed

Digiovanna, Jack; Rattanatamrong, Prapaporn; Zhao, Ming; Mahmoudi, Babak; Hermer, Linda; Figueiredo, Renato; Principe, Jose C; Fortes, Jose; Sanchez, Justin C

2010-01-01

A Cyber-Workstation (CW) to study in vivo, real-time interactions between computational models and large-scale brain subsystems during behavioral experiments has been designed and implemented. The design philosophy seeks to directly link the in vivo neurophysiology laboratory with scalable computing resources to enable more sophisticated computational neuroscience investigation. The architecture designed here allows scientists to develop new models and integrate them with existing models (e.g. recursive least-squares regressor) by specifying appropriate connections in a block-diagram. Then, adaptive middleware transparently implements these user specifications using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and flexible neuroscience research test-bed to provide the neurophysiology laboratory extensive computational power from an outside source. The CW consolidates distributed software and hardware resources to support time-critical and/or resource-demanding computing during data collection from behaving animals. This power and flexibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefly the computational infrastructure and its most relevant components. Each component is discussed within a systematic process of setting up an in vivo, neuroscience experiment. Furthermore, a co-adaptive brain machine interface is implemented on the CW to illustrate how this integrated computational and experimental platform can be used to study systems neurophysiology and learning in a behavior task. We believe this implementation is also the first remote execution and adaptation of a brain-machine interface.

Cyber-Workstation for Computational Neuroscience

PubMed Central

DiGiovanna, Jack; Rattanatamrong, Prapaporn; Zhao, Ming; Mahmoudi, Babak; Hermer, Linda; Figueiredo, Renato; Principe, Jose C.; Fortes, Jose; Sanchez, Justin C.

2009-01-01

A Cyber-Workstation (CW) to study in vivo, real-time interactions between computational models and large-scale brain subsystems during behavioral experiments has been designed and implemented. The design philosophy seeks to directly link the in vivo neurophysiology laboratory with scalable computing resources to enable more sophisticated computational neuroscience investigation. The architecture designed here allows scientists to develop new models and integrate them with existing models (e.g. recursive least-squares regressor) by specifying appropriate connections in a block-diagram. Then, adaptive middleware transparently implements these user specifications using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and flexible neuroscience research test-bed to provide the neurophysiology laboratory extensive computational power from an outside source. The CW consolidates distributed software and hardware resources to support time-critical and/or resource-demanding computing during data collection from behaving animals. This power and flexibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefly the computational infrastructure and its most relevant components. Each component is discussed within a systematic process of setting up an in vivo, neuroscience experiment. Furthermore, a co-adaptive brain machine interface is implemented on the CW to illustrate how this integrated computational and experimental platform can be used to study systems neurophysiology and learning in a behavior task. We believe this implementation is also the first remote execution and adaptation of a brain-machine interface. PMID:20126436

Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke.

PubMed

van Dokkum, L E H; Ward, T; Laffont, I

2015-02-01

The idea of using brain computer interfaces (BCI) for rehabilitation emerged relatively recently. Basically, BCI for neurorehabilitation involves the recording and decoding of local brain signals generated by the patient, as he/her tries to perform a particular task (even if imperfect), or during a mental imagery task. The main objective is to promote the recruitment of selected brain areas involved and to facilitate neural plasticity. The recorded signal can be used in several ways: (i) to objectify and strengthen motor imagery-based training, by providing the patient feedback on the imagined motor task, for example, in a virtual environment; (ii) to generate a desired motor task via functional electrical stimulation or rehabilitative robotic orthoses attached to the patient's limb – encouraging and optimizing task execution as well as "closing" the disrupted sensorimotor loop by giving the patient the appropriate sensory feedback; (iii) to understand cerebral reorganizations after lesion, in order to influence or even quantify plasticity-induced changes in brain networks. For example, applying cerebral stimulation to re-equilibrate inter-hemispheric imbalance as shown by functional recording of brain activity during movement may help recovery. Its potential usefulness for a patient population has been demonstrated on various levels and its diverseness in interface applications makes it adaptable to a large population. The position and status of these very new rehabilitation systems should now be considered with respect to our current and more or less validated traditional methods, as well as in the light of the wide range of possible brain damage. The heterogeneity in post-damage expression inevitably complicates the decoding of brain signals and thus their use in pathological conditions, asking for controlled clinical trials. Copyright © 2015. Published by Elsevier Masson SAS.

Evaluation of a modified Fitts law brain-computer interface target acquisition task in able and motor disabled individuals

NASA Astrophysics Data System (ADS)

Felton, E. A.; Radwin, R. G.; Wilson, J. A.; Williams, J. C.

2009-10-01

A brain-computer interface (BCI) is a communication system that takes recorded brain signals and translates them into real-time actions, in this case movement of a cursor on a computer screen. This work applied Fitts' law to the evaluation of performance on a target acquisition task during sensorimotor rhythm-based BCI training. Fitts' law, which has been used as a predictor of movement time in studies of human movement, was used here to determine the information transfer rate, which was based on target acquisition time and target difficulty. The information transfer rate was used to make comparisons between control modalities and subject groups on the same task. Data were analyzed from eight able-bodied and five motor disabled participants who wore an electrode cap that recorded and translated their electroencephalogram (EEG) signals into computer cursor movements. Direct comparisons were made between able-bodied and disabled subjects, and between EEG and joystick cursor control in able-bodied subjects. Fitts' law aptly described the relationship between movement time and index of difficulty for each task movement direction when evaluated separately and averaged together. This study showed that Fitts' law can be successfully applied to computer cursor movement controlled by neural signals.

Assistive device with conventional, alternative, and brain-computer interface inputs to enhance interaction with the environment for people with amyotrophic lateral sclerosis: a feasibility and usability study.

PubMed

Schettini, Francesca; Riccio, Angela; Simione, Luca; Liberati, Giulia; Caruso, Mario; Frasca, Vittorio; Calabrese, Barbara; Mecella, Massimo; Pizzimenti, Alessia; Inghilleri, Maurizio; Mattia, Donatella; Cincotti, Febo

2015-03-01

To evaluate the feasibility and usability of an assistive technology (AT) prototype designed to be operated with conventional/alternative input channels and a P300-based brain-computer interface (BCI) in order to provide users who have different degrees of muscular impairment resulting from amyotrophic lateral sclerosis (ALS) with communication and environmental control applications. Proof-of-principle study with a convenience sample. An apartment-like space designed to be fully accessible by people with motor disabilities for occupational therapy, placed in a neurologic rehabilitation hospital. End-users with ALS (N=8; 5 men, 3 women; mean age ± SD, 60 ± 12 y) recruited by a clinical team from an ALS center. Three experimental conditions based on (1) a widely validated P300-based BCI alone; (2) the AT prototype operated by a conventional/alternative input device tailored to the specific end-user's residual motor abilities; and (3) the AT prototype accessed by a P300-based BCI. These 3 conditions were presented to all participants in 3 different sessions. System usability was evaluated in terms of effectiveness (accuracy), efficiency (written symbol rate, time for correct selection, workload), and end-user satisfaction (overall satisfaction) domains. A comparison of the data collected in the 3 conditions was performed. Effectiveness and end-user satisfaction did not significantly differ among the 3 experimental conditions. Condition III was less efficient than condition II as expressed by the longer time for correct selection. A BCI can be used as an input channel to access an AT by persons with ALS, with no significant reduction of usability. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

PubMed Central

Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

2016-01-01

At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals. PMID:27458376

Operation of a brain-computer interface walking simulator for individuals with spinal cord injury

PubMed Central

2013-01-01

Background Spinal cord injury (SCI) can leave the affected individuals with paraparesis or paraplegia, thus rendering them unable to ambulate. Since there are currently no restorative treatments for this population, novel approaches such as brain-controlled prostheses have been sought. Our recent studies show that a brain-computer interface (BCI) can be used to control ambulation within a virtual reality environment (VRE), suggesting that a BCI-controlled lower extremity prosthesis for ambulation may be feasible. However, the operability of our BCI has not yet been tested in a SCI population. Methods Five participants with paraplegia or tetraplegia due to SCI underwent a 10-min training session in which they alternated between kinesthetic motor imagery (KMI) of idling and walking while their electroencephalogram (EEG) were recorded. Participants then performed a goal-oriented online task, where they utilized KMI to control the linear ambulation of an avatar while making 10 sequential stops at designated points within the VRE. Multiple online trials were performed in a single day, and this procedure was repeated across 5 experimental days. Results Classification accuracy of idling and walking was estimated offline and ranged from 60.5% (p = 0.0176) to 92.3% (p = 1.36×10−20) across participants and days. Offline analysis revealed that the activation of mid-frontal areas mostly in the μ and low β bands was the most consistent feature for differentiating between idling and walking KMI. In the online task, participants achieved an average performance of 7.4±2.3 successful stops in 273±51 sec. These performances were purposeful, i.e. significantly different from the random walk Monte Carlo simulations (p<0.01), and all but one participant achieved purposeful control within the first day of the experiments. Finally, all participants were able to maintain purposeful control throughout the study, and their online performances improved over time. Conclusions The results of this study demonstrate that SCI participants can purposefully operate a self-paced BCI walking simulator to complete a goal-oriented ambulation task. The operation of the proposed BCI system requires short training, is intuitive, and robust against participant-to-participant and day-to-day neurophysiological variations. These findings indicate that BCI-controlled lower extremity prostheses for gait rehabilitation or restoration after SCI may be feasible in the future. PMID:23866985

Computer interface for mechanical arm

NASA Technical Reports Server (NTRS)

Derocher, W. L.; Zermuehlen, R. O.

1978-01-01

Man/machine interface commands computer-controlled mechanical arm. Remotely-controlled arm has six degrees of freedom and is controlled through "supervisory-control" mode, in which all motions of arm follow set of preprogramed sequences. For simplicity, few prescribed commands are required to accomplish entire operation. Applications include operating computer-controlled arm to handle radioactive of explosive materials or commanding arm to perform functions in hostile environments. Modified version using displays may be applied in medicine.

An Architectural Experience for Interface Design

ERIC Educational Resources Information Center

Gong, Susan P.

2016-01-01

The problem of human-computer interface design was brought to the foreground with the emergence of the personal computer, the increasing complexity of electronic systems, and the need to accommodate the human operator in these systems. With each new technological generation discovering the interface design problems of its own technologies, initial…

Control of a visual keyboard using an electrocorticographic brain-computer interface.

PubMed

Krusienski, Dean J; Shih, Jerry J

2011-05-01

Brain-computer interfaces (BCIs) are devices that enable severely disabled people to communicate and interact with their environments using their brain waves. Most studies investigating BCI in humans have used scalp EEG as the source of electrical signals and focused on motor control of prostheses or computer cursors on a screen. The authors hypothesize that the use of brain signals obtained directly from the cortical surface will more effectively control a communication/spelling task compared to scalp EEG. A total of 6 patients with medically intractable epilepsy were tested for the ability to control a visual keyboard using electrocorticographic (ECOG) signals. ECOG data collected during a P300 visual task paradigm were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in 5 of the 6 people tested. ECOG data from electrodes outside the language cortex contributed to the classifier and enabled participants to write words on a visual keyboard. This is a novel finding because previous invasive BCI research in humans used signals exclusively from the motor cortex to control a computer cursor or prosthetic device. These results demonstrate that ECOG signals from electrodes both overlying and outside the language cortex can reliably control a visual keyboard to generate language output without voice or limb movements.

ABrIL - Advanced Brain Imaging Lab : a cloud based computation environment for cooperative neuroimaging projects.

PubMed

Neves Tafula, Sérgio M; Moreira da Silva, Nádia; Rozanski, Verena E; Silva Cunha, João Paulo

2014-01-01

Neuroscience is an increasingly multidisciplinary and highly cooperative field where neuroimaging plays an important role. Neuroimaging rapid evolution is demanding for a growing number of computing resources and skills that need to be put in place at every lab. Typically each group tries to setup their own servers and workstations to support their neuroimaging needs, having to learn from Operating System management to specific neuroscience software tools details before any results can be obtained from each setup. This setup and learning process is replicated in every lab, even if a strong collaboration among several groups is going on. In this paper we present a new cloud service model - Brain Imaging Application as a Service (BiAaaS) - and one of its implementation - Advanced Brain Imaging Lab (ABrIL) - in the form of an ubiquitous virtual desktop remote infrastructure that offers a set of neuroimaging computational services in an interactive neuroscientist-friendly graphical user interface (GUI). This remote desktop has been used for several multi-institution cooperative projects with different neuroscience objectives that already achieved important results, such as the contribution to a high impact paper published in the January issue of the Neuroimage journal. The ABrIL system has shown its applicability in several neuroscience projects with a relatively low-cost, promoting truly collaborative actions and speeding up project results and their clinical applicability.

Adaptive hybrid brain-computer interaction: ask a trainer for assistance!

PubMed

Müller-Putz, Gernot R; Steyrl, David; Faller, Josef

2014-01-01

In applying mental imagery brain-computer interfaces (BCIs) to end users, training is a key part for novice users to get control. In general learning situations, it is an established concept that a trainer assists a trainee to improve his/her aptitude in certain skills. In this work, we want to evaluate whether we can apply this concept in the context of event-related desynchronization (ERD) based, adaptive, hybrid BCIs. Hence, in a first session we merged the features of a high aptitude BCI user, a trainer, and a novice user, the trainee, in a closed-loop BCI feedback task and automatically adapted the classifier over time. In a second session the trainees operated the system unassisted. Twelve healthy participants ran through this protocol. Along with the trainer, the trainees achieved a very high overall peak accuracy of 95.3 %. In the second session, where users operated the BCI unassisted, they still achieved a high overall peak accuracy of 83.6%. Ten of twelve first time BCI users successfully achieved significantly better than chance accuracy. Concluding, we can say that this trainer-trainee approach is very promising. Future research should investigate, whether this approach is superior to conventional training approaches. This trainer-trainee concept could have potential for future application of BCIs to end users.

Temporal coding of brain patterns for direct limb control in humans.

PubMed

Müller-Putz, Gernot R; Scherer, Reinhold; Pfurtscheller, Gert; Neuper, Christa

2010-01-01

For individuals with a high spinal cord injury (SCI) not only the lower limbs, but also the upper extremities are paralyzed. A neuroprosthesis can be used to restore the lost hand and arm function in those tetraplegics. The main problem for this group of individuals, however, is the reduced ability to voluntarily operate device controllers. A brain-computer interface provides a non-manual alternative to conventional input devices by translating brain activity patterns into control commands. We show that the temporal coding of individual mental imagery pattern can be used to control two independent degrees of freedom - grasp and elbow function - of an artificial robotic arm by utilizing a minimum number of EEG scalp electrodes. We describe the procedure from the initial screening to the final application. From eight naïve subjects participating online feedback experiments, four were able to voluntarily control an artificial arm by inducing one motor imagery pattern derived from one EEG derivation only.

A brain-computer interface based on self-regulation of gamma-oscillations in the superior parietal cortex

NASA Astrophysics Data System (ADS)

Grosse-Wentrup, Moritz; Schölkopf, Bernhard

2014-10-01

Objective. Brain-computer interface (BCI) systems are often based on motor- and/or sensory processes that are known to be impaired in late stages of amyotrophic lateral sclerosis (ALS). We propose a novel BCI designed for patients in late stages of ALS that only requires high-level cognitive processes to transmit information from the user to the BCI. Approach. We trained subjects via EEG-based neurofeedback to self-regulate the amplitude of gamma-oscillations in the superior parietal cortex (SPC). We argue that parietal gamma-oscillations are likely to be associated with high-level attentional processes, thereby providing a communication channel that does not rely on the integrity of sensory- and/or motor-pathways impaired in late stages of ALS. Main results. Healthy subjects quickly learned to self-regulate gamma-power in the SPC by alternating between states of focused attention and relaxed wakefulness, resulting in an average decoding accuracy of 70.2%. One locked-in ALS patient (ALS-FRS-R score of zero) achieved an average decoding accuracy significantly above chance-level though insufficient for communication (55.8%). Significance. Self-regulation of gamma-power in the SPC is a feasible paradigm for brain-computer interfacing and may be preserved in late stages of ALS. This provides a novel approach to testing whether completely locked-in ALS patients retain the capacity for goal-directed thinking.

The Impact of Different Visual Feedbacks in User Training on Motor Imagery Control in BCI.

PubMed

Zapała, Dariusz; Francuz, Piotr; Zapała, Ewelina; Kopiś, Natalia; Wierzgała, Piotr; Augustynowicz, Paweł; Majkowski, Andrzej; Kołodziej, Marcin

2018-03-01

The challenges of research into brain-computer interfaces (BCI) include significant individual differences in learning pace and in the effective operation of BCI devices. The use of neurofeedback training is a popular method of improving the effectiveness BCI operation. The purpose of the present study was to determine to what extent it is possible to improve the effectiveness of operation of sensorimotor rhythm-based brain-computer interfaces (SMR-BCI) by supplementing user training with elements modifying the characteristics of visual feedback. Four experimental groups had training designed to reinforce BCI control by: visual feedback in the form of dummy faces expressing emotions (Group 1); flashing the principal elements of visual feedback (Group 2) and giving both visual feedbacks in one condition (Group 3). The fourth group participated in training with no modifications (Group 4). Training consisted of a series of trials where the subjects directed a ball into a basket located to the right or left side of the screen. In Group 1 a schematic image a face, placed on the controlled object, showed various emotions, depending on the accuracy of control. In Group 2, the cue and targets were flashed with different frequency (4 Hz) than the remaining elements visible on the monitor. Both modifications were also used simultaneously in Group 3. SMR activity during the task was recorded before and after the training. In Group 3 there was a significant improvement in SMR control, compared to subjects in Group 2 and 4 (control). Differences between subjects in Groups 1, 2 and 4 (control) were insignificant. This means that relatively small changes in the training procedure may significantly impact the effectiveness of BCI control. Analysis of behavioural data acquired from all participants at training showed greater effectiveness in directing the object towards the right side of the screen. Subjects with the greatest improvement in SMR control showed a significantly lower difference in the accuracy of rightward and leftward movement than others.

A Brain-Machine Interface Instructed by Direct Intracortical Microstimulation

PubMed Central

O'Doherty, Joseph E.; Lebedev, Mikhail A.; Hanson, Timothy L.; Fitzsimmons, Nathan A.; Nicolelis, Miguel A. L.

2009-01-01

Brain–machine interfaces (BMIs) establish direct communication between the brain and artificial actuators. As such, they hold considerable promise for restoring mobility and communication in patients suffering from severe body paralysis. To achieve this end, future BMIs must also provide a means for delivering sensory signals from the actuators back to the brain. Prosthetic sensation is needed so that neuroprostheses can be better perceived and controlled. Here we show that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI. Somatosensory instructions were provided to two monkeys operating the BMI using either: (a) vibrotactile stimulation of the monkey's hands or (b) multi-channel intracortical microstimulation (ICMS) delivered to the primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PP) in the other. Stimulus delivery was contingent on the position of the computer cursor: the monkey placed it in the center of the screen to receive machine–brain recursive input. After 2 weeks of training, the same level of proficiency in utilizing somatosensory information was achieved with ICMS of S1 as with the stimulus delivered to the hand skin. ICMS of PP was not effective. These results indicate that direct, bi-directional communication between the brain and neuroprosthetic devices can be achieved through the combination of chronic multi-electrode recording and microstimulation of S1. We propose that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices. PMID:19750199

Preliminary results of BRAVO project: brain computer interfaces for Robotic enhanced Action in Visuo-motOr tasks.

PubMed

Bergamasco, Massimo; Frisoli, Antonio; Fontana, Marco; Loconsole, Claudio; Leonardis, Daniele; Troncossi, Marco; Foumashi, Mohammad Mozaffari; Parenti-Castelli, Vincenzo

2011-01-01

This paper presents the preliminary results of the project BRAVO (Brain computer interfaces for Robotic enhanced Action in Visuo-motOr tasks). The objective of this project is to define a new approach to the development of assistive and rehabilitative robots for motor impaired users to perform complex visuomotor tasks that require a sequence of reaches, grasps and manipulations of objects. BRAVO aims at developing new robotic interfaces and HW/SW architectures for rehabilitation and regain/restoration of motor function in patients with upper limb sensorimotor impairment through extensive rehabilitation therapy and active assistance in the execution of Activities of Daily Living. The final system developed within this project will include a robotic arm exoskeleton and a hand orthosis that will be integrated together for providing force assistance. The main novelty that BRAVO introduces is the control of the robotic assistive device through the active prediction of intention/action. The system will actually integrate the information about the movement carried out by the user with a prediction of the performed action through an interpretation of current gaze of the user (measured through eye-tracking), brain activation (measured through BCI) and force sensor measurements. © 2011 IEEE

Neuromodulation, agency and autonomy.

PubMed

Glannon, Walter

2014-01-01

Neuromodulation consists in altering brain activity to restore mental and physical functions in individuals with neuropsychiatric disorders and brain and spinal cord injuries. This can be achieved by delivering electrical stimulation that excites or inhibits neural tissue, by using electrical signals in the brain to move computer cursors or robotic arms, or by displaying brain activity to subjects who regulate that activity by their own responses to it. As enabling prostheses, deep-brain stimulation and brain-computer interfaces (BCIs) are forms of extended embodiment that become integrated into the individual's conception of himself as an autonomous agent. In BCIs and neurofeedback, the success or failure of the techniques depends on the interaction between the learner and the trainer. The restoration of agency and autonomy through neuromodulation thus involves neurophysiological, psychological and social factors.

Feasibility of task-specific brain-machine interface training for upper-extremity paralysis in patients with chronic hemiparetic stroke.

PubMed

Nishimoto, Atsuko; Kawakami, Michiyuki; Fujiwara, Toshiyuki; Hiramoto, Miho; Honaga, Kaoru; Abe, Kaoru; Mizuno, Katsuhiro; Ushiba, Junichi; Liu, Meigen

2018-01-10

Brain-machine interface training was developed for upper-extremity rehabilitation for patients with severe hemiparesis. Its clinical application, however, has been limited because of its lack of feasibility in real-world rehabilitation settings. We developed a new compact task-specific brain-machine interface system that enables task-specific training, including reach-and-grasp tasks, and studied its clinical feasibility and effectiveness for upper-extremity motor paralysis in patients with stroke. Prospective beforeâ€"after study. Twenty-six patients with severe chronic hemiparetic stroke. Participants were trained with the brain-machine interface system to pick up and release pegs during 40-min sessions and 40 min of standard occupational therapy per day for 10 days. Fugl-Meyer upper-extremity motor (FMA) and Motor Activity Log-14 amount of use (MAL-AOU) scores were assessed before and after the intervention. To test its feasibility, 4 occupational therapists who operated the system for the first time assessed it with the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST) 2.0. FMA and MAL-AOU scores improved significantly after brain-machine interface training, with the effect sizes being medium and large, respectively (p<0.01, d=0.55; p<0.01, d=0.88). QUEST effectiveness and safety scores showed feasibility and satisfaction in the clinical setting. Our newly developed compact brain-machine interface system is feasible for use in real-world clinical settings.

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

PubMed Central

Hong, Keum-Shik; Khan, Muhammad Jawad

2017-01-01

In this article, non-invasive hybrid brain–computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain–computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided. PMID:28790910

Toward Petascale Biologically Plausible Neural Networks

NASA Astrophysics Data System (ADS)

Long, Lyle

This talk will describe an approach to achieving petascale neural networks. Artificial intelligence has been oversold for many decades. Computers in the beginning could only do about 16,000 operations per second. Computer processing power, however, has been doubling every two years thanks to Moore's law, and growing even faster due to massively parallel architectures. Finally, 60 years after the first AI conference we have computers on the order of the performance of the human brain (1016 operations per second). The main issues now are algorithms, software, and learning. We have excellent models of neurons, such as the Hodgkin-Huxley model, but we do not know how the human neurons are wired together. With careful attention to efficient parallel computing, event-driven programming, table lookups, and memory minimization massive scale simulations can be performed. The code that will be described was written in C + + and uses the Message Passing Interface (MPI). It uses the full Hodgkin-Huxley neuron model, not a simplified model. It also allows arbitrary network structures (deep, recurrent, convolutional, all-to-all, etc.). The code is scalable, and has, so far, been tested on up to 2,048 processor cores using 107 neurons and 109 synapses.

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

PubMed Central

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-01

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier. PMID:28124985

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.

PubMed

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-23

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.

Brain-computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum.

PubMed

Friedrich, Elisabeth V C; Suttie, Neil; Sivanathan, Aparajithan; Lim, Theodore; Louchart, Sandy; Pineda, Jaime A

2014-01-01

Individuals with autism spectrum disorder (ASD) show deficits in social and communicative skills, including imitation, empathy, and shared attention, as well as restricted interests and repetitive patterns of behaviors. Evidence for and against the idea that dysfunctions in the mirror neuron system are involved in imitation and could be one underlying cause for ASD is discussed in this review. Neurofeedback interventions have reduced symptoms in children with ASD by self-regulation of brain rhythms. However, cortical deficiencies are not the only cause of these symptoms. Peripheral physiological activity, such as the heart rate and its variability, is closely linked to neurophysiological signals and associated with social engagement. Therefore, a combined approach targeting the interplay between brain, body, and behavior could be more effective. Brain-computer interface applications for combined neurofeedback and biofeedback treatment for children with ASD are currently nonexistent. To facilitate their use, we have designed an innovative game that includes social interactions and provides neural- and body-based feedback that corresponds directly to the underlying significance of the trained signals as well as to the behavior that is reinforced.

Prediction of brain-computer interface aptitude from individual brain structure.

PubMed

Halder, S; Varkuti, B; Bogdan, M; Kübler, A; Rosenstiel, W; Sitaram, R; Birbaumer, N

2013-01-01

Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. This confirms that structural brain traits contribute to individual performance in BCI use.

Prediction of brain-computer interface aptitude from individual brain structure

PubMed Central

Halder, S.; Varkuti, B.; Bogdan, M.; Kübler, A.; Rosenstiel, W.; Sitaram, R.; Birbaumer, N.

2013-01-01

Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use. PMID:23565083

Evolvix BEST Names for semantic reproducibility across code2brain interfaces.

PubMed

Loewe, Laurence; Scheuer, Katherine S; Keel, Seth A; Vyas, Vaibhav; Liblit, Ben; Hanlon, Bret; Ferris, Michael C; Yin, John; Dutra, Inês; Pietsch, Anthony; Javid, Christine G; Moog, Cecilia L; Meyer, Jocelyn; Dresel, Jerdon; McLoone, Brian; Loberger, Sonya; Movaghar, Arezoo; Gilchrist-Scott, Morgaine; Sabri, Yazeed; Sescleifer, Dave; Pereda-Zorrilla, Ivan; Zietlow, Andrew; Smith, Rodrigo; Pietenpol, Samantha; Goldfinger, Jacob; Atzen, Sarah L; Freiberg, Erika; Waters, Noah P; Nusbaum, Claire; Nolan, Erik; Hotz, Alyssa; Kliman, Richard M; Mentewab, Ayalew; Fregien, Nathan; Loewe, Martha

2017-01-01

Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general-purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long-term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder-brains to reader-brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core. © 2016 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

A Macintosh based data system for array spectrometers (Poster)

NASA Astrophysics Data System (ADS)

Bregman, J.; Moss, N.

An interactive data aquisition and reduction system has been assembled by combining a Macintosh computer with an instrument controller (an Apple II computer) via an RS-232 interface. The data system provides flexibility for operating different linear array spectrometers. The standard Macintosh interface is used to provide ease of operation and to allow transferring the reduced data to commercial graphics software.

BNCI systems as a potential assistive technology: ethical issues and participatory research in the BrainAble project.

PubMed

Carmichael, Clare; Carmichael, Patrick

2014-01-01

This paper highlights aspects related to current research and thinking about ethical issues in relation to Brain Computer Interface (BCI) and Brain-Neuronal Computer Interfaces (BNCI) research through the experience of one particular project, BrainAble, which is exploring and developing the potential of these technologies to enable people with complex disabilities to control computers. It describes how ethical practice has been developed both within the multidisciplinary research team and with participants. The paper presents findings in which participants shared their views of the project prototypes, of the potential of BCI/BNCI systems as an assistive technology, and of their other possible applications. This draws attention to the importance of ethical practice in projects where high expectations of technologies, and representations of "ideal types" of disabled users may reinforce stereotypes or drown out participant "voices". Ethical frameworks for research and development in emergent areas such as BCI/BNCI systems should be based on broad notions of a "duty of care" while being sufficiently flexible that researchers can adapt project procedures according to participant needs. They need to be frequently revisited, not only in the light of experience, but also to ensure they reflect new research findings and ever more complex and powerful technologies.

[Neural engineering and neural prostheses].

PubMed

Gao, Shang-Kai; Zhang, Zhi-Guang; Gao, Xiao-Rong; Hong, Bo; Yang, Fu-Sheng

2006-03-01

The motivation of the brain-computer interface (BCI) research and its potential applications are introduced in this paper. Some of the problems in BCI-based medical device developments are also discussed.

Design and implementation of a P300-based brain-computer interface for controlling an internet browser.

PubMed

Mugler, Emily M; Ruf, Carolin A; Halder, Sebastian; Bensch, Michael; Kubler, Andrea

2010-12-01

An electroencephalographic (EEG) brain-computer interface (BCI) internet browser was designed and evaluated with 10 healthy volunteers and three individuals with advanced amyotrophic lateral sclerosis (ALS), all of whom were given tasks to execute on the internet using the browser. Participants with ALS achieved an average accuracy of 73% and a subsequent information transfer rate (ITR) of 8.6 bits/min and healthy participants with no prior BCI experience over 90% accuracy and an ITR of 14.4 bits/min. We define additional criteria for unrestricted internet access for evaluation of the presented and future internet browsers, and we provide a review of the existing browsers in the literature. The P300-based browser provides unrestricted access and enables free web surfing for individuals with paralysis.

Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry.

PubMed

Korczowski, L; Congedo, M; Jutten, C

2015-08-01

The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.

Application of a brain-computer interface for person authentication using EEG responses to photo stimuli.

PubMed

Mu, Zhendong; Yin, Jinhai; Hu, Jianfeng

2018-01-01

In this paper, a person authentication system that can effectively identify individuals by generating unique electroencephalogram signal features in response to self-face and non-self-face photos is presented. In order to achieve a good stability performance, the sequence of self-face photo including first-occurrence position and non-first-occurrence position are taken into account in the serial occurrence of visual stimuli. In addition, a Fisher linear classification method and event-related potential technique for feature analysis is adapted to yield remarkably better outcomes than that by most of the existing methods in the field. The results have shown that the EEG-based person authentications via brain-computer interface can be considered as a suitable approach for biometric authentication system.

Brain Painting: First Evaluation of a New Brain–Computer Interface Application with ALS-Patients and Healthy Volunteers

PubMed Central

Münßinger, Jana I.; Halder, Sebastian; Kleih, Sonja C.; Furdea, Adrian; Raco, Valerio; Hösle, Adi; Kübler, Andrea

2010-01-01

Brain–computer interfaces (BCIs) enable paralyzed patients to communicate; however, up to date, no creative expression was possible. The current study investigated the accuracy and user-friendliness of P300-Brain Painting, a new BCI application developed to paint pictures using brain activity only. Two different versions of the P300-Brain Painting application were tested: A colored matrix tested by a group of ALS-patients (n = 3) and healthy participants (n = 10), and a black and white matrix tested by healthy participants (n = 10). The three ALS-patients achieved high accuracies; two of them reaching above 89% accuracy. In healthy subjects, a comparison between the P300-Brain Painting application (colored matrix) and the P300-Spelling application revealed significantly lower accuracy and P300 amplitudes for the P300-Brain Painting application. This drop in accuracy and P300 amplitudes was not found when comparing the P300-Spelling application to an adapted, black and white matrix of the P300-Brain Painting application. By employing a black and white matrix, the accuracy of the P300-Brain Painting application was significantly enhanced and reached the accuracy of the P300-Spelling application. ALS-patients greatly enjoyed P300-Brain Painting and were able to use the application with the same accuracy as healthy subjects. P300-Brain Painting enables paralyzed patients to express themselves creatively and to participate in the prolific society through exhibitions. PMID:21151375

Improving brain computer interface research through user involvement - The transformative potential of integrating civil society organisations in research projects

PubMed Central

Wakunuma, Kutoma; Rainey, Stephen; Hansen, Christian

2017-01-01

Research on Brain Computer Interfaces (BCI) often aims to provide solutions for vulnerable populations, such as individuals with diseases, conditions or disabilities that keep them from using traditional interfaces. Such research thereby contributes to the public good. This contribution to the public good corresponds to a broader drive of research and funding policy that focuses on promoting beneficial societal impact. One way of achieving this is to engage with the public. In practical terms this can be done by integrating civil society organisations (CSOs) in research. The open question at the heart of this paper is whether and how such CSO integration can transform the research and contribute to the public good. To answer this question the paper describes five detailed qualitative case studies of research projects including CSOs. The paper finds that transformative impact of CSO integration is possible but by no means assured. It provides recommendations on how transformative impact can be promoted. PMID:28207882

Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing.

PubMed

Rajan, Krishna; Garofalo, Erik; Chiolerio, Alessandro

2018-01-27

A recent trend in the development of high mass consumption electron devices is towards electronic textiles (e-textiles), smart wearable devices, smart clothes, and flexible or printable electronics. Intrinsically soft, stretchable, flexible, Wearable Memories and Computing devices (WMCs) bring us closer to sci-fi scenarios, where future electronic systems are totally integrated in our everyday outfits and help us in achieving a higher comfort level, interacting for us with other digital devices such as smartphones and domotics, or with analog devices, such as our brain/peripheral nervous system. WMC will enable each of us to contribute to open and big data systems as individual nodes, providing real-time information about physical and environmental parameters (including air pollution monitoring, sound and light pollution, chemical or radioactive fallout alert, network availability, and so on). Furthermore, WMC could be directly connected to human brain and enable extremely fast operation and unprecedented interface complexity, directly mapping the continuous states available to biological systems. This review focuses on recent advances in nanotechnology and materials science and pays particular attention to any result and promising technology to enable intrinsically soft, stretchable, flexible WMC.

Control of an electrical prosthesis with an SSVEP-based BCI.

PubMed

Müller-Putz, Gernot R; Pfurtscheller, Gert

2008-01-01

Brain-computer interfaces (BCIs) are systems that establish a direct connection between the human brain and a computer, thus providing an additional communication channel. They are used in a broad field of applications nowadays. One important issue is the control of neuroprosthetic devices for the restoration of the grasp function in spinal-cord-injured people. In this communication, an asynchronous (self-paced) four-class BCI based on steady-state visual evoked potentials (SSVEPs) was used to control a two-axes electrical hand prosthesis. During training, four healthy participants reached an online classification accuracy between 44% and 88%. Controlling the prosthetic hand asynchronously, the participants reached a performance of 75.5 to 217.5 s to copy a series of movements, whereas the fastest possible duration determined by the setup was 64 s. The number of false negative (FN) decisions varied from 0 to 10 (the maximal possible decisions were 34). It can be stated that the SSVEP-based BCI, operating in an asynchronous mode, is feasible for the control of neuroprosthetic devices with the flickering lights mounted on its surface.

Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing

PubMed Central

Rajan, Krishna; Garofalo, Erik

2018-01-01

A recent trend in the development of high mass consumption electron devices is towards electronic textiles (e-textiles), smart wearable devices, smart clothes, and flexible or printable electronics. Intrinsically soft, stretchable, flexible, Wearable Memories and Computing devices (WMCs) bring us closer to sci-fi scenarios, where future electronic systems are totally integrated in our everyday outfits and help us in achieving a higher comfort level, interacting for us with other digital devices such as smartphones and domotics, or with analog devices, such as our brain/peripheral nervous system. WMC will enable each of us to contribute to open and big data systems as individual nodes, providing real-time information about physical and environmental parameters (including air pollution monitoring, sound and light pollution, chemical or radioactive fallout alert, network availability, and so on). Furthermore, WMC could be directly connected to human brain and enable extremely fast operation and unprecedented interface complexity, directly mapping the continuous states available to biological systems. This review focuses on recent advances in nanotechnology and materials science and pays particular attention to any result and promising technology to enable intrinsically soft, stretchable, flexible WMC. PMID:29382050

A novel Morse code-inspired method for multiclass motor imagery brain-computer interface (BCI) design.

PubMed

Jiang, Jun; Zhou, Zongtan; Yin, Erwei; Yu, Yang; Liu, Yadong; Hu, Dewen

2015-11-01

Motor imagery (MI)-based brain-computer interfaces (BCIs) allow disabled individuals to control external devices voluntarily, helping us to restore lost motor functions. However, the number of control commands available in MI-based BCIs remains limited, limiting the usability of BCI systems in control applications involving multiple degrees of freedom (DOF), such as control of a robot arm. To address this problem, we developed a novel Morse code-inspired method for MI-based BCI design to increase the number of output commands. Using this method, brain activities are modulated by sequences of MI (sMI) tasks, which are constructed by alternately imagining movements of the left or right hand or no motion. The codes of the sMI task was detected from EEG signals and mapped to special commands. According to permutation theory, an sMI task with N-length allows 2 × (2(N)-1) possible commands with the left and right MI tasks under self-paced conditions. To verify its feasibility, the new method was used to construct a six-class BCI system to control the arm of a humanoid robot. Four subjects participated in our experiment and the averaged accuracy of the six-class sMI tasks was 89.4%. The Cohen's kappa coefficient and the throughput of our BCI paradigm are 0.88 ± 0.060 and 23.5bits per minute (bpm), respectively. Furthermore, all of the subjects could operate an actual three-joint robot arm to grasp an object in around 49.1s using our approach. These promising results suggest that the Morse code-inspired method could be used in the design of BCIs for multi-DOF control. Copyright © 2015 Elsevier Ltd. All rights reserved.

Building an organic computing device with multiple interconnected brains

PubMed Central

Pais-Vieira, Miguel; Chiuffa, Gabriela; Lebedev, Mikhail; Yadav, Amol; Nicolelis, Miguel A. L.

2015-01-01

Recently, we proposed that Brainets, i.e. networks formed by multiple animal brains, cooperating and exchanging information in real time through direct brain-to-brain interfaces, could provide the core of a new type of computing device: an organic computer. Here, we describe the first experimental demonstration of such a Brainet, built by interconnecting four adult rat brains. Brainets worked by concurrently recording the extracellular electrical activity generated by populations of cortical neurons distributed across multiple rats chronically implanted with multi-electrode arrays. Cortical neuronal activity was recorded and analyzed in real time, and then delivered to the somatosensory cortices of other animals that participated in the Brainet using intracortical microstimulation (ICMS). Using this approach, different Brainet architectures solved a number of useful computational problems, such as discrete classification, image processing, storage and retrieval of tactile information, and even weather forecasting. Brainets consistently performed at the same or higher levels than single rats in these tasks. Based on these findings, we propose that Brainets could be used to investigate animal social behaviors as well as a test bed for exploring the properties and potential applications of organic computers. PMID:26158615

A brain-spine interface alleviating gait deficits after spinal cord injury in primates.

PubMed

Capogrosso, Marco; Milekovic, Tomislav; Borton, David; Wagner, Fabien; Moraud, Eduardo Martin; Mignardot, Jean-Baptiste; Buse, Nicolas; Gandar, Jerome; Barraud, Quentin; Xing, David; Rey, Elodie; Duis, Simone; Jianzhong, Yang; Ko, Wai Kin D; Li, Qin; Detemple, Peter; Denison, Tim; Micera, Silvestro; Bezard, Erwan; Bloch, Jocelyne; Courtine, Grégoire

2016-11-10

Spinal cord injury disrupts the communication between the brain and the spinal circuits that orchestrate movement. To bypass the lesion, brain-computer interfaces have directly linked cortical activity to electrical stimulation of muscles, and have thus restored grasping abilities after hand paralysis. Theoretically, this strategy could also restore control over leg muscle activity for walking. However, replicating the complex sequence of individual muscle activation patterns underlying natural and adaptive locomotor movements poses formidable conceptual and technological challenges. Recently, it was shown in rats that epidural electrical stimulation of the lumbar spinal cord can reproduce the natural activation of synergistic muscle groups producing locomotion. Here we interface leg motor cortex activity with epidural electrical stimulation protocols to establish a brain-spine interface that alleviated gait deficits after a spinal cord injury in non-human primates. Rhesus monkeys (Macaca mulatta) were implanted with an intracortical microelectrode array in the leg area of the motor cortex and with a spinal cord stimulation system composed of a spatially selective epidural implant and a pulse generator with real-time triggering capabilities. We designed and implemented wireless control systems that linked online neural decoding of extension and flexion motor states with stimulation protocols promoting these movements. These systems allowed the monkeys to behave freely without any restrictions or constraining tethered electronics. After validation of the brain-spine interface in intact (uninjured) monkeys, we performed a unilateral corticospinal tract lesion at the thoracic level. As early as six days post-injury and without prior training of the monkeys, the brain-spine interface restored weight-bearing locomotion of the paralysed leg on a treadmill and overground. The implantable components integrated in the brain-spine interface have all been approved for investigational applications in similar human research, suggesting a practical translational pathway for proof-of-concept studies in people with spinal cord injury.

A subject-independent pattern-based Brain-Computer Interface

PubMed Central

Ray, Andreas M.; Sitaram, Ranganatha; Rana, Mohit; Pasqualotto, Emanuele; Buyukturkoglu, Korhan; Guan, Cuntai; Ang, Kai-Keng; Tejos, Cristián; Zamorano, Francisco; Aboitiz, Francisco; Birbaumer, Niels; Ruiz, Sergio

2015-01-01

While earlier Brain-Computer Interface (BCI) studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG) signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs) from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e., happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to “match” their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders. PMID:26539089

CAD/CAE Integration Enhanced by New CAD Services Standard

NASA Technical Reports Server (NTRS)

Claus, Russell W.

2002-01-01

A Government-industry team led by the NASA Glenn Research Center has developed a computer interface standard for accessing data from computer-aided design (CAD) systems. The Object Management Group, an international computer standards organization, has adopted this CAD services standard. The new standard allows software (e.g., computer-aided engineering (CAE) and computer-aided manufacturing software to access multiple CAD systems through one programming interface. The interface is built on top of a distributed computing system called the Common Object Request Broker Architecture (CORBA). CORBA allows the CAD services software to operate in a distributed, heterogeneous computing environment.

A comparison study of visually stimulated brain-computer and eye-tracking interfaces

NASA Astrophysics Data System (ADS)

Suefusa, Kaori; Tanaka, Toshihisa

2017-06-01

Objective. Brain-computer interfacing (BCI) based on visual stimuli detects the target on a screen on which a user is focusing. The detection of the gazing target can be achieved by tracking gaze positions with a video camera, which is called eye-tracking or eye-tracking interfaces (ETIs). The two types of interface have been developed in different communities. Thus, little work on a comprehensive comparison between these two types of interface has been reported. This paper quantitatively compares the performance of these two interfaces on the same experimental platform. Specifically, our study is focused on two major paradigms of BCI and ETI: steady-state visual evoked potential-based BCIs and dwelling-based ETIs. Approach. Recognition accuracy and the information transfer rate were measured by giving subjects the task of selecting one of four targets by gazing at it. The targets were displayed in three different sizes (with sides 20, 40 and 60 mm long) to evaluate performance with respect to the target size. Main results. The experimental results showed that the BCI was comparable to the ETI in terms of accuracy and the information transfer rate. In particular, when the size of a target was relatively small, the BCI had significantly better performance than the ETI. Significance. The results on which of the two interfaces works better in different situations would not only enable us to improve the design of the interfaces but would also allow for the appropriate choice of interface based on the situation. Specifically, one can choose an interface based on the size of the screen that displays the targets.

A model for the control mode man-computer interface dialogue

NASA Technical Reports Server (NTRS)

Chafin, R. L.

1981-01-01

A four stage model is presented for the control mode man-computer interface dialogue. It consists of context development, semantic development syntactic development, and command execution. Each stage is discussed in terms of the operator skill levels (naive, novice, competent, and expert) and pertinent human factors issues. These issues are human problem solving, human memory, and schemata. The execution stage is discussed in terms of the operators typing skills. This model provides an understanding of the human process in command mode activity for computer systems and a foundation for relating system characteristics to operator characteristics.

A novel graphical user interface for ultrasound-guided shoulder arthroscopic surgery

NASA Astrophysics Data System (ADS)

Tyryshkin, K.; Mousavi, P.; Beek, M.; Pichora, D.; Abolmaesumi, P.

2007-03-01

This paper presents a novel graphical user interface developed for a navigation system for ultrasound-guided computer-assisted shoulder arthroscopic surgery. The envisioned purpose of the interface is to assist the surgeon in determining the position and orientation of the arthroscopic camera and other surgical tools within the anatomy of the patient. The user interface features real time position tracking of the arthroscopic instruments with an optical tracking system, and visualization of their graphical representations relative to a three-dimensional shoulder surface model of the patient, created from computed tomography images. In addition, the developed graphical interface facilitates fast and user-friendly intra-operative calibration of the arthroscope and the arthroscopic burr, capture and segmentation of ultrasound images, and intra-operative registration. A pilot study simulating the computer-aided shoulder arthroscopic procedure on a shoulder phantom demonstrated the speed, efficiency and ease-of-use of the system.

Portable multiplicity counter

DOEpatents

Newell, Matthew R [Los Alamos, NM; Jones, David Carl [Los Alamos, NM

2009-09-01

A portable multiplicity counter has signal input circuitry, processing circuitry and a user/computer interface disposed in a housing. The processing circuitry, which can comprise a microcontroller integrated circuit operably coupled to shift register circuitry implemented in a field programmable gate array, is configured to be operable via the user/computer interface to count input signal pluses receivable at said signal input circuitry and record time correlations thereof in a total counting mode, coincidence counting mode and/or a multiplicity counting mode. The user/computer interface can be for example an LCD display/keypad and/or a USB interface. The counter can include a battery pack for powering the counter and low/high voltage power supplies for biasing external detectors so that the counter can be configured as a hand-held device for counting neutron events.

Software components for medical image visualization and surgical planning

NASA Astrophysics Data System (ADS)

Starreveld, Yves P.; Gobbi, David G.; Finnis, Kirk; Peters, Terence M.

2001-05-01

Purpose: The development of new applications in medical image visualization and surgical planning requires the completion of many common tasks such as image reading and re-sampling, segmentation, volume rendering, and surface display. Intra-operative use requires an interface to a tracking system and image registration, and the application requires basic, easy to understand user interface components. Rapid changes in computer and end-application hardware, as well as in operating systems and network environments make it desirable to have a hardware and operating system as an independent collection of reusable software components that can be assembled rapidly to prototype new applications. Methods: Using the OpenGL based Visualization Toolkit as a base, we have developed a set of components that implement the above mentioned tasks. The components are written in both C++ and Python, but all are accessible from Python, a byte compiled scripting language. The components have been used on the Red Hat Linux, Silicon Graphics Iris, Microsoft Windows, and Apple OS X platforms. Rigorous object-oriented software design methods have been applied to ensure hardware independence and a standard application programming interface (API). There are components to acquire, display, and register images from MRI, MRA, CT, Computed Rotational Angiography (CRA), Digital Subtraction Angiography (DSA), 2D and 3D ultrasound, video and physiological recordings. Interfaces to various tracking systems for intra-operative use have also been implemented. Results: The described components have been implemented and tested. To date they have been used to create image manipulation and viewing tools, a deep brain functional atlas, a 3D ultrasound acquisition and display platform, a prototype minimally invasive robotic coronary artery bypass graft planning system, a tracked neuro-endoscope guidance system and a frame-based stereotaxy neurosurgery planning tool. The frame-based stereotaxy module has been licensed and certified for use in a commercial image guidance system. Conclusions: It is feasible to encapsulate image manipulation and surgical guidance tasks in individual, reusable software modules. These modules allow for faster development of new applications. The strict application of object oriented software design methods allows individual components of such a system to make the transition from the research environment to a commercial one.

Development of a hybrid mental speller combining EEG-based brain-computer interface and webcam-based eye-tracking.

PubMed

Lee, Jun-Hak; Lim, Jeong-Hwan; Hwang, Han-Jeong; Im, Chang-Hwan

2013-01-01

The main goal of this study was to develop a hybrid mental spelling system combining a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) technology and a webcam-based eye-tracker, which utilizes information from the brain electrical activity and eye gaze direction at the same time. In the hybrid mental spelling system, a character decoded using SSVEP was not typed if the position of the selected character was not matched with the eye direction information ('left' or 'right') obtained from the eye-tracker. Thus, the users did not need to correct a misspelled character using a 'BACKSPACE' key. To verify the feasibility of the developed hybrid mental spelling system, we conducted online experiments with ten healthy participants. Each participant was asked to type 15 English words consisting of 68 characters. As a result, 16.6 typing errors could be prevented on average, demonstrating that the implemented hybrid mental spelling system could enhance the practicality of our mental spelling system.

P300 brain computer interface: current challenges and emerging trends

PubMed Central

Fazel-Rezai, Reza; Allison, Brendan Z.; Guger, Christoph; Sellers, Eric W.; Kleih, Sonja C.; Kübler, Andrea

2012-01-01

A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility. PMID:22822397

Motor imagery based brain-computer interfaces: An emerging technology to rehabilitate motor deficits.

PubMed

Alonso-Valerdi, Luz Maria; Salido-Ruiz, Ricardo Antonio; Ramirez-Mendoza, Ricardo A

2015-12-01

When the sensory-motor integration system is malfunctioning provokes a wide variety of neurological disorders, which in many cases cannot be treated with conventional medication, or via existing therapeutic technology. A brain-computer interface (BCI) is a tool that permits to reintegrate the sensory-motor loop, accessing directly to brain information. A potential, promising and quite investigated application of BCI has been in the motor rehabilitation field. It is well-known that motor deficits are the major disability wherewith the worldwide population lives. Therefore, this paper aims to specify the foundation of motor rehabilitation BCIs, as well as to review the recent research conducted so far (specifically, from 2007 to date), in order to evaluate the suitability and reliability of this technology. Although BCI for post-stroke rehabilitation is still in its infancy, the tendency is towards the development of implantable devices that encompass a BCI module plus a stimulation system. Copyright © 2015 Elsevier Ltd. All rights reserved.

BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.

PubMed

Zhao, Ming; Rattanatamrong, Prapaporn; DiGiovanna, Jack; Mahmoudi, Babak; Figueiredo, Renato J; Sanchez, Justin C; Príncipe, José C; Fortes, José A B

2008-01-01

Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.

Multi-channel linear descriptors for event-related EEG collected in brain computer interface.

PubMed

Pei, Xiao-mei; Zheng, Chong-xun; Xu, Jin; Bin, Guang-yu; Wang, Hong-wu

2006-03-01

By three multi-channel linear descriptors, i.e. spatial complexity (omega), field power (sigma) and frequency of field changes (phi), event-related EEG data within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the event-related EEG data indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the event-related paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing event-related EEG. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of EEG patterns in the application of brain computer interfaces.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Designing an operator interface? Consider user`s `psychology`

DOE Office of Scientific and Technical Information (OSTI.GOV)

Toffer, D.E.

The modern operator interface is a channel of communication between operators and the plant that, ideally, provides them with information necessary to keep the plant running at maximum efficiency. Advances in automation technology have increased information flow from the field to the screen. New and improved Supervisory Control and Data Acquisition (SCADA) packages provide designers with powerful and open design considerations. All too often, however, systems go to the field designed for the software rather than the operator. Plant operators` jobs have changed fundamentally, from controlling their plants from out in the field to doing so from within control rooms.more » Control room-based operation does not denote idleness. Trained operators should be engaged in examination of plant status and cognitive evaluation of plant efficiencies. Designers who are extremely computer literate, often do not consider demographics of field operators. Many field operators have little knowledge of modern computer systems. As a result, they do not take full advantage of the interface`s capabilities. Designers often fail to understand the true nature of how operators run their plants. To aid field operators, designers must provide familiar controls and intuitive choices. To achieve success in interface design, it is necessary to understand the ways in which humans think conceptually, and to understand how they process this information physically. The physical and the conceptual are closely related when working with any type of interface. Designers should ask themselves: {open_quotes}What type of information is useful to the field operator?{close_quotes} Let`s explore an integration model that contains the following key elements: (1) Easily navigated menus; (2) Reduced chances for misunderstanding; (3) Accurate representations of the plant or operation; (4) Consistent and predictable operation; (5) A pleasant and engaging interface that conforms to the operator`s expectations. 4 figs.« less

Removal of proprioception by BCI raises a stronger body ownership illusion in control of a humanlike robot.

PubMed

Alimardani, Maryam; Nishio, Shuichi; Ishiguro, Hiroshi

2016-09-22

Body ownership illusions provide evidence that our sense of self is not coherent and can be extended to non-body objects. Studying about these illusions gives us practical tools to understand the brain mechanisms that underlie body recognition and the experience of self. We previously introduced an illusion of body ownership transfer (BOT) for operators of a very humanlike robot. This sensation of owning the robot's body was confirmed when operators controlled the robot either by performing the desired motion with their body (motion-control) or by employing a brain-computer interface (BCI) that translated motor imagery commands to robot movement (BCI-control). The interesting observation during BCI-control was that the illusion could be induced even with a noticeable delay in the BCI system. Temporal discrepancy has always shown critical weakening effects on body ownership illusions. However the delay-robustness of BOT during BCI-control raised a question about the interaction between the proprioceptive inputs and delayed visual feedback in agency-driven illusions. In this work, we compared the intensity of BOT illusion for operators in two conditions; motion-control and BCI-control. Our results revealed a significantly stronger BOT illusion for the case of BCI-control. This finding highlights BCI's potential in inducing stronger agency-driven illusions by building a direct communication between the brain and controlled body, and therefore removing awareness from the subject's own body.

x-y-recording in transmission electron microscopy. A versatile and inexpensive interface to personal computers with application to stereology.

PubMed

Rickmann, M; Siklós, L; Joó, F; Wolff, J R

1990-09-01

An interface for IBM XT/AT-compatible computers is described which has been designed to read the actual specimen stage position of electron microscopes. The complete system consists of (i) optical incremental encoders attached to the x- and y-stage drivers of the microscope, (ii) two keypads for operator input, (iii) an interface card fitted to the bus of the personal computer, (iv) a standard configuration IBM XT (or compatible) personal computer optionally equipped with a (v) HP Graphic Language controllable colour plotter. The small size of the encoders and their connection to the stage drivers by simple ribbed belts allows an easy adaptation of the system to most electron microscopes. Operation of the interface card itself is supported by any high-level language available for personal computers. By the modular concept of these languages, the system can be customized to various applications, and no computer expertise is needed for actual operation. The present configuration offers an inexpensive attachment, which covers a wide range of applications from a simple notebook to high-resolution (200-nm) mapping of tissue. Since section coordinates can be processed in real-time, stereological estimations can be derived directly "on microscope". This is exemplified by an application in which particle numbers were determined by the disector method.

Brain-Computer Symbiosis

PubMed Central

Schalk, Gerwin

2009-01-01

The theoretical groundwork of the 1930’s and 1940’s and the technical advance of computers in the following decades provided the basis for dramatic increases in human efficiency. While computers continue to evolve, and we can still expect increasing benefits from their use, the interface between humans and computers has begun to present a serious impediment to full realization of the potential payoff. This article is about the theoretical and practical possibility that direct communication between the brain and the computer can be used to overcome this impediment by improving or augmenting conventional forms of human communication. It is about the opportunity that the limitations of our body’s input and output capacities can be overcome using direct interaction with the brain, and it discusses the assumptions, possible limitations, and implications of a technology that I anticipate will be a major source of pervasive changes in the coming decades. PMID:18310804

A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives

PubMed Central

Lee, Yushin; Yun, Myung Hwan

2017-01-01

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation. PMID:28453547

A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives.

PubMed

Choi, Inchul; Rhiu, Ilsun; Lee, Yushin; Yun, Myung Hwan; Nam, Chang S

2017-01-01

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation.

Operation of a P300-based brain-computer interface by individuals with cervical spinal cord injury.

PubMed

Ikegami, Shiro; Takano, Kouji; Saeki, Naokatsu; Kansaku, Kenji

2011-05-01

This study evaluates the efficacy of a P300-based brain-computer interface (BCI) with green/blue flicker matrices for individuals with cervical spinal cord injury (SCI). Ten individuals with cervical SCI (age 26-53, all male) and 10 age- and sex-matched able-bodied controls (age 27-52, all male) with no prior BCI experience were asked to input hiragana (Japanese alphabet) characters using the P300 BCI with two distinct types of visual stimuli, white/gray and green/blue, in an 8×10 flicker matrix. Both online and offline performance were evaluated. The mean online accuracy of the SCI subjects was 88.0% for the white/gray and 90.7% for the green/blue flicker matrices. The accuracy of the control subjects was 77.3% and 86.0% for the white/gray and green/blue, respectively. There was a significant difference in online accuracy between the two types of flicker matrix. SCI subjects performed with greater accuracy than controls, but the main effect was not significant. Individuals with cervical SCI successfully controlled the P300 BCI, and the green/blue flicker matrices were associated with significantly higher accuracy than the white/gray matrices. The P300 BCI with the green/blue flicker matrices is effective for use not only in able-bodied subjects, but also in individuals with cervical SCI. Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Behavioral rehabilitation of the eye closure reflex in senescent rats using a real-time biosignal acquisition system.

PubMed

Prueckl, R; Taub, A H; Herreros, I; Hogri, R; Magal, A; Bamford, S A; Giovannucci, A; Almog, R Ofek; Shacham-Diamand, Y; Verschure, P F M J; Mintz, M; Scharinger, J; Silmon, A; Guger, C

2011-01-01

In this paper the replacement of a lost learning function of rats through a computer-based real-time recording and feedback system is shown. In an experiment two recording electrodes and one stimulation electrode were implanted in an anesthetized rat. During a classical-conditioning paradigm, which includes tone and airpuff stimulation, biosignals were recorded and the stimulation events detected. A computational model of the cerebellum acquired the association between the stimuli and gave feedback to the brain of the rat using deep brain stimulation in order to close the eyelid of the rat. The study shows that replacement of a lost brain function using a direct bidirectional interface to the brain is realizable and can inspire future research for brain rehabilitation.

An SSVEP-Based Brain-Computer Interface for Text Spelling With Adaptive Queries That Maximize Information Gain Rates.

PubMed

Akce, Abdullah; Norton, James J S; Bretl, Timothy

2015-09-01

This paper presents a brain-computer interface for text entry using steady-state visually evoked potentials (SSVEP). Like other SSVEP-based spellers, ours identifies the desired input character by posing questions (or queries) to users through a visual interface. Each query defines a mapping from possible characters to steady-state stimuli. The user responds by attending to one of these stimuli. Unlike other SSVEP-based spellers, ours chooses from a much larger pool of possible queries-on the order of ten thousand instead of ten. The larger query pool allows our speller to adapt more effectively to the inherent structure of what is being typed and to the input performance of the user, both of which make certain queries provide more information than others. In particular, our speller chooses queries from this pool that maximize the amount of information to be received per unit of time, a measure of mutual information that we call information gain rate. To validate our interface, we compared it with two other state-of-the-art SSVEP-based spellers, which were re-implemented to use the same input mechanism. Results showed that our interface, with the larger query pool, allowed users to spell multiple-word texts nearly twice as fast as they could with the compared spellers.

Real-time Adaptive EEG Source Separation using Online Recursive Independent Component Analysis

PubMed Central

Hsu, Sheng-Hsiou; Mullen, Tim; Jung, Tzyy-Ping; Cauwenberghs, Gert

2016-01-01

Independent Component Analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: (a) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; (b) capability to detect and adapt to non-stationarity in 64-ch simulated EEG data; and (c) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces. PMID:26685257

BCILAB: a platform for brain-computer interface development

NASA Astrophysics Data System (ADS)

Kothe, Christian Andreas; Makeig, Scott

2013-10-01

Objective. The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography, functional near-infrared spectroscopy, etc., and to derive real-time estimates of user cognitive state, response, or intent for a variety of purposes: to restore communication by the severely disabled, to effect brain-actuated control and, more recently, to augment human-computer interaction. Continuing these advances, largely achieved through increases in computational power and methods, requires software tools to streamline the creation, testing, evaluation and deployment of new data analysis methods. Approach. Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for the development and testing of brain-computer interface (BCI) methods by providing an organized collection of over 100 pre-implemented methods and method variants, an easily extensible framework for the rapid prototyping of new methods, and a highly automated framework for systematic testing and evaluation of new implementations. Main results. To validate and illustrate the use of the framework, we present two sample analyses of publicly available data sets from recent BCI competitions and from a rapid serial visual presentation task. We demonstrate the straightforward use of BCILAB to obtain results compatible with the current BCI literature. Significance. The aim of the BCILAB toolbox is to provide the BCI community a powerful toolkit for methods research and evaluation, thereby helping to accelerate the pace of innovation in the field, while complementing the existing spectrum of tools for real-time BCI experimentation, deployment and use.

The Brain Computer Interface Future: Time for a Strategy

DTIC Science & Technology

2013-02-14

electrophysiological activity can be measured by electroencepholography ( EEG ), electrocorticography (ECoG), magnetoencephalography (MEG), or signal activity...magnetic resonance imaging (MRI) or near infrared spectroscopy. Currently EEG is most the most widely used BCI interface due to high temporal...resolution, less user risk, and lower costs.12 EEG technology has been widely available for many decades but has significantly expanded as researchers

NeuroMEMS: Neural Probe Microtechnologies

PubMed Central

HajjHassan, Mohamad; Chodavarapu, Vamsy; Musallam, Sam

2008-01-01

Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer's, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultra-long multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies. PMID:27873894

Programmable neural processing on a smartdust for brain-computer interfaces.

PubMed

Yuwen Sun; Shimeng Huang; Oresko, Joseph J; Cheng, Allen C

2010-10-01

Brain-computer interfaces (BCIs) offer tremendous promise for improving the quality of life for disabled individuals. BCIs use spike sorting to identify the source of each neural firing. To date, spike sorting has been performed by either using off-chip analysis, which requires a wired connection penetrating the skull to a bulky external power/processing unit, or via custom application-specific integrated circuits that lack the programmability to perform different algorithms and upgrades. In this research, we propose and test the feasibility of performing on-chip, real-time spike sorting on a programmable smartdust, including feature extraction, classification, compression, and wireless transmission. A detailed power/performance tradeoff analysis using DVFS is presented. Our experimental results show that the execution time and power density meet the requirements to perform real-time spike sorting and wireless transmission on a single neural channel.

A Prototype SSVEP Based Real Time BCI Gaming System

PubMed Central

Martišius, Ignas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel. PMID:27051414

Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface.

PubMed

Besio, Walter G; Cao, Hongbao; Zhou, Peng

2008-04-01

For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.

Bipolar electrode selection for a motor imagery based brain computer interface

NASA Astrophysics Data System (ADS)

Lou, Bin; Hong, Bo; Gao, Xiaorong; Gao, Shangkai

2008-09-01

A motor imagery based brain-computer interface (BCI) provides a non-muscular communication channel that enables people with paralysis to control external devices using their motor imagination. Reducing the number of electrodes is critical to improving the portability and practicability of the BCI system. A novel method is proposed to reduce the number of electrodes to a total of four by finding the optimal positions of two bipolar electrodes. Independent component analysis (ICA) is applied to find the source components of mu and alpha rhythms, and optimal electrodes are chosen by comparing the projection weights of sources on each channel. The results of eight subjects demonstrate the better classification performance of the optimal layout compared with traditional layouts, and the stability of this optimal layout over a one week interval was further verified.

The investigation of brain-computer interface for motor imagery and execution using functional near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Zhang, Zhen; Jiao, Xuejun; Xu, Fengang; Jiang, Jin; Yang, Hanjun; Cao, Yong; Fu, Jiahao

2017-01-01

Functional near-infrared spectroscopy (fNIRS), which can measure cortex hemoglobin activity, has been widely adopted in brain-computer interface (BCI). To explore the feasibility of recognizing motor imagery (MI) and motor execution (ME) in the same motion. We measured changes of oxygenated hemoglobin (HBO) and deoxygenated hemoglobin (HBR) on PFC and Motor Cortex (MC) when 15 subjects performing hand extension and finger tapping tasks. The mean, slope, quadratic coefficient and approximate entropy features were extracted from HBO as the input of support vector machine (SVM). For the four-class fNIRS-BCI classifiers, we realized 87.65% and 87.58% classification accuracy corresponding to hand extension and finger tapping tasks. In conclusion, it is effective for fNIRS-BCI to recognize MI and ME in the same motion.

A Prototype SSVEP Based Real Time BCI Gaming System.

PubMed

Martišius, Ignas; Damaševičius, Robertas

2016-01-01

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

Estimating the mutual information of an EEG-based Brain-Computer Interface.

PubMed

Schlögl, A; Neuper, C; Pfurtscheller, G

2002-01-01

An EEG-based Brain-Computer Interface (BCI) could be used as an additional communication channel between human thoughts and the environment. The efficacy of such a BCI depends mainly on the transmitted information rate. Shannon's communication theory was used to quantify the information rate of BCI data. For this purpose, experimental EEG data from four BCI experiments was analyzed off-line. Subjects imaginated left and right hand movements during EEG recording from the sensorimotor area. Adaptive autoregressive (AAR) parameters were used as features of single trial EEG and classified with linear discriminant analysis. The intra-trial variation as well as the inter-trial variability, the signal-to-noise ratio, the entropy of information, and the information rate were estimated. The entropy difference was used as a measure of the separability of two classes of EEG patterns.

Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.

PubMed

McFarland, Dennis J; Krusienski, Dean J; Wolpaw, Jonathan R

2006-01-01

The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata.

PubMed

Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

2017-11-08

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.

Initial constructs for patient-centered outcome measures to evaluate brain-computer interfaces.

PubMed

Andresen, Elena M; Fried-Oken, Melanie; Peters, Betts; Patrick, Donald L

2016-10-01

The authors describe preliminary work toward the creation of patient-centered outcome (PCO) measures to evaluate brain-computer interface (BCI) as an assistive technology (AT) for individuals with severe speech and physical impairments (SSPI). In Phase 1, 591 items from 15 existing measures were mapped to the International Classification of Functioning, Disability and Health (ICF). In Phase 2, qualitative interviews were conducted with eight people with SSPI and seven caregivers. Resulting text data were coded in an iterative analysis. Most items (79%) were mapped to the ICF environmental domain; over half (53%) were mapped to more than one domain. The ICF framework was well suited for mapping items related to body functions and structures, but less so for items in other areas, including personal factors. Two constructs emerged from qualitative data: quality of life (QOL) and AT. Component domains and themes were identified for each. Preliminary constructs, domains and themes were generated for future PCO measures relevant to BCI. Existing instruments are sufficient for initial items but do not adequately match the values of people with SSPI and their caregivers. Field methods for interviewing people with SSPI were successful, and support the inclusion of these individuals in PCO research. Implications for Rehabilitation Adapted interview methods allow people with severe speech and physical impairments to participate in patient-centered outcomes research. Patient-centered outcome measures are needed to evaluate the clinical implementation of brain-computer interface as an assistive technology.

TCP/IP Interface for the Satellite Orbit Analysis Program (SOAP)

NASA Technical Reports Server (NTRS)

Carnright, Robert; Stodden, David; Coggi, John

2009-01-01

The Transmission Control Protocol/ Internet protocol (TCP/IP) interface for the Satellite Orbit Analysis Program (SOAP) provides the means for the software to establish real-time interfaces with other software. Such interfaces can operate between two programs, either on the same computer or on different computers joined by a network. The SOAP TCP/IP module employs a client/server interface where SOAP is the server and other applications can be clients. Real-time interfaces between software offer a number of advantages over embedding all of the common functionality within a single program. One advantage is that they allow each program to divide the computation labor between processors or computers running the separate applications. Secondly, each program can be allowed to provide its own expertise domain with other programs able to use this expertise.

Human-Avatar Symbiosis for the Treatment of Auditory Verbal Hallucinations in Schizophrenia through Virtual/Augmented Reality and Brain-Computer Interfaces

PubMed Central

Fernández-Caballero, Antonio; Navarro, Elena; Fernández-Sotos, Patricia; González, Pascual; Ricarte, Jorge J.; Latorre, José M.; Rodriguez-Jimenez, Roberto

2017-01-01

This perspective paper faces the future of alternative treatments that take advantage of a social and cognitive approach with regards to pharmacological therapy of auditory verbal hallucinations (AVH) in patients with schizophrenia. AVH are the perception of voices in the absence of auditory stimulation and represents a severe mental health symptom. Virtual/augmented reality (VR/AR) and brain computer interfaces (BCI) are technologies that are growing more and more in different medical and psychological applications. Our position is that their combined use in computer-based therapies offers still unforeseen possibilities for the treatment of physical and mental disabilities. This is why, the paper expects that researchers and clinicians undergo a pathway toward human-avatar symbiosis for AVH by taking full advantage of new technologies. This outlook supposes to address challenging issues in the understanding of non-pharmacological treatment of schizophrenia-related disorders and the exploitation of VR/AR and BCI to achieve a real human-avatar symbiosis. PMID:29209193

A cell-phone-based brain-computer interface for communication in daily life

NASA Astrophysics Data System (ADS)

Wang, Yu-Te; Wang, Yijun; Jung, Tzyy-Ping

2011-04-01

Moving a brain-computer interface (BCI) system from a laboratory demonstration to real-life applications still poses severe challenges to the BCI community. This study aims to integrate a mobile and wireless electroencephalogram (EEG) system and a signal-processing platform based on a cell phone into a truly wearable and wireless online BCI. Its practicality and implications in a routine BCI are demonstrated through the realization and testing of a steady-state visual evoked potential (SSVEP)-based BCI. This study implemented and tested online signal processing methods in both time and frequency domains for detecting SSVEPs. The results of this study showed that the performance of the proposed cell-phone-based platform was comparable, in terms of the information transfer rate, with other BCI systems using bulky commercial EEG systems and personal computers. To the best of our knowledge, this study is the first to demonstrate a truly portable, cost-effective and miniature cell-phone-based platform for online BCIs.

Nonlinear dimensionality reduction of electroencephalogram (EEG) for Brain Computer interfaces.

PubMed

Teli, Mohammad Nayeem; Anderson, Charles

2009-01-01

Patterns in electroencephalogram (EEG) signals are analyzed for a Brain Computer Interface (BCI). An important aspect of this analysis is the work on transformations of high dimensional EEG data to low dimensional spaces in which we can classify the data according to mental tasks being performed. In this research we investigate how a Neural Network (NN) in an auto-encoder with bottleneck configuration can find such a transformation. We implemented two approximate second-order methods to optimize the weights of these networks, because the more common first-order methods are very slow to converge for networks like these with more than three layers of computational units. The resulting non-linear projections of time embedded EEG signals show interesting separations that are related to tasks. The bottleneck networks do indeed discover nonlinear transformations to low-dimensional spaces that capture much of the information present in EEG signals. However, the resulting low-dimensional representations do not improve classification rates beyond what is possible using Quadratic Discriminant Analysis (QDA) on the original time-lagged EEG.

Human-Avatar Symbiosis for the Treatment of Auditory Verbal Hallucinations in Schizophrenia through Virtual/Augmented Reality and Brain-Computer Interfaces.

PubMed

Fernández-Caballero, Antonio; Navarro, Elena; Fernández-Sotos, Patricia; González, Pascual; Ricarte, Jorge J; Latorre, José M; Rodriguez-Jimenez, Roberto

2017-01-01

This perspective paper faces the future of alternative treatments that take advantage of a social and cognitive approach with regards to pharmacological therapy of auditory verbal hallucinations (AVH) in patients with schizophrenia. AVH are the perception of voices in the absence of auditory stimulation and represents a severe mental health symptom. Virtual/augmented reality (VR/AR) and brain computer interfaces (BCI) are technologies that are growing more and more in different medical and psychological applications. Our position is that their combined use in computer-based therapies offers still unforeseen possibilities for the treatment of physical and mental disabilities. This is why, the paper expects that researchers and clinicians undergo a pathway toward human-avatar symbiosis for AVH by taking full advantage of new technologies. This outlook supposes to address challenging issues in the understanding of non-pharmacological treatment of schizophrenia-related disorders and the exploitation of VR/AR and BCI to achieve a real human-avatar symbiosis.

[A wireless smart home system based on brain-computer interface of steady state visual evoked potential].

PubMed

Zhao, Li; Xing, Xiao; Guo, Xuhong; Liu, Zehua; He, Yang

2014-10-01

Brain-computer interface (BCI) system is a system that achieves communication and control among humans and computers and other electronic equipment with the electroencephalogram (EEG) signals. This paper describes the working theory of the wireless smart home system based on the BCI technology. We started to get the steady-state visual evoked potential (SSVEP) using the single chip microcomputer and the visual stimulation which composed by LED lamp to stimulate human eyes. Then, through building the power spectral transformation on the LabVIEW platform, we processed timely those EEG signals under different frequency stimulation so as to transfer them to different instructions. Those instructions could be received by the wireless transceiver equipment to control the household appliances and to achieve the intelligent control towards the specified devices. The experimental results showed that the correct rate for the 10 subjects reached 100%, and the control time of average single device was 4 seconds, thus this design could totally achieve the original purpose of smart home system.

A cell-phone-based brain-computer interface for communication in daily life.

PubMed

Wang, Yu-Te; Wang, Yijun; Jung, Tzyy-Ping

2011-04-01

Moving a brain-computer interface (BCI) system from a laboratory demonstration to real-life applications still poses severe challenges to the BCI community. This study aims to integrate a mobile and wireless electroencephalogram (EEG) system and a signal-processing platform based on a cell phone into a truly wearable and wireless online BCI. Its practicality and implications in a routine BCI are demonstrated through the realization and testing of a steady-state visual evoked potential (SSVEP)-based BCI. This study implemented and tested online signal processing methods in both time and frequency domains for detecting SSVEPs. The results of this study showed that the performance of the proposed cell-phone-based platform was comparable, in terms of the information transfer rate, with other BCI systems using bulky commercial EEG systems and personal computers. To the best of our knowledge, this study is the first to demonstrate a truly portable, cost-effective and miniature cell-phone-based platform for online BCIs.

Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments

PubMed Central

Víctor Rodrigo, Mercado-García

2017-01-01

Tridimensional representations stimulate cognitive processes that are the core and foundation of human-computer interaction (HCI). Those cognitive processes take place while a user navigates and explores a virtual environment (VE) and are mainly related to spatial memory storage, attention, and perception. VEs have many distinctive features (e.g., involvement, immersion, and presence) that can significantly improve HCI in highly demanding and interactive systems such as brain-computer interfaces (BCI). BCI is as a nonmuscular communication channel that attempts to reestablish the interaction between an individual and his/her environment. Although BCI research started in the sixties, this technology is not efficient or reliable yet for everyone at any time. Over the past few years, researchers have argued that main BCI flaws could be associated with HCI issues. The evidence presented thus far shows that VEs can (1) set out working environmental conditions, (2) maximize the efficiency of BCI control panels, (3) implement navigation systems based not only on user intentions but also on user emotions, and (4) regulate user mental state to increase the differentiation between control and noncontrol modalities. PMID:29317861

Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

PubMed

Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

2015-11-01

One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Current trends in hardware and software for brain-computer interfaces (BCIs)

NASA Astrophysics Data System (ADS)

Brunner, P.; Bianchi, L.; Guger, C.; Cincotti, F.; Schalk, G.

2011-04-01

A brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the development of certification, dissemination and reimbursement procedures.

Brain-Computer Interface Spellers: A Review.

PubMed

Rezeika, Aya; Benda, Mihaly; Stawicki, Piotr; Gembler, Felix; Saboor, Abdul; Volosyak, Ivan

2018-03-30

A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

A comprehensive approach to decipher biological computation to achieve next generation high-performance exascale computing.

DOE Office of Scientific and Technical Information (OSTI.GOV)

James, Conrad D.; Schiess, Adrian B.; Howell, Jamie

2013-10-01

The human brain (volume=1200cm3) consumes 20W and is capable of performing > 10^16 operations/s. Current supercomputer technology has reached 1015 operations/s, yet it requires 1500m^3 and 3MW, giving the brain a 10^12 advantage in operations/s/W/cm^3. Thus, to reach exascale computation, two achievements are required: 1) improved understanding of computation in biological tissue, and 2) a paradigm shift towards neuromorphic computing where hardware circuits mimic properties of neural tissue. To address 1), we will interrogate corticostriatal networks in mouse brain tissue slices, specifically with regard to their frequency filtering capabilities as a function of input stimulus. To address 2), we willmore » instantiate biological computing characteristics such as multi-bit storage into hardware devices with future computational and memory applications. Resistive memory devices will be modeled, designed, and fabricated in the MESA facility in consultation with our internal and external collaborators.« less

Basic concepts and development of an all-purpose computer interface for ROC/FROC observer study.

PubMed

Shiraishi, Junji; Fukuoka, Daisuke; Hara, Takeshi; Abe, Hiroyuki

2013-01-01

In this study, we initially investigated various aspects of requirements for a computer interface employed in receiver operating characteristic (ROC) and free-response ROC (FROC) observer studies which involve digital images and ratings obtained by observers (radiologists). Secondly, by taking into account these aspects, an all-purpose computer interface utilized for these observer performance studies was developed. Basically, the observer studies can be classified into three paradigms, such as one rating for one case without an identification of a signal location, one rating for one case with an identification of a signal location, and multiple ratings for one case with identification of signal locations. For these paradigms, display modes on the computer interface can be used for single/multiple views of a static image, continuous viewing with cascade images (i.e., CT, MRI), and dynamic viewing of movies (i.e., DSA, ultrasound). Various functions on these display modes, which include windowing (contrast/level), magnifications, and annotations, are needed to be selected by an experimenter corresponding to the purpose of the research. In addition, the rules of judgment for distinguishing between true positives and false positives are an important factor for estimating diagnostic accuracy in an observer study. We developed a computer interface which runs on a Windows operating system by taking into account all aspects required for various observer studies. This computer interface requires experimenters to have sufficient knowledge about ROC/FROC observer studies, but allows its use for any purpose of the observer studies. This computer interface will be distributed publicly in the near future.

[Brain-Computer Interface: the First Clinical Experience in Russia].

PubMed

Mokienko, O A; Lyukmanov, R Kh; Chernikova, L A; Suponeva, N A; Piradov, M A; Frolov, A A

2016-01-01

Motor imagery is suggested to stimulate the same plastic mechanisms in the brain as a real movement. The brain-computer interface (BCI) controls motor imagery by converting EEG during this process into the commands for an external device. This article presents the results of two-stage study of the clinical use of non-invasive BCI in the rehabilitation of patients with severe hemiparesis caused by focal brain damage. It was found that the ability to control BCI did not depend on the duration of a disease, brain lesion localization and the degree of neurological deficit. The first step of the study involved 36 patients; it showed that the efficacy of rehabilitation was higher in the group with the use of BCI (the score on the Action Research Arm Test (ARAT) improved from 1 [0; 2] to 5 [0; 16] points, p = 0.012; no significant improvement was observed in control group). The second step of the study involved 19 patients; the complex BCI-exoskeleton (i.e. with the kinesthetic feedback) was used for motor imagery trainings. The improvement of the motor function of hands was proved by ARAT (the score improved from 2 [0; 37] to 4 [1; 45:5] points, p = 0.005) and Fugl-Meyer scale (from 72 [63; 110 ] to 79 [68; 115] points, p = 0.005).

Abnormal Neural Connectivity in Schizophrenia and fMRI-Brain-Computer Interface as a Potential Therapeutic Approach

PubMed Central

Ruiz, Sergio; Birbaumer, Niels; Sitaram, Ranganatha

2012-01-01

Considering that single locations of structural and functional abnormalities are insufficient to explain the diverse psychopathology of schizophrenia, new models have postulated that the impairments associated with the disease arise from a failure to integrate the activity of local and distributed neural circuits: the “abnormal neural connectivity hypothesis.” In the last years, new evidence coming from neuroimaging have supported and expanded this theory. However, despite the increasing evidence that schizophrenia is a disorder of neural connectivity, so far there are no treatments that have shown to produce a significant change in brain connectivity, or that have been specifically designed to alleviate this problem. Brain-Computer Interfaces based on real-time functional Magnetic Resonance Imaging (fMRI-BCI) are novel techniques that have allowed subjects to achieve self-regulation of circumscribed brain regions. In recent studies, experiments with this technology have resulted in new findings suggesting that this methodology could be used to train subjects to enhance brain connectivity, and therefore could potentially be used as a therapeutic tool in mental disorders including schizophrenia. The present article summarizes the findings coming from hemodynamics-based neuroimaging that support the abnormal connectivity hypothesis in schizophrenia, and discusses a new approach that could address this problem. PMID:23525496

Brain-Computer Interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients?

PubMed Central

Kübler, A.; Birbaumer, N.

2008-01-01

Objective To investigate the relationship between physical impairment and brain-computer interface (BCI) performance. Method We present a meta-analysis of 29 patients with amyotrophic lateral sclerosis and 6 with other severe neurological diseases in different stages of physical impairment who were trained with a BCI. In most cases voluntary regulation of slow cortical potentials has been used as input signal for BCI control. More recently sensorimotor rhythms and the P300 event-related brain potential were recorded. Results A strong correlation has been found between physical impairment and BCI performance, indicating that performance worsens as impairment increases. Seven patients were in the complete locked-in state (CLIS) with no communication possible. After removal of these patients from the analysis, the relationship between physical impairment and BCI performance disappeared. The lack of a relation between physical impairment and BCI performance was confirmed when adding BCI data of patients from other BCI research groups. Conclusions Basic communication (yes/no) was not restored in any of the CLIS patients with a BCI. Whether locked-in patients can transfer learned brain control to the CLIS remains an open empirical question. Significance Voluntary brain regulation for communication is possible in all stages of paralysis except the CLIS. PMID:18824406

Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report.

PubMed

Broetz, Doris; Braun, Christoph; Weber, Cornelia; Soekadar, Surjo R; Caria, Andrea; Birbaumer, Niels

2010-09-01

There is no accepted and efficient rehabilitation strategy to reduce focal impairments for patients with chronic stroke who lack residual movements. A 67-year-old hemiplegic patient with no active finger extension was trained with a brain-computer interface (BCI) combined with a specific daily life-oriented physiotherapy. The BCI used electrical brain activity (EEG) and magnetic brain activity (MEG) to drive an orthosis and a robot affixed to the patient's affected upper extremity, which enabled him to move the paralyzed arm and hand driven by voluntary modulation of micro-rhythm activity. In addition, the patient practiced goal-directed physiotherapy training. Over 1 year, he completed 3 training blocks. Arm motor function, gait capacities (using Fugl-Meyer Assessment, Wolf Motor Function Test, Modified Ashworth Scale, 10-m walk speed, and goal attainment score), and brain reorganization (functional MRI, MEG) were repeatedly assessed. The ability of hand and arm movements as well as speed and safety of gait improved significantly (mean 46.6%). Improvement of motor function was associated with increased micro-oscillations in the ipsilesional motor cortex. This proof-of-principle study suggests that the combination of BCI training with goal-directed, active physical therapy may improve the motor abilities of chronic stroke patients despite apparent initial paralysis.

Physiological properties of brain-machine interface input signals.

PubMed

Slutzky, Marc W; Flint, Robert D

2017-08-01

Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance-including movement-related information, longevity, and stability-of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability. Copyright © 2017 the American Physiological Society.

Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

PubMed

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Effects of Soft Drinks on Resting State EEG and Brain-Computer Interface Performance.

PubMed

Meng, Jianjun; Mundahl, John; Streitz, Taylor; Maile, Kaitlin; Gulachek, Nicholas; He, Jeffrey; He, Bin

2017-01-01

Motor imagery-based (MI based) brain-computer interface (BCI) using electroencephalography (EEG) allows users to directly control a computer or external device by modulating and decoding the brain waves. A variety of factors could potentially affect the performance of BCI such as the health status of subjects or the environment. In this study, we investigated the effects of soft drinks and regular coffee on EEG signals under resting state and on the performance of MI based BCI. Twenty-six healthy human subjects participated in three or four BCI sessions with a resting period in each session. During each session, the subjects drank an unlabeled soft drink with either sugar (Caffeine Free Coca-Cola), caffeine (Diet Coke), neither ingredient (Caffeine Free Diet Coke), or a regular coffee if there was a fourth session. The resting state spectral power in each condition was compared; the analysis showed that power in alpha and beta band after caffeine consumption were decreased substantially compared to control and sugar condition. Although the attenuation of powers in the frequency range used for the online BCI control signal was shown, group averaged BCI online performance after consuming caffeine was similar to those of other conditions. This work, for the first time, shows the effect of caffeine, sugar intake on the online BCI performance and resting state brain signal.

Automated selection of brain regions for real-time fMRI brain-computer interfaces

NASA Astrophysics Data System (ADS)

Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

2017-02-01

Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces.

PubMed

Grissmann, Sebastian; Zander, Thorsten O; Faller, Josef; Brönstrup, Jonas; Kelava, Augustin; Gramann, Klaus; Gerjets, Peter

2017-01-01

Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios.

Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces

PubMed Central

Grissmann, Sebastian; Zander, Thorsten O.; Faller, Josef; Brönstrup, Jonas; Kelava, Augustin; Gramann, Klaus; Gerjets, Peter

2017-01-01

Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios. PMID:28769776

Hand-in-hand advances in biomedical engineering and sensorimotor restoration.

PubMed

Pisotta, Iolanda; Perruchoud, David; Ionta, Silvio

2015-05-15

Living in a multisensory world entails the continuous sensory processing of environmental information in order to enact appropriate motor routines. The interaction between our body and our brain is the crucial factor for achieving such sensorimotor integration ability. Several clinical conditions dramatically affect the constant body-brain exchange, but the latest developments in biomedical engineering provide promising solutions for overcoming this communication breakdown. The ultimate technological developments succeeded in transforming neuronal electrical activity into computational input for robotic devices, giving birth to the era of the so-called brain-machine interfaces. Combining rehabilitation robotics and experimental neuroscience the rise of brain-machine interfaces into clinical protocols provided the technological solution for bypassing the neural disconnection and restore sensorimotor function. Based on these advances, the recovery of sensorimotor functionality is progressively becoming a concrete reality. However, despite the success of several recent techniques, some open issues still need to be addressed. Typical interventions for sensorimotor deficits include pharmaceutical treatments and manual/robotic assistance in passive movements. These procedures achieve symptoms relief but their applicability to more severe disconnection pathologies is limited (e.g. spinal cord injury or amputation). Here we review how state-of-the-art solutions in biomedical engineering are continuously increasing expectances in sensorimotor rehabilitation, as well as the current challenges especially with regards to the translation of the signals from brain-machine interfaces into sensory feedback and the incorporation of brain-machine interfaces into daily activities. Copyright © 2015 Elsevier B.V. All rights reserved.

A primer on brain-machine interfaces, concepts, and technology: a key element in the future of functional neurorestoration.

PubMed

Lee, Brian; Liu, Charles Y; Apuzzo, Michael L J

2013-01-01

Conventionally, the practice of neurosurgery has been characterized by the removal of pathology, congenital or acquired. The emerging complement to the removal of pathology is surgery for the specific purpose of restoration of function. Advents in neuroscience, technology, and the understanding of neural circuitry are creating opportunities to intervene in disease processes in a reparative manner, thereby advancing toward the long-sought-after concept of neurorestoration. Approaching the issue of neurorestoration from a biomedical engineering perspective is the rapidly growing arena of implantable devices. Implantable devices are becoming more common in medicine and are making significant advancements to improve a patient's functional outcome. Devices such as deep brain stimulators, vagus nerve stimulators, and spinal cord stimulators are now becoming more commonplace in neurosurgery as we utilize our understanding of the nervous system to interpret neural activity and restore function. One of the most exciting prospects in neurosurgery is the technologically driven field of brain-machine interface, also known as brain-computer interface, or neuroprosthetics. The successful development of this technology will have far-reaching implications for patients suffering from a great number of diseases, including but not limited to spinal cord injury, paralysis, stroke, or loss of limb. This article provides an overview of the issues related to neurorestoration using implantable devices with a specific focus on brain-machine interface technology. Copyright © 2013 Elsevier Inc. All rights reserved.

An Object-Oriented Graphical User Interface for a Reusable Rocket Engine Intelligent Control System

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.; Musgrave, Jeffrey L.; Guo, Ten-Huei; Paxson, Daniel E.; Wong, Edmond; Saus, Joseph R.; Merrill, Walter C.

1994-01-01

An intelligent control system for reusable rocket engines under development at NASA Lewis Research Center requires a graphical user interface to allow observation of the closed-loop system in operation. The simulation testbed consists of a real-time engine simulation computer, a controls computer, and several auxiliary computers for diagnostics and coordination. The system is set up so that the simulation computer could be replaced by the real engine and the change would be transparent to the control system. Because of the hard real-time requirement of the control computer, putting a graphical user interface on it was not an option. Thus, a separate computer used strictly for the graphical user interface was warranted. An object-oriented LISP-based graphical user interface has been developed on a Texas Instruments Explorer 2+ to indicate the condition of the engine to the observer through plots, animation, interactive graphics, and text.

Computer-aided placement of deep brain stimulators: from planning to intraoperative guidance

NASA Astrophysics Data System (ADS)

D'Haese, Pierre-Francois; Pallavaram, Srivatsan; Kao, Chris; Konrad, Peter E.; Dawant, Benoit M.

2005-04-01

The long term objective of our research is to develop a system that will automate as much as possible DBS implantation procedures. It is estimated that about 180,000 patients/year would benefit from DBS implantation. Yet, only 3000 procedures are performed annually. This is so because the combined expertise required to perform the procedure successfully is only available at a limited number of sites. Our goal is to transform this procedure into a procedure that can be performed by a general neurosurgeon at a community hospital. In this work we report on our current progress toward developing a system for the computer-assisted pre-operative selection of target points and for the intra-operative adjustment of these points. The system consists of a deformable atlas of optimal target points that can be used to select automatically the pre-operative target, of an electrophysiological atlas, and of an intra-operative interface. The atlas is deformed using a rigid then a non-rigid registration algorithm developed at our institution. Results we have obtained show that automatic prediction of target points is an achievable goal. Our results also indicate that electrophysiological information can be used to resolve structures not visible in anatomic images, thus improving both pre-operative and intra-operative guidance. Our intra-operative system has reached the stage of a working prototype that is clinically used at our institution.

An approach to a real-time distribution system

NASA Technical Reports Server (NTRS)

Kittle, Frank P., Jr.; Paddock, Eddie J.; Pocklington, Tony; Wang, Lui

1990-01-01

The requirements of a real-time data distribution system are to provide fast, reliable delivery of data from source to destination with little or no impact to the data source. In this particular case, the data sources are inside an operational environment, the Mission Control Center (MCC), and any workstation receiving data directly from the operational computer must conform to the software standards of the MCC. In order to supply data to development workstations outside of the MCC, it is necessary to use gateway computers that prevent unauthorized data transfer back to the operational computers. Many software programs produced on the development workstations are targeted for real-time operation. Therefore, these programs must migrate from the development workstation to the operational workstation. It is yet another requirement for the Data Distribution System to ensure smooth transition of the data interfaces for the application developers. A standard data interface model has already been set up for the operational environment, so the interface between the distribution system and the application software was developed to match that model as closely as possible. The system as a whole therefore allows the rapid development of real-time applications without impacting the data sources. In summary, this approach to a real-time data distribution system provides development users outside of the MCC with an interface to MCC real-time data sources. In addition, the data interface was developed with a flexible and portable software design. This design allows for the smooth transition of new real-time applications to the MCC operational environment.

Use of parallel computing for analyzing big data in EEG studies of ambiguous perception

NASA Astrophysics Data System (ADS)

Maksimenko, Vladimir A.; Grubov, Vadim V.; Kirsanov, Daniil V.

2018-02-01

Problem of interaction between human and machine systems through the neuro-interfaces (or brain-computer interfaces) is an urgent task which requires analysis of large amount of neurophysiological EEG data. In present paper we consider the methods of parallel computing as one of the most powerful tools for processing experimental data in real-time with respect to multichannel structure of EEG. In this context we demonstrate the application of parallel computing for the estimation of the spectral properties of multichannel EEG signals, associated with the visual perception. Using CUDA C library we run wavelet-based algorithm on GPUs and show possibility for detection of specific patterns in multichannel set of EEG data in real-time.

Standard interface: Twin-coaxial converter

NASA Technical Reports Server (NTRS)

Lushbaugh, W. A.

1976-01-01

The network operations control center standard interface has been adopted as a standard computer interface for all future minicomputer based subsystem development for the Deep Space Network. Discussed is an intercomputer communications link using a pair of coaxial cables. This unit is capable of transmitting and receiving digital information at distances up to 600 m with complete ground isolation between the communicating devices. A converter is described that allows a computer equipped with the standard interface to use the twin coaxial link.

Papers from the Fifth International Brain-Computer Interface Meeting

NASA Astrophysics Data System (ADS)

Huggins, Jane E.; Wolpaw, Jonathan R.

2014-06-01

Brain-computer interfaces (BCIs), also known as brain-machine interfaces (BMIs), translate brain activity into new outputs that replace, restore, enhance, supplement or improve natural brain outputs. BCI research and development has grown rapidly for the past two decades. It is beginning to provide useful communication and control capacities to people with severe neuromuscular disabilities; and it is expanding into new areas such as neurorehabilitation that may greatly increase its clinical impact. At the same time, significant challenges remain, particularly in regard to translating laboratory advances into clinical use. The papers in this special section report some of the work presented at the Fifth International BCI Meeting held on 3-7 June 2013 at the Asilomar Conference Center in Pacific Grove, California, USA. Like its predecessors over the past 15 years, this meeting was supported by the National Institutes of Health, the National Science Foundation, and a variety of other governmental and private sponsors [1]. This fifth meeting was organized and managed by a program committee of BCI researchers from throughout the world [2]. It retained the distinctive retreat-style format developed by the Wadsworth Center researchers who organized and managed the first four meetings. The 301 attendees came from 165 research groups in 29 countries; 37% were students or postdoctoral fellows. Of more than 200 extended abstracts submitted for peer review, 25 were selected for oral presentation [3], and 181 were presented as posters [4] and published in the open-access conference proceedings [5]. The meeting featured 19 highly interactive workshops [6] covering the broad spectrum of BCI research and development, as well as many demonstrations of BCI systems and associated technology. Like the first four meetings, this one included attendees and embraced topics from across the broad spectrum of disciplines essential to effective BCI research and development, including neuroscience, engineering, applied mathematics, computer science, psychology and rehabilitation. In addition, this fifth meeting extended the spectrum in two very important ways. For the first time, presentations were given by several people who could potentially benefit from current BCI technology-people with severe disabilities who need assistive technology for communication. One presented in person and one remotely. A Virtual BCI User's Forum allowed these presenters and other potential BCI users to speak directly to the BCI research community about the advantages and disadvantages of current BCIs and important directions for future study (see [7]). Their personal experiences and desires can help guide BCI research and development. Their active participation, particularly in regard to the selection of goals and the evaluation and optimization of new methods and systems, is essential if BCIs are to become clinically valuable and widely used technology. The second major innovation in this meeting was the strong emphasis on ethical issues related to BCI development and use. The meeting opened with a keynote presentation entitled 'Neuroethics, BCIs and the Cyborg Myth' by Dr Joseph Fins, a noted authority on neuroethics from the Weill Cornell Medical College and the Rockefeller University. He focused on the ability of BCIs to relieve suffering and restore function, while cautioning against applications that take intentional control away from the user. Ethical issues were also addressed in several of the workshops, and arose on multiple occasions and in multiple contexts over the course of the meeting. Their prominence reflected the growing importance and difficulty of ethical issues as BCI capacities and applications grow and extend to potentially enhancing or supplementing normal nervous system function. The 16 articles in this special section reflect the breadth, depth, growing maturity and future directions of BCI research. The first paper presents a tutorial on best practices in BCI performance measurement [8]. The following eight papers focus on specific BCI applications and on methods for increasing their usefulness for people with severe disabilities. The next two examine how brain activity and BCI use affect each other. The final five studies investigate brain signals and evaluate new signal processing algorithms in order to improve BCI performance and broaden its possible applications in some of the newest areas of BCI research, including the direct interpretation of speech from electrocorticographic (ECoG) activity [9]. Together, these papers span many aspects of BCI research, including different recording modalities (i.e. electroencephalogram (EEG), ECoG, functional magnetic resonance imaging (fMRI)) and signal types (e.g. P300 event-related potentials (ERPs), sensorimotor rhythms, steady-state visual evoked potentials (SSVEPs)). Furthermore, additional clinically related studies that were presented at the meeting but were considered to be outside the scope of the Journal of Neural Engineering will appear in a special issue of the Archives of Physical Medicine and Rehabilitation . With a theme of 'Defining the Future' the Fifth International BCI Meeting tackled the issues of a rapidly growing multidisciplinary research and development enterprise that is now entering clinical use. Important new areas that received attention included the need for active involvement of the people with severe disabilities who are the primary initial users of BCI technology and the growing importance and difficulty of the multiple ethical questions raised by BCIs and their potential applications. The meeting also marked the launching of the new journal Brain--Computer Interfaces , dedicated to BCI research and development, and initiated the establishment of the Brain--Computer Interface Society, which will organize and manage the Sixth International BCI Meeting to be held in 2016. References [1] http://bcimeeting.org/2013/sponsors.html [2] http://bcimeeting.org/2013/meetinginfo.html [3] http://bcimeeting.org/2013/researchsessions.html (indexes individual abstracts) [4] http://bcimeeting.org/2013/posters.html (indexes individual abstracts) [5] http://castor.tugraz.at/doku/BCIMeeting2013/BCIMeeting2013_all.pdf [6] Huggins J E et al 2014 Workshops of the Fifth International Brain--Computer Interface Meeting: Defining the Future Brain--Computer Interface J. 1 27-49 [7] Peters B, Bieker G, Heckman S M, Huggins J E, Wolf C, Zeitlin D and Fried-Oken M 2014 Brain--computer interface users speak up: the Virtual Users' Forum at the 2013 International BCI Meeting Archives of Physical Medicine and Rehabilitation vol 95 fall supplement at press [8] Thompson D E et al 2014 Performance measurement for brain-computer or brain-machine interfaces: a tutorial J. Neural Eng. 11 035001 [9] Mugler E, Patton J, Flint R, Wright Z, Schuele S, Rosenow J, Shih J, Krusienski D and Slutzky M 2014 Direct classification of all American English phonemes using signals from functional speech motor cortex J. Neural Eng. 11 035015

Hands-Free, Heads-Up Control System for Unmanned Ground Vehicles

DTIC Science & Technology

2011-08-10

interface evaluation Industry evaluated two commercial-off-the-shelf (COTS) brain computer interfaces from two companies – Neurosky and Emotiv ...useless, resulting in very low command recognition accuracy. In addition, latency issues plagued the system. Figure 6 Emotiv Headset The... Emotiv system, unlike the Neurosky, required great effort to use and calibrate. It requires 16 foam tips to be wet with saline solution and then

Using Brain–Computer Interfaces and Brain-State Dependent Stimulation as Tools in Cognitive Neuroscience

PubMed Central

Jensen, Ole; Bahramisharif, Ali; Oostenveld, Robert; Klanke, Stefan; Hadjipapas, Avgis; Okazaki, Yuka O.; van Gerven, Marcel A. J.

2011-01-01

Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain–computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain-state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real-time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work. PMID:21687463

A Symbiotic Brain-Machine Interface through Value-Based Decision Making

PubMed Central

Mahmoudi, Babak; Sanchez, Justin C.

2011-01-01

Background In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC). Methodology The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc. Conclusions Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain. PMID:21423797

Normalization as a canonical neural computation

PubMed Central

Carandini, Matteo; Heeger, David J.

2012-01-01

There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation. PMID:22108672

An auditory brain-computer interface evoked by natural speech

NASA Astrophysics Data System (ADS)

Lopez-Gordo, M. A.; Fernandez, E.; Romero, S.; Pelayo, F.; Prieto, Alberto

2012-06-01

Brain-computer interfaces (BCIs) are mainly intended for people unable to perform any muscular movement, such as patients in a complete locked-in state. The majority of BCIs interact visually with the user, either in the form of stimulation or biofeedback. However, visual BCIs challenge their ultimate use because they require the subjects to gaze, explore and shift eye-gaze using their muscles, thus excluding patients in a complete locked-in state or under the condition of the unresponsive wakefulness syndrome. In this study, we present a novel fully auditory EEG-BCI based on a dichotic listening paradigm using human voice for stimulation. This interface has been evaluated with healthy volunteers, achieving an average information transmission rate of 1.5 bits min-1 in full-length trials and 2.7 bits min-1 using the optimal length of trials, recorded with only one channel and without formal training. This novel technique opens the door to a more natural communication with users unable to use visual BCIs, with promising results in terms of performance, usability, training and cognitive effort.

Technology transfer of operator-in-the-loop simulation

NASA Technical Reports Server (NTRS)

Yae, K. H.; Lin, H. C.; Lin, T. C.; Frisch, H. P.

1994-01-01

The technology developed for operator-in-the-loop simulation in space teleoperation has been applied to Caterpillar's backhoe, wheel loader, and off-highway truck. On an SGI workstation, the simulation integrates computer modeling of kinematics and dynamics, real-time computational and visualization, and an interface with the operator through the operator's console. The console is interfaced with the workstation through an IBM-PC in which the operator's commands were digitized and sent through an RS-232 serial port. The simulation gave visual feedback adequate for the operator in the loop, with the camera's field of vision projected on a large screen in multiple view windows. The view control can emulate either stationary or moving cameras. This simulator created an innovative engineering design environment by integrating computer software and hardware with the human operator's interactions. The backhoe simulation has been adopted by Caterpillar in building a virtual reality tool for backhoe design.

Electrostatics with Computer-Interfaced Charge Sensors

ERIC Educational Resources Information Center

Morse, Robert A.

2006-01-01

Computer interfaced electrostatic charge sensors allow both qualitative and quantitative measurements of electrostatic charge but are quite sensitive to charges accumulating on modern synthetic materials. They need to be used with care so that students can correctly interpret their measurements. This paper describes the operation of the sensors,…

Whole brain analysis of postmortem density changes of grey and white matter on computed tomography by statistical parametric mapping.

PubMed

Nishiyama, Yuichi; Kanayama, Hidekazu; Mori, Hiroshi; Tada, Keiji; Yamamoto, Yasushi; Katsube, Takashi; Takeshita, Haruo; Kawakami, Kazunori; Kitagaki, Hajime

2017-06-01

This study examined the usefulness of statistical parametric mapping (SPM) for investigating postmortem changes on brain computed tomography (CT). This retrospective study included 128 patients (23 - 100 years old) without cerebral abnormalities who underwent unenhanced brain CT before and after death. The antemortem CT (AMCT) scans and postmortem CT (PMCT) scans were spatially normalized using our original brain CT template, and postmortem changes of CT values (in Hounsfield units; HU) were analysed by the SPM technique. Compared with AMCT scans, 58.6 % and 98.4 % of PMCT scans showed loss of the cerebral sulci and an unclear grey matter (GM)-white matter (WM) interface, respectively. SPM analysis revealed a significant decrease in cortical GM density within 70 min after death on PMCT scans, suggesting cytotoxic brain oedema. Furthermore, there was a significant increase in the density of the WM, lenticular nucleus and thalamus more than 120 min after death. The SPM technique demonstrated typical postmortem changes on brain CT scans, and revealed that the unclear GM-WM interface on early PMCT scans is caused by a rapid decrease in cortical GM density combined with a delayed increase in WM density. SPM may be useful for assessment of whole brain postmortem changes. • The original brain CT template achieved successful normalization of brain morphology. • Postmortem changes in the brain were independent of sex. • Cortical GM density decreased rapidly after death. • WM and deep GM densities increased following cortical GM density change. • SPM could be useful for assessment of whole brain postmortem changes.

Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface.

PubMed

Young, Brittany M; Nigogosyan, Zack; Walton, Léo M; Song, Jie; Nair, Veena A; Grogan, Scott W; Tyler, Mitchell E; Edwards, Dorothy F; Caldera, Kristin; Sattin, Justin A; Williams, Justin C; Prabhakaran, Vivek

2014-01-01

This study aims to examine the changes in task-related brain activity induced by rehabilitative therapy using brain-computer interface (BCI) technologies and whether these changes are relevant to functional gains achieved through the use of these therapies. Stroke patients with persistent upper-extremity motor deficits received interventional rehabilitation therapy using a closed-loop neurofeedback BCI device (n = 8) or no therapy (n = 6). Behavioral assessments using the Stroke Impact Scale, the Action Research Arm Test (ARAT), and the Nine-Hole Peg Test (9-HPT) as well as task-based fMRI scans were conducted before, during, after, and 1 month after therapy administration or at analogous intervals in the absence of therapy. Laterality Index (LI) values during finger tapping of each hand were calculated for each time point and assessed for correlation with behavioral outcomes. Brain activity during finger tapping of each hand shifted over the course of BCI therapy, but not in the absence of therapy, to greater involvement of the non-lesioned hemisphere (and lesser involvement of the stroke-lesioned hemisphere) as measured by LI. Moreover, changes from baseline LI values during finger tapping of the impaired hand were correlated with gains in both objective and subjective behavioral measures. These findings suggest that the administration of interventional BCI therapy can induce differential changes in brain activity patterns between the lesioned and non-lesioned hemispheres and that these brain changes are associated with changes in specific motor functions.

Adaptive voting computer system

NASA Technical Reports Server (NTRS)

Koczela, L. J.; Wilgus, D. S. (Inventor)

1974-01-01

A computer system is reported that uses adaptive voting to tolerate failures and operates in a fail-operational, fail-safe manner. Each of four computers is individually connected to one of four external input/output (I/O) busses which interface with external subsystems. Each computer is connected to receive input data and commands from the other three computers and to furnish output data commands to the other three computers. An adaptive control apparatus including a voter-comparator-switch (VCS) is provided for each computer to receive signals from each of the computers and permits adaptive voting among the computers to permit the fail-operational, fail-safe operation.

Flashing characters with famous faces improves ERP-based brain-computer interface performance

NASA Astrophysics Data System (ADS)

Kaufmann, T.; Schulz, S. M.; Grünzinger, C.; Kübler, A.

2011-10-01

Currently, the event-related potential (ERP)-based spelling device, often referred to as P300-Speller, is the most commonly used brain-computer interface (BCI) for enhancing communication of patients with impaired speech or motor function. Among numerous improvements, a most central feature has received little attention, namely optimizing the stimulus used for eliciting ERPs. Therefore we compared P300-Speller performance with the standard stimulus (flashing characters) against performance with stimuli known for eliciting particularly strong ERPs due to their psychological salience, i.e. flashing familiar faces transparently superimposed on characters. Our results not only indicate remarkably increased ERPs in response to familiar faces but also improved P300-Speller performance due to a significant reduction of stimulus sequences needed for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-Speller.

Ethics in published brain-computer interface research

NASA Astrophysics Data System (ADS)

Specker Sullivan, L.; Illes, J.

2018-02-01

Objective. Sophisticated signal processing has opened the doors to more research with human subjects than ever before. The increase in the use of human subjects in research comes with a need for increased human subjects protections. Approach. We quantified the presence or absence of ethics language in published reports of brain-computer interface (BCI) studies that involved human subjects and qualitatively characterized ethics statements. Main results. Reports of BCI studies with human subjects that are published in neural engineering and engineering journals are anchored in the rationale of technological improvement. Ethics language is markedly absent, omitted from 31% of studies published in neural engineering journals and 59% of studies in biomedical engineering journals. Significance. As the integration of technological tools with the capacities of the mind deepens, explicit attention to ethical issues will ensure that broad human benefit is embraced and not eclipsed by technological exclusiveness.

Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface

PubMed Central

Blakely, Tim M.; Miller, Kai J.; Rao, Rajesh P. N.; Ojemann, Jeffrey G.

2014-01-01

Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75–200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report 3 characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms. PMID:25599079

A novel brain-computer interface based on the rapid serial visual presentation paradigm.

PubMed

Acqualagna, Laura; Treder, Matthias Sebastian; Schreuder, Martijn; Blankertz, Benjamin

2010-01-01

Most present-day visual brain computer interfaces (BCIs) suffer from the fact that they rely on eye movements, are slow-paced, or feature a small vocabulary. As a potential remedy, we explored a novel BCI paradigm consisting of a central rapid serial visual presentation (RSVP) of the stimuli. It has a large vocabulary and realizes a BCI system based on covert non-spatial selective visual attention. In an offline study, eight participants were presented sequences of rapid bursts of symbols. Two different speeds and two different color conditions were investigated. Robust early visual and P300 components were elicited time-locked to the presentation of the target. Offline classification revealed a mean accuracy of up to 90% for selecting the correct symbol out of 30 possibilities. The results suggest that RSVP-BCI is a promising new paradigm, also for patients with oculomotor impairments.

DARPA-funded efforts in the development of novel brain-computer interface technologies.

PubMed

Miranda, Robbin A; Casebeer, William D; Hein, Amy M; Judy, Jack W; Krotkov, Eric P; Laabs, Tracy L; Manzo, Justin E; Pankratz, Kent G; Pratt, Gill A; Sanchez, Justin C; Weber, Douglas J; Wheeler, Tracey L; Ling, Geoffrey S F

2015-04-15

The Defense Advanced Research Projects Agency (DARPA) has funded innovative scientific research and technology developments in the field of brain-computer interfaces (BCI) since the 1970s. This review highlights some of DARPA's major advances in the field of BCI, particularly those made in recent years. Two broad categories of DARPA programs are presented with respect to the ultimate goals of supporting the nation's warfighters: (1) BCI efforts aimed at restoring neural and/or behavioral function, and (2) BCI efforts aimed at improving human training and performance. The programs discussed are synergistic and complementary to one another, and, moreover, promote interdisciplinary collaborations among researchers, engineers, and clinicians. Finally, this review includes a summary of some of the remaining challenges for the field of BCI, as well as the goals of new DARPA efforts in this domain. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications.

PubMed

Hemakom, Apit; Goverdovsky, Valentin; Looney, David; Mandic, Danilo P

2016-04-13

An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative brain-computer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate. © 2016 The Author(s).

Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.

PubMed

Hoang, Tuan; Tran, Dat; Huang, Xu

2013-01-01

Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.

Micro-video display with ocular tracking and interactive voice control

NASA Technical Reports Server (NTRS)

Miller, James E.

1993-01-01

In certain space-restricted environments, many of the benefits resulting from computer technology have been foregone because of the size, weight, inconvenience, and lack of mobility associated with existing computer interface devices. Accordingly, an effort to develop a highly miniaturized and 'wearable' computer display and control interface device, referred to as the Sensory Integrated Data Interface (SIDI), is underway. The system incorporates a micro-video display that provides data display and ocular tracking on a lightweight headset. Software commands are implemented by conjunctive eye movement and voice commands of the operator. In this initial prototyping effort, various 'off-the-shelf' components have been integrated into a desktop computer and with a customized menu-tree software application to demonstrate feasibility and conceptual capabilities. When fully developed as a customized system, the interface device will allow mobile, 'hand-free' operation of portable computer equipment. It will thus allow integration of information technology applications into those restrictive environments, both military and industrial, that have not yet taken advantage of the computer revolution. This effort is Phase 1 of Small Business Innovative Research (SBIR) Topic number N90-331 sponsored by the Naval Undersea Warfare Center Division, Newport. The prime contractor is Foster-Miller, Inc. of Waltham, MA.

The Pursuit of Chronically Reliable Neural Interfaces: A Materials Perspective.

PubMed

Guo, Liang

2016-01-01

Brain-computer interfaces represent one of the most astonishing technologies in our era. However, the grand challenge of chronic instability and limited throughput of the electrode-tissue interface has significantly hindered the further development and ultimate deployment of such exciting technologies. A multidisciplinary research workforce has been called upon to respond to this engineering need. In this paper, I briefly review this multidisciplinary pursuit of chronically reliable neural interfaces from a materials perspective by analyzing the problem, abstracting the engineering principles, and summarizing the corresponding engineering strategies. I further draw my future perspectives by extending the proposed engineering principles.

A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities

NASA Astrophysics Data System (ADS)

Leeb, Robert; Sagha, Hesam; Chavarriaga, Ricardo; Millán, José del R.

2011-04-01

Hybrid brain-computer interfaces (BCIs) are representing a recent approach to develop practical BCIs. In such a system disabled users are able to use all their remaining functionalities as control possibilities in parallel with the BCI. Sometimes these people have residual activity of their muscles. Therefore, in the presented hybrid BCI framework we want to explore the parallel usage of electroencephalographic (EEG) and electromyographic (EMG) activity, whereby the control abilities of both channels are fused. Results showed that the participants could achieve a good control of their hybrid BCI independently of their level of muscular fatigue. Thereby the multimodal fusion approach of muscular and brain activity yielded better and more stable performance compared to the single conditions. Even in the case of an increasing muscular fatigue a good control (moderate and graceful degradation of the performance compared to the non-fatigued case) and a smooth handover could be achieved. Therefore, such systems allow the users a very reliable hybrid BCI control although they are getting more and more exhausted or fatigued during the day.

A noninvasive brain computer interface using visually-induced near-infrared spectroscopy responses.

PubMed

Chen, Cheng-Hsuan; Ho, Ming-Shan; Shyu, Kuo-Kai; Hsu, Kou-Cheng; Wang, Kuo-Wei; Lee, Po-Lei

2014-09-19

Visually-induced near-infrared spectroscopy (NIRS) response was utilized to design a brain computer interface (BCI) system. Four circular checkerboards driven by distinct flickering sequences were displayed on a LCD screen as visual stimuli to induce subjects' NIRS responses. Each flickering sequence was a concatenated sequence of alternative flickering segments and resting segments. The flickering segment was designed with fixed duration of 3s whereas the resting segment was chosen randomly within 15-20s to create the mutual independencies among different flickering sequences. Six subjects were recruited in this study and subjects were requested to gaze at the four visual stimuli one-after-one in a random order. Since visual responses in human brain are time-locked to the onsets of visual stimuli and the flicker sequences of distinct visual stimuli were designed mutually independent, the NIRS responses induced by user's gazed targets can be discerned from non-gazed targets by applying a simple averaging process. The accuracies for the six subjects were higher than 90% after 10 or more epochs being averaged. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Toward brain-actuated car applications: Self-paced control with a motor imagery-based brain-computer interface.

PubMed

Yu, Yang; Zhou, Zongtan; Yin, Erwei; Jiang, Jun; Tang, Jingsheng; Liu, Yadong; Hu, Dewen

2016-10-01

This study presented a paradigm for controlling a car using an asynchronous electroencephalogram (EEG)-based brain-computer interface (BCI) and presented the experimental results of a simulation performed in an experimental environment outside the laboratory. This paradigm uses two distinct MI tasks, imaginary left- and right-hand movements, to generate a multi-task car control strategy consisting of starting the engine, moving forward, turning left, turning right, moving backward, and stopping the engine. Five healthy subjects participated in the online car control experiment, and all successfully controlled the car by following a previously outlined route. Subject S1 exhibited the most satisfactory BCI-based performance, which was comparable to the manual control-based performance. We hypothesize that the proposed self-paced car control paradigm based on EEG signals could potentially be used in car control applications, and we provide a complementary or alternative way for individuals with locked-in disorders to achieve more mobility in the future, as well as providing a supplementary car-driving strategy to assist healthy people in driving a car. Copyright © 2016 Elsevier Ltd. All rights reserved.

PET Mapping for Brain-Computer Interface Stimulation of the Ventroposterior Medial Nucleus of the Thalamus in Rats with Implanted Electrodes.

PubMed

Zhu, Yunqi; Xu, Kedi; Xu, Caiyun; Zhang, Jiacheng; Ji, Jianfeng; Zheng, Xiaoxiang; Zhang, Hong; Tian, Mei

2016-07-01

Brain-computer interface (BCI) technology has great potential for improving the quality of life for neurologic patients. This study aimed to use PET mapping for BCI-based stimulation in a rat model with electrodes implanted in the ventroposterior medial (VPM) nucleus of the thalamus. PET imaging studies were conducted before and after stimulation of the right VPM. Stimulation induced significant orienting performance. (18)F-FDG uptake increased significantly in the paraventricular thalamic nucleus, septohippocampal nucleus, olfactory bulb, left crus II of the ansiform lobule of the cerebellum, and bilaterally in the lateral septum, amygdala, piriform cortex, endopiriform nucleus, and insular cortex, but it decreased in the right secondary visual cortex, right simple lobule of the cerebellum, and bilaterally in the somatosensory cortex. This study demonstrated that PET mapping after VPM stimulation can identify specific brain regions associated with orienting performance. PET molecular imaging may be an important approach for BCI-based research and its clinical applications. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke

PubMed Central

Remsik, Alexander; Young, Brittany; Vermilyea, Rebecca; Kiekoefer, Laura; Abrams, Jessica; Elmore, Samantha Evander; Schultz, Paige; Nair, Veena; Edwards, Dorothy; Williams, Justin; Prabhakaran, Vivek

2016-01-01

Stroke is a leading cause of acquired disability resulting in distal upper extremity functional motor impairment. Stroke mortality rates continue to decline with advances in healthcare and medical technology. This has led to an increased demand for advanced, personalized rehabilitation. Survivors often experience some level of spontaneous recovery shortly after their stroke event; yet reach a functional plateau after which there is exiguous motor recovery. Nevertheless, studies have demonstrated the potential for recovery beyond this plateau. Non-traditional neurorehabilitation techniques, such as those incorporating the brain-computer interface (BCI), are being investigated for rehabilitation. BCIs may offer a gateway to the brain’s plasticity and revolutionize how humans interact with the world. Non-invasive BCIs work by closing the proprioceptive feedback loop with real-time, multi-sensory feedback allowing for volitional modulation of brain signals to assist hand function. BCI technology potentially promotes neuroplasticity and Hebbian-based motor recovery by rewarding cortical activity associated with sensory-motor rhythms through use with a variety of self-guided and assistive modalities. PMID:27112213

Exploring differences between left and right hand motor imagery via spatio-temporal EEG microstate.

PubMed

Liu, Weifeng; Liu, Xiaoming; Dai, Ruomeng; Tang, Xiaoying

2017-12-01

EEG-based motor imagery is very useful in brain-computer interface. How to identify the imaging movement is still being researched. Electroencephalography (EEG) microstates reflect the spatial configuration of quasi-stable electrical potential topographies. Different microstates represent different brain functions. In this paper, microstate method was used to process the EEG-based motor imagery to obtain microstate. The single-trial EEG microstate sequences differences between two motor imagery tasks - imagination of left and right hand movement were investigated. The microstate parameters - duration, time coverage and occurrence per second as well as the transition probability of the microstate sequences were obtained with spatio-temporal microstate analysis. The results were shown significant differences (P < 0.05) with paired t-test between the two tasks. Then these microstate parameters were used as features and a linear support vector machine (SVM) was utilized to classify the two tasks with mean accuracy 89.17%, superior performance compared to the other methods. These indicate that the microstate can be a promising feature to improve the performance of the brain-computer interface classification.

A USB 2.0 computer interface for the UCO/Lick CCD cameras

NASA Astrophysics Data System (ADS)

Wei, Mingzhi; Stover, Richard J.

2004-09-01

The new UCO/Lick Observatory CCD camera uses a 200 MHz fiber optic cable to transmit image data and an RS232 serial line for low speed bidirectional command and control. Increasingly RS232 is a legacy interface supported on fewer computers. The fiber optic cable requires either a custom interface board that is plugged into the mainboard of the image acquisition computer to accept the fiber directly or an interface converter that translates the fiber data onto a widely used standard interface. We present here a simple USB 2.0 interface for the UCO/Lick camera. A single USB cable connects to the image acquisition computer and the camera's RS232 serial and fiber optic cables plug into the USB interface. Since most computers now support USB 2.0 the Lick interface makes it possible to use the camera on essentially any modern computer that has the supporting software. No hardware modifications or additions to the computer are needed. The necessary device driver software has been written for the Linux operating system which is now widely used at Lick Observatory. The complete data acquisition software for the Lick CCD camera is running on a variety of PC style computers as well as an HP laptop.

Three-Dimensional Computer Graphics Brain-Mapping Project.

DTIC Science & Technology

1987-03-15

NEUROQUANT . This package was directed towards quantitative microneuroanatomic data acquisition and analysis. Using this interface, image frames captured...populations of brains. This would have been aprohibitive task if done manually with a densitometer and film, due to user error and bias. NEUROQUANT functioned...of cells were of interest. NEUROQUANT is presently being implemented with a more fully automatic method of localizing the cell bodies directly

Brain machine interfaces combining microelectrode arrays with nanostructured optical biochemical sensors

NASA Astrophysics Data System (ADS)

Hajj-Hassan, Mohamad; Gonzalez, Timothy; Ghafer-Zadeh, Ebrahim; Chodavarapu, Vamsy; Musallam, Sam; Andrews, Mark

2009-02-01

Neural microelectrodes are an important component of neural prosthetic systems which assist paralyzed patients by allowing them to operate computers or robots using their neural activity. These microelectrodes are also used in clinical settings to localize the locus of seizure initiation in epilepsy or to stimulate sub-cortical structures in patients with Parkinson's disease. In neural prosthetic systems, implanted microelectrodes record the electrical potential generated by specific thoughts and relay the signals to algorithms trained to interpret these thoughts. In this paper, we describe novel elongated multi-site neural electrodes that can record electrical signals and specific neural biomarkers and that can reach depths greater than 8mm in the sulcus of non-human primates (monkeys). We hypothesize that additional signals recorded by the multimodal probes will increase the information yield when compared to standard probes that record just electropotentials. We describe integration of optical biochemical sensors with neural microelectrodes. The sensors are made using sol-gel derived xerogel thin films that encapsulate specific biomarker responsive luminophores in their nanostructured pores. The desired neural biomarkers are O2, pH, K+, and Na+ ions. As a prototype, we demonstrate direct-write patterning to create oxygen-responsive xerogel waveguide structures on the neural microelectrodes. The recording of neural biomarkers along with electrical activity could help the development of intelligent and more userfriendly neural prosthesis/brain machine interfaces as well as aid in providing answers to complex brain diseases and disorders.

Removal of proprioception by BCI raises a stronger body ownership illusion in control of a humanlike robot

PubMed Central

Alimardani, Maryam; Nishio, Shuichi; Ishiguro, Hiroshi

2016-01-01

Body ownership illusions provide evidence that our sense of self is not coherent and can be extended to non-body objects. Studying about these illusions gives us practical tools to understand the brain mechanisms that underlie body recognition and the experience of self. We previously introduced an illusion of body ownership transfer (BOT) for operators of a very humanlike robot. This sensation of owning the robot’s body was confirmed when operators controlled the robot either by performing the desired motion with their body (motion-control) or by employing a brain-computer interface (BCI) that translated motor imagery commands to robot movement (BCI-control). The interesting observation during BCI-control was that the illusion could be induced even with a noticeable delay in the BCI system. Temporal discrepancy has always shown critical weakening effects on body ownership illusions. However the delay-robustness of BOT during BCI-control raised a question about the interaction between the proprioceptive inputs and delayed visual feedback in agency-driven illusions. In this work, we compared the intensity of BOT illusion for operators in two conditions; motion-control and BCI-control. Our results revealed a significantly stronger BOT illusion for the case of BCI-control. This finding highlights BCI’s potential in inducing stronger agency-driven illusions by building a direct communication between the brain and controlled body, and therefore removing awareness from the subject’s own body. PMID:27654174

Unscented Kalman Filter for Brain-Machine Interfaces

PubMed Central

Li, Zheng; O'Doherty, Joseph E.; Hanson, Timothy L.; Lebedev, Mikhail A.; Henriquez, Craig S.; Nicolelis, Miguel A. L.

2009-01-01

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation. PMID:19603074

Endpoint-based parallel data processing in a parallel active messaging interface of a parallel computer

DOEpatents

Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.

2014-08-12

Endpoint-based parallel data processing in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI, including establishing a data communications geometry, the geometry specifying, for tasks representing processes of execution of the parallel application, a set of endpoints that are used in collective operations of the PAMI including a plurality of endpoints for one of the tasks; receiving in endpoints of the geometry an instruction for a collective operation; and executing the instruction for a collective operation through the endpoints in dependence upon the geometry, including dividing data communications operations among the plurality of endpoints for one of the tasks.

Brain-computer interface users speak up: the Virtual Users' Forum at the 2013 International Brain-Computer Interface Meeting.

PubMed

Peters, Betts; Bieker, Gregory; Heckman, Susan M; Huggins, Jane E; Wolf, Catherine; Zeitlin, Debra; Fried-Oken, Melanie

2015-03-01

More than 300 researchers gathered at the 2013 International Brain-Computer Interface (BCI) Meeting to discuss current practice and future goals for BCI research and development. The authors organized the Virtual Users' Forum at the meeting to provide the BCI community with feedback from users. We report on the Virtual Users' Forum, including initial results from ongoing research being conducted by 2 BCI groups. Online surveys and in-person interviews were used to solicit feedback from people with disabilities who are expert and novice BCI users. For the Virtual Users' Forum, their responses were organized into 4 major themes: current (non-BCI) communication methods, experiences with BCI research, challenges of current BCIs, and future BCI developments. Two authors with severe disabilities gave presentations during the Virtual Users' Forum, and their comments are integrated with the other results. While participants' hopes for BCIs of the future remain high, their comments about available systems mirror those made by consumers about conventional assistive technology. They reflect concerns about reliability (eg, typing accuracy/speed), utility (eg, applications and the desire for real-time interactions), ease of use (eg, portability and system setup), and support (eg, technical support and caregiver training). People with disabilities, as target users of BCI systems, can provide valuable feedback and input on the development of BCI as an assistive technology. To this end, participatory action research should be considered as a valuable methodology for future BCI research. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.

PubMed

Spüler, Martin; Rosenstiel, Wolfgang; Bogdan, Martin

2012-01-01

The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.

The impact of goal-oriented task design on neurofeedback learning for brain-computer interface control.

PubMed

McWhinney, S R; Tremblay, A; Boe, S G; Bardouille, T

2018-02-01

Neurofeedback training teaches individuals to modulate brain activity by providing real-time feedback and can be used for brain-computer interface control. The present study aimed to optimize training by maximizing engagement through goal-oriented task design. Participants were shown either a visual display or a robot, where each was manipulated using motor imagery (MI)-related electroencephalography signals. Those with the robot were instructed to quickly navigate grid spaces, as the potential for goal-oriented design to strengthen learning was central to our investigation. Both groups were hypothesized to show increased magnitude of these signals across 10 sessions, with the greatest gains being seen in those navigating the robot due to increased engagement. Participants demonstrated the predicted increase in magnitude, with no differentiation between hemispheres. Participants navigating the robot showed stronger left-hand MI increases than those with the computer display. This is likely due to success being reliant on maintaining strong MI-related signals. While older participants showed stronger signals in early sessions, this trend later reversed, suggesting greater natural proficiency but reduced flexibility. These results demonstrate capacity for modulating neurofeedback using MI over a series of training sessions, using tasks of varied design. Importantly, the more goal-oriented robot control task resulted in greater improvements.

Goal selection versus process control in a brain-computer interface based on sensorimotor rhythms.

PubMed

Royer, Audrey S; He, Bin

2009-02-01

In a brain-computer interface (BCI) utilizing a process control strategy, the signal from the cortex is used to control the fine motor details normally handled by other parts of the brain. In a BCI utilizing a goal selection strategy, the signal from the cortex is used to determine the overall end goal of the user, and the BCI controls the fine motor details. A BCI based on goal selection may be an easier and more natural system than one based on process control. Although goal selection in theory may surpass process control, the two have never been directly compared, as we are reporting here. Eight young healthy human subjects participated in the present study, three trained and five naïve in BCI usage. Scalp-recorded electroencephalograms (EEG) were used to control a computer cursor during five different paradigms. The paradigms were similar in their underlying signal processing and used the same control signal. However, three were based on goal selection, and two on process control. For both the trained and naïve populations, goal selection had more hits per run, was faster, more accurate (for seven out of eight subjects) and had a higher information transfer rate than process control. Goal selection outperformed process control in every measure studied in the present investigation.

Neurofeedback: One of today's techniques in psychiatry?

PubMed

Arns, M; Batail, J-M; Bioulac, S; Congedo, M; Daudet, C; Drapier, D; Fovet, T; Jardri, R; Le-Van-Quyen, M; Lotte, F; Mehler, D; Micoulaud-Franchi, J-A; Purper-Ouakil, D; Vialatte, F

2017-04-01

Neurofeedback is a technique that aims to teach a subject to regulate a brain parameter measured by a technical interface to modulate his/her related brain and cognitive activities. However, the use of neurofeedback as a therapeutic tool for psychiatric disorders remains controversial. The aim of this review is to summarize and to comment the level of evidence of electroencephalogram (EEG) neurofeedback and real-time functional magnetic resonance imaging (fMRI) neurofeedback for therapeutic application in psychiatry. Literature on neurofeedback and mental disorders but also on brain computer interfaces (BCI) used in the field of neurocognitive science has been considered by the group of expert of the Neurofeedback evaluation & training (NExT) section of the French Association of biological psychiatry and neuropsychopharmacology (AFPBN). Results show a potential efficacy of EEG-neurofeedback in the treatment of attentional-deficit/hyperactivity disorder (ADHD) in children, even if this is still debated. For other mental disorders, there is too limited research to warrant the use of EEG-neurofeedback in clinical practice. Regarding fMRI neurofeedback, the level of evidence remains too weak, for now, to justify clinical use. The literature review highlights various unclear points, such as indications (psychiatric disorders, pathophysiologic rationale), protocols (brain signals targeted, learning characteristics) and techniques (EEG, fMRI, signal processing). The field of neurofeedback involves psychiatrists, neurophysiologists and researchers in the field of brain computer interfaces. Future studies should determine the criteria for optimizing neurofeedback sessions. A better understanding of the learning processes underpinning neurofeedback could be a key element to develop the use of this technique in clinical practice. Copyright © 2016 L’Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

``DMS-R, the Brain of the ISS'', 10 Years of Continuous Successful Operation in Space

NASA Astrophysics Data System (ADS)

Wolff, Bernd; Scheffers, Peter

2012-08-01

Space industries on both sides of the Atlantic were faced with a new situation of collaboration in the beginning of the 1990s.In 1995, industrial cooperation between ASTRIUM ST, Bremen and RSC-E, Moscow started aiming the outfitting of the Russian Service Module ZVEZDA for the ISS with computers. The requested equipments had to provide not only redundancy but fault tolerance and high availability. The design and development of two fault tolerant computers, (FTCs) responsible for the telemetry (Telemetry Computer: TC) and the central control (CC), as well as the man machine interface CPC were contracted to ASTRIUM ST, Bremen. The computer system is responsible e.g. for the life support system and the ISS re-boost control.In July 2000, the integration of the Russian Service Module ZVEZDA with Russian ZARYA FGB and American Node 1 bears witness for transatlantic and European cooperation.The Russian Service module ZVEZDA provides several basic functions as Avionics Control, the Environmental Control and Life Support (ECLS) in the ISS and control of the docked Automatic Transfer Vehicle (ATV) which includes re-boost of ISS. If these elementary functions fail or do not work reliable the effects for the ISS will be catastrophic with respect to Safety (manned space) and ISS mission.For that reason the responsible computer system Data Management System - Russia (DMS-R) is also called "The brain of the ISS".The Russian Service module ZVEZDA, including DMS-R, was launched on 12th of July, 2000. DMS-R was operational also during launch and docking.The talk provide information about the definition, design and development of DMS-R, the integration of DMS-R in the Russian Service module and the maintenance of the system in space. Besides the technical aspects are also the German - Russian cooperation an important subject of this speech. An outlook finalises the talk providing further development activities and application of fault tolerant systems.The importance of the DMS-R equipment for the ISS related to availability and reliability is reported in paragraph 1.2, describing a serious incident.The DMS-R architecture, consisting of two fault tolerant computers, their interconnection via MIL 1553 STD Bus and the Control Post Computer (CPC) as man- machine interface is given in figure 1. The main data transfer within the ISS and therefore also the Russian segment is managed by the MIL1553 STD bus. The focus of this script is neither the operational concept nor the fault tolerant design according the Byzantine Theorem, but the architectural embedment. One fault tolerant computer consists out of up to four fault containment regions (FCR), comparing in- and output data and deciding by majority voting whether a faulty FCR has to be isolated. For this purpose all data have to pass the so-called fault management element and are distributed to the other participants in the computer pool (FTC). Each fault containment region is connected to the avionic busses of the vehicle avionics system. In case of a faulty FCR (wrong calculation result was detected by the other FCRs or by build-in self-detection) the dedicated FCR will reset itself or will be reset by the others. The bus controller functions of the isolated FCR will be taken over according to a specific deterministic scheme from another FCR. The FTC data throughput will be maintained, the FTC operation will continue without interruption. Each FCR consists of an application CPU board (ALB), the fault management layer (FML), the avionics bus interface board (AVI) and a power supply (PSU), sharing a VME data bus.The FML is fully transparent, in terms of I/O accessibility, to the application S/W and votes the data autonomously received from the avionics busses and transmitted from the application.

Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI

PubMed Central

Müller-Putz, Gernot R.; Breitwieser, Christian; Cincotti, Febo; Leeb, Robert; Schreuder, Martijn; Leotta, Francesco; Tavella, Michele; Bianchi, Luigi; Kreilinger, Alex; Ramsay, Andrew; Rohm, Martin; Sagebaum, Max; Tonin, Luca; Neuper, Christa; Millán, José del. R.

2011-01-01

The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a new concept and common software framework is introduced. It consists of four interfaces connecting the classical BCI modules: signal acquisition, preprocessing, feature extraction, classification, and the application. But it provides also the concept of fusion and shared control. In a proof of concept, the functionality of the proposed system was demonstrated. PMID:22131973

Distributed computing system with dual independent communications paths between computers and employing split tokens

NASA Technical Reports Server (NTRS)

Rasmussen, Robert D. (Inventor); Manning, Robert M. (Inventor); Lewis, Blair F. (Inventor); Bolotin, Gary S. (Inventor); Ward, Richard S. (Inventor)

1990-01-01

This is a distributed computing system providing flexible fault tolerance; ease of software design and concurrency specification; and dynamic balance of the loads. The system comprises a plurality of computers each having a first input/output interface and a second input/output interface for interfacing to communications networks each second input/output interface including a bypass for bypassing the associated computer. A global communications network interconnects the first input/output interfaces for providing each computer the ability to broadcast messages simultaneously to the remainder of the computers. A meshwork communications network interconnects the second input/output interfaces providing each computer with the ability to establish a communications link with another of the computers bypassing the remainder of computers. Each computer is controlled by a resident copy of a common operating system. Communications between respective ones of computers is by means of split tokens each having a moving first portion which is sent from computer to computer and a resident second portion which is disposed in the memory of at least one of computer and wherein the location of the second portion is part of the first portion. The split tokens represent both functions to be executed by the computers and data to be employed in the execution of the functions. The first input/output interfaces each include logic for detecting a collision between messages and for terminating the broadcasting of a message whereby collisions between messages are detected and avoided.

Reaching and Grasping a Glass of Water by Locked-In ALS Patients through a BCI-Controlled Humanoid Robot

PubMed Central

Spataro, Rossella; Chella, Antonio; Allison, Brendan; Giardina, Marcello; Sorbello, Rosario; Tramonte, Salvatore; Guger, Christoph; La Bella, Vincenzo

2017-01-01

Locked-in Amyotrophic Lateral Sclerosis (ALS) patients are fully dependent on caregivers for any daily need. At this stage, basic communication and environmental control may not be possible even with commonly used augmentative and alternative communication devices. Brain Computer Interface (BCI) technology allows users to modulate brain activity for communication and control of machines and devices, without requiring a motor control. In the last several years, numerous articles have described how persons with ALS could effectively use BCIs for different goals, usually spelling. In the present study, locked-in ALS patients used a BCI system to directly control the humanoid robot NAO (Aldebaran Robotics, France) with the aim of reaching and grasping a glass of water. Four ALS patients and four healthy controls were recruited and trained to operate this humanoid robot through a P300-based BCI. A few minutes training was sufficient to efficiently operate the system in different environments. Three out of the four ALS patients and all controls successfully performed the task with a high level of accuracy. These results suggest that BCI-operated robots can be used by locked-in ALS patients as an artificial alter-ego, the machine being able to move, speak and act in his/her place. PMID:28298888

Evaluating the Effects of Interface Disruption Using fNIR Spectroscopy

DTIC Science & Technology

2011-02-28

Error Potentials in Brain- Computer Interfaces. ADVANCES IN COGNITIVE NEURODYNAMICS , 2008: p. 777-782. 21. Nieuwenhuis S, et al., Psychophysiology...introduced until the 1990‟s and holds great potential for extremely non-invasive cognitive state measurement. It is significantly easier and faster to...As a reminder, the general protocol is as follows: 1) Researchers gather benchmark tasks from cognitive psychology that elicit high and low

International Assessment of Research and Development in Brain-Computer Interfaces. WTEC Panel Report

DTIC Science & Technology

2007-10-01

the quality of life of those affected with CNS-related disabilities (Lebedev and Nicolelis 2006; Schwartz et al. 2006). The future economic impact...abiotic interface and assessing their potential to affect device function. Although the broad brush strokes are in place, significant detail is lacking... affect electrode impedance. The thought is that astrocytes increase extracellular tortuosity in the surrounding tissue, which increases the path length

Implementation of an Embedded Web Server Application for Wireless Control of Brain Computer Interface Based Home Environments.

PubMed

Aydın, Eda Akman; Bay, Ömer Faruk; Güler, İnan

2016-01-01

Brain Computer Interface (BCI) based environment control systems could facilitate life of people with neuromuscular diseases, reduces dependence on their caregivers, and improves their quality of life. As well as easy usage, low-cost, and robust system performance, mobility is an important functionality expected from a practical BCI system in real life. In this study, in order to enhance users' mobility, we propose internet based wireless communication between BCI system and home environment. We designed and implemented a prototype of an embedded low-cost, low power, easy to use web server which is employed in internet based wireless control of a BCI based home environment. The embedded web server provides remote access to the environmental control module through BCI and web interfaces. While the proposed system offers to BCI users enhanced mobility, it also provides remote control of the home environment by caregivers as well as the individuals in initial stages of neuromuscular disease. The input of BCI system is P300 potentials. We used Region Based Paradigm (RBP) as stimulus interface. Performance of the BCI system is evaluated on data recorded from 8 non-disabled subjects. The experimental results indicate that the proposed web server enables internet based wireless control of electrical home appliances successfully through BCIs.

Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients.

PubMed

Bai, Ou; Lin, Peter; Huang, Dandan; Fei, Ding-Yu; Floeter, Mary Kay

2010-08-01

Patients usually require long-term training for effective EEG-based brain-computer interface (BCI) control due to fatigue caused by the demands for focused attention during prolonged BCI operation. We intended to develop a user-friendly BCI requiring minimal training and less mental load. Testing of BCI performance was investigated in three patients with amyotrophic lateral sclerosis (ALS) and three patients with primary lateral sclerosis (PLS), who had no previous BCI experience. All patients performed binary control of cursor movement. One ALS patient and one PLS patient performed four-directional cursor control in a two-dimensional domain under a BCI paradigm associated with human natural motor behavior using motor execution and motor imagery. Subjects practiced for 5-10min and then participated in a multi-session study of either binary control or four-directional control including online BCI game over 1.5-2h in a single visit. Event-related desynchronization and event-related synchronization in the beta band were observed in all patients during the production of voluntary movement either by motor execution or motor imagery. The online binary control of cursor movement was achieved with an average accuracy about 82.1+/-8.2% with motor execution and about 80% with motor imagery, whereas offline accuracy was achieved with 91.4+/-3.4% with motor execution and 83.3+/-8.9% with motor imagery after optimization. In addition, four-directional cursor control was achieved with an accuracy of 50-60% with motor execution and motor imagery. Patients with ALS or PLS may achieve BCI control without extended training, and fatigue might be reduced during operation of a BCI associated with human natural motor behavior. The development of a user-friendly BCI will promote practical BCI applications in paralyzed patients. Copyright 2010 International Federation of Clinical Neurophysiology. All rights reserved.

Cybathlon experiences of the Graz BCI racing team Mirage91 in the brain-computer interface discipline.

PubMed

Statthaler, Karina; Schwarz, Andreas; Steyrl, David; Kobler, Reinmar; Höller, Maria Katharina; Brandstetter, Julia; Hehenberger, Lea; Bigga, Marvin; Müller-Putz, Gernot

2017-12-28

In this work, we share our experiences made at the world-wide first CYBATHLON, an event organized by the Eidgenössische Technische Hochschule Zürich (ETH Zürich), which took place in Zurich in October 2016. It is a championship for severely motor impaired people using assistive prototype devices to compete against each other. Our team, the Graz BCI Racing Team MIRAGE91 from Graz University of Technology, participated in the discipline "Brain-Computer Interface Race". A brain-computer interface (BCI) is a device facilitating control of applications via the user's thoughts. Prominent applications include assistive technology such as wheelchairs, neuroprostheses or communication devices. In the CYBATHLON BCI Race, pilots compete in a BCI-controlled computer game. We report on setting up our team, the BCI customization to our pilot including long term training and the final BCI system. Furthermore, we describe CYBATHLON participation and analyze our CYBATHLON result. We found that our pilot was compliant over the whole time and that we could significantly reduce the average runtime between start and finish from initially 178 s to 143 s. After the release of the final championship specifications with shorter track length, the average runtime converged to 120 s. We successfully participated in the qualification race at CYBATHLON 2016, but performed notably worse than during training, with a runtime of 196 s. We speculate that shifts in the features, due to the nonstationarities in the electroencephalogram (EEG), but also arousal are possible reasons for the unexpected result. Potential counteracting measures are discussed. The CYBATHLON 2016 was a great opportunity for our student team. We consolidated our theoretical knowledge and turned it into practice, allowing our pilot to play a computer game. However, further research is required to make BCI technology invariant to non-task related changes of the EEG.

PHREEQCI; a graphical user interface for the geochemical computer program PHREEQC

USGS Publications Warehouse

Charlton, Scott R.; Macklin, Clifford L.; Parkhurst, David L.

1997-01-01

PhreeqcI is a Windows-based graphical user interface for the geochemical computer program PHREEQC. PhreeqcI provides the capability to generate and edit input data files, run simulations, and view text files containing simulation results, all within the framework of a single interface. PHREEQC is a multipurpose geochemical program that can perform speciation, inverse, reaction-path, and 1D advective reaction-transport modeling. Interactive access to all of the capabilities of PHREEQC is available with PhreeqcI. The interface is written in Visual Basic and will run on personal computers under the Windows(3.1), Windows95, and WindowsNT operating systems.

General-Purpose Serial Interface For Remote Control

NASA Technical Reports Server (NTRS)

Busquets, Anthony M.; Gupton, Lawrence E.

1990-01-01

Computer controls remote television camera. General-purpose controller developed to serve as interface between host computer and pan/tilt/zoom/focus functions on series of automated video cameras. Interface port based on 8251 programmable communications-interface circuit configured for tristated outputs, and connects controller system to any host computer with RS-232 input/output (I/O) port. Accepts byte-coded data from host, compares them with prestored codes in read-only memory (ROM), and closes or opens appropriate switches. Six output ports control opening and closing of as many as 48 switches. Operator controls remote television camera by speaking commands, in system including general-purpose controller.

Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

NASA Astrophysics Data System (ADS)

Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

2014-12-01

Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet computers.

Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task.

PubMed

Revechkis, Boris; Aflalo, Tyson N S; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A

2014-12-01

To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like 'Face in a Crowd' task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the 'Crowd') using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a 'Crowd Off' condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet computers.

Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

PubMed Central

Degenhart, Alan D.; Kelly, John W.; Ashmore, Robin C.; Collinger, Jennifer L.; Tyler-Kabara, Elizabeth C.; Weber, Douglas J.; Wang, Wei

2011-01-01

This paper presents “Craniux,” an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development. PMID:21687575

Craniux: a LabVIEW-based modular software framework for brain-machine interface research.

PubMed

Degenhart, Alan D; Kelly, John W; Ashmore, Robin C; Collinger, Jennifer L; Tyler-Kabara, Elizabeth C; Weber, Douglas J; Wang, Wei

2011-01-01

This paper presents "Craniux," an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.

Enhanced inter-subject brain computer interface with associative sensorimotor oscillations.

PubMed

Saha, Simanto; Ahmed, Khawza I; Mostafa, Raqibul; Khandoker, Ahsan H; Hadjileontiadis, Leontios

2017-02-01

Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.

Clinical usefulness of brain-computer interface-controlled functional electrical stimulation for improving brain activity in children with spastic cerebral palsy: a pilot randomized controlled trial.

PubMed

Kim, Tae-Woo; Lee, Byoung-Hee

2016-09-01

[Purpose] Evaluating the effect of brain-computer interface (BCI)-based functional electrical stimulation (FES) training on brain activity in children with spastic cerebral palsy (CP) was the aim of this study. [Subjects and Methods] Subjects were randomized into a BCI-FES group (n=9) and a functional electrical stimulation (FES) control group (n=9). Subjects in the BCI-FES group received wrist and hand extension training with FES for 30 minutes per day, 5 times per week for 6 weeks under the BCI-based program. The FES group received wrist and hand extension training with FES for the same amount of time. Sensorimotor rhythms (SMR) and middle beta waves (M-beta) were measured in frontopolar regions 1 and 2 (Fp1, Fp2) to determine the effects of BCI-FES training. [Results] Significant improvements in the SMR and M-beta of Fp1 and Fp2 were seen in the BCI-FES group. In contrast, significant improvement was only seen in the SMR and M-beta of Fp2 in the control group. [Conclusion] The results of the present study suggest that BCI-controlled FES training may be helpful in improving brain activity in patients with cerebral palsy and may be applied as effectively as traditional FES training.

A Menu-Driven Interface to Unix-Based Resources

PubMed Central

Evans, Elizabeth A.

1989-01-01

Unix has often been overlooked in the past as a viable operating system for anyone other than computer scientists. Its terseness, non-mnemonic nature of the commands, and the lack of user-friendly software to run under it are but a few of the user-related reasons which have been cited. It is, nevertheless, the operating system of choice in many cases. This paper describes a menu-driven interface to Unix which provides user-friendlier access to the software resources available on the computers running under Unix.

User-customized brain computer interfaces using Bayesian optimization

NASA Astrophysics Data System (ADS)

Bashashati, Hossein; Ward, Rabab K.; Bashashati, Ali

2016-04-01

Objective. The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject’s brain characteristics. Approach. To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers. Main Results. We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature. Significance. Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.

Mission-based Scenario Research: Experimental Design And Analysis

DTIC Science & Technology

2012-01-01

neurotechnologies called Brain-Computer Interaction Technologies. 15. SUBJECT TERMS neuroimaging, EEG, task loading, neurotechnologies , ground... neurotechnologies called Brain-Computer Interaction Technologies. INTRODUCTION Imagine a system that can identify operator fatigue during a long-term...BCIT), a class of neurotechnologies , that aim to improve task performance by incorporating measures of brain activity to optimize the interactions

Microcontroller interface for diode array spectrometry

NASA Astrophysics Data System (ADS)

Aguo, L.; Williams, R. R.

An alternative to bus-based computer interfacing is presented using diode array spectrometry as a typical application. The new interface consists of an embedded single-chip microcomputer, known as a microcontroller, which provides all necessary digital I/O and analog-to-digital conversion (ADC) along with an unprecedented amount of intelligence. Communication with a host computer system is accomplished by a standard serial interface so this type of interfacing is applicable to a wide range of personal and minicomputers and can be easily networked. Data are acquired asynchronousty and sent to the host on command. New operating modes which have no traditional counterparts are presented.

Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface.

PubMed

Bakardjian, Hovagim; Tanaka, Toshihisa; Cichocki, Andrzej

2010-01-18

This study pursues the optimization of the brain responses to small reversing patterns in a Steady-State Visual Evoked Potentials (SSVEP) paradigm, which could be used to maximize the efficiency of applications such as Brain-Computer Interfaces (BCI). We investigated the SSVEP frequency response for 32 frequencies (5-84 Hz), and the time dynamics of the brain response at 8, 14 and 28 Hz, to aid the definition of the optimal neurophysiological parameters and to outline the onset-delay and other limitations of SSVEP stimuli in applications such as our previously described four-command BCI system. Our results showed that the 5.6-15.3 Hz pattern reversal stimulation evoked the strongest responses, peaking at 12 Hz, and exhibiting weaker local maxima at 28 and 42 Hz. After stimulation onset, the long-term SSVEP response was highly non-stationary and the dynamics, including the first peak, was frequency-dependent. The evaluation of the performance of a frequency-optimized eight-command BCI system with dynamic neurofeedback showed a mean success rate of 98%, and a time delay of 3.4s. Robust BCI performance was achieved by all subjects even when using numerous small patterns clustered very close to each other and moving rapidly in 2D space. These results emphasize the need for SSVEP applications to optimize not only the analysis algorithms but also the stimuli in order to maximize the brain responses they rely on. (c) 2009 Elsevier Ireland Ltd. All rights reserved.

The Effects of Closed-Loop Medical Devices on the Autonomy and Accountability of Persons and Systems.

PubMed

Kellmeyer, Philipp; Cochrane, Thomas; Müller, Oliver; Mitchell, Christine; Ball, Tonio; Fins, Joseph J; Biller-Andorno, Nikola

2016-10-01

Closed-loop medical devices such as brain-computer interfaces are an emerging and rapidly advancing neurotechnology. The target patients for brain-computer interfaces (BCIs) are often severely paralyzed, and thus particularly vulnerable in terms of personal autonomy, decisionmaking capacity, and agency. Here we analyze the effects of closed-loop medical devices on the autonomy and accountability of both persons (as patients or research participants) and neurotechnological closed-loop medical systems. We show that although BCIs can strengthen patient autonomy by preserving or restoring communicative abilities and/or motor control, closed-loop devices may also create challenges for moral and legal accountability. We advocate the development of a comprehensive ethical and legal framework to address the challenges of emerging closed-loop neurotechnologies like BCIs and stress the centrality of informed consent and refusal as a means to foster accountability. We propose the creation of an international neuroethics task force with members from medical neuroscience, neuroengineering, computer science, medical law, and medical ethics, as well as representatives of patient advocacy groups and the public.

Design and Implementation of a Brain Computer Interface System for Controlling a Robotic Claw

NASA Astrophysics Data System (ADS)

Angelakis, D.; Zoumis, S.; Asvestas, P.

2017-11-01

The aim of this paper is to present the design and implementation of a brain-computer interface (BCI) system that can control a robotic claw. The system is based on the Emotiv Epoc headset, which provides the capability of simultaneous recording of 14 EEG channels, as well as wireless connectivity by means of the Bluetooth protocol. The system is initially trained to decode what user thinks to properly formatted data. The headset communicates with a personal computer, which runs a dedicated software application, implemented under the Processing integrated development environment. The application acquires the data from the headset and invokes suitable commands to an Arduino Uno board. The board decodes the received commands and produces corresponding signals to a servo motor that controls the position of the robotic claw. The system was tested successfully on a healthy, male subject, aged 28 years. The results are promising, taking into account that no specialized hardware was used. However, tests on a larger number of users is necessary in order to draw solid conclusions regarding the performance of the proposed system.

Neurobionics and the brain-computer interface: current applications and future horizons.

PubMed

Rosenfeld, Jeffrey V; Wong, Yan Tat

2017-05-01

The brain-computer interface (BCI) is an exciting advance in neuroscience and engineering. In a motor BCI, electrical recordings from the motor cortex of paralysed humans are decoded by a computer and used to drive robotic arms or to restore movement in a paralysed hand by stimulating the muscles in the forearm. Simultaneously integrating a BCI with the sensory cortex will further enhance dexterity and fine control. BCIs are also being developed to: provide ambulation for paraplegic patients through controlling robotic exoskeletons; restore vision in people with acquired blindness; detect and control epileptic seizures; and improve control of movement disorders and memory enhancement. High-fidelity connectivity with small groups of neurons requires microelectrode placement in the cerebral cortex. Electrodes placed on the cortical surface are less invasive but produce inferior fidelity. Scalp surface recording using electroencephalography is much less precise. BCI technology is still in an early phase of development and awaits further technical improvements and larger multicentre clinical trials before wider clinical application and impact on the care of people with disabilities. There are also many ethical challenges to explore as this technology evolves.

New Perspectives on Neuroengineering and Neurotechnologies: NSF-DFG Workshop Report.

PubMed

Moritz, Chet T; Ruther, Patrick; Goering, Sara; Stett, Alfred; Ball, Tonio; Burgard, Wolfram; Chudler, Eric H; Rao, Rajesh P N

2016-07-01

To identify and overcome barriers to creating new neurotechnologies capable of restoring both motor and sensory function in individuals with neurological conditions. This report builds upon the outcomes of a joint workshop between the US National Science Foundation and the German Research Foundation on New Perspectives in Neuroengineering and Neurotechnology convened in Arlington, VA, USA, November 13-14, 2014. The participants identified key technological challenges for recording and manipulating neural activity, decoding, and interpreting brain data in the presence of plasticity, and early considerations of ethical and social issues pertinent to the adoption of neurotechnologies. The envisaged progress in neuroengineering requires tightly integrated hardware and signal processing efforts, advances in understanding of physiological adaptations to closed-loop interactions with neural devices, and an open dialog with stakeholders and potential end-users of neurotechnology. The development of new neurotechnologies (e.g., bidirectional brain-computer interfaces) could significantly improve the quality of life of people living with the effects of brain or spinal cord injury, or other neurodegenerative diseases. Focused efforts aimed at overcoming the remaining barriers at the electrode tissue interface, developing implantable hardware with on-board computation, and refining stimulation methods to precisely activate neural tissue will advance both our understanding of brain function and our ability to treat currently intractable disorders of the nervous system.

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme

PubMed Central

Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI. PMID:26880873

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

PubMed

Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

Spatial issues in user interface design from a graphic design perspective

NASA Technical Reports Server (NTRS)

Marcus, Aaron

1989-01-01

The user interface of a computer system is a visual display that provides information about the status of operations on data within the computer and control options to the user that enable adjustments to these operations. From the very beginning of computer technology the user interface was a spatial display, although its spatial features were not necessarily complex or explicitly recognized by the users. All text and nonverbal signs appeared in a virtual space generally thought of as a single flat plane of symbols. Current technology of high performance workstations permits any element of the display to appear as dynamic, multicolor, 3-D signs in a virtual 3-D space. The complexity of appearance and the user's interaction with the display provide significant challenges to the graphic designer of current and future user interfaces. In particular, spatial depiction provides many opportunities for effective communication of objects, structures, processes, navigation, selection, and manipulation. Issues are presented that are relevant to the graphic designer seeking to optimize the user interface's spatial attributes for effective visual communication.

Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies.

PubMed

Peikon, Ian D; Fitzsimmons, Nathan A; Lebedev, Mikhail A; Nicolelis, Miguel A L

2009-06-15

Collection and analysis of limb kinematic data are essential components of the study of biological motion, including research into biomechanics, kinesiology, neurophysiology and brain-machine interfaces (BMIs). In particular, BMI research requires advanced, real-time systems capable of sampling limb kinematics with minimal contact to the subject's body. To answer this demand, we have developed an automated video tracking system for real-time tracking of multiple body parts in freely behaving primates. The system employs high-contrast markers painted on the animal's joints to continuously track the three-dimensional positions of their limbs during activity. Two-dimensional coordinates captured by each video camera are combined and converted to three-dimensional coordinates using a quadratic fitting algorithm. Real-time operation of the system is accomplished using direct memory access (DMA). The system tracks the markers at a rate of 52 frames per second (fps) in real-time and up to 100fps if video recordings are captured to be later analyzed off-line. The system has been tested in several BMI primate experiments, in which limb position was sampled simultaneously with chronic recordings of the extracellular activity of hundreds of cortical cells. During these recordings, multiple computational models were employed to extract a series of kinematic parameters from neuronal ensemble activity in real-time. The system operated reliably under these experimental conditions and was able to compensate for marker occlusions that occurred during natural movements. We propose that this system could also be extended to applications that include other classes of biological motion.

The Relationship of Three-Dimensional Human Skull Motion to Brain Tissue Deformation in Magnetic Resonance Elastography Studies

PubMed Central

Badachhape, Andrew A.; Okamoto, Ruth J.; Durham, Ramona S.; Efron, Brent D.; Nadell, Sam J.; Johnson, Curtis L.; Bayly, Philip V.

2017-01-01

In traumatic brain injury (TBI), membranes such as the dura mater, arachnoid mater, and pia mater play a vital role in transmitting motion from the skull to brain tissue. Magnetic resonance elastography (MRE) is an imaging technique developed for noninvasive estimation of soft tissue material parameters. In MRE, dynamic deformation of brain tissue is induced by skull vibrations during magnetic resonance imaging (MRI); however, skull motion and its mode of transmission to the brain remain largely uncharacterized. In this study, displacements of points in the skull, reconstructed using data from an array of MRI-safe accelerometers, were compared to displacements of neighboring material points in brain tissue, estimated from MRE measurements. Comparison of the relative amplitudes, directions, and temporal phases of harmonic motion in the skulls and brains of six human subjects shows that the skull–brain interface significantly attenuates and delays transmission of motion from skull to brain. In contrast, in a cylindrical gelatin “phantom,” displacements of the rigid case (reconstructed from accelerometer data) were transmitted to the gelatin inside (estimated from MRE data) with little attenuation or phase lag. This quantitative characterization of the skull–brain interface will be valuable in the parameterization and validation of computer models of TBI. PMID:28267188

The Relationship of Three-Dimensional Human Skull Motion to Brain Tissue Deformation in Magnetic Resonance Elastography Studies.

PubMed

Badachhape, Andrew A; Okamoto, Ruth J; Durham, Ramona S; Efron, Brent D; Nadell, Sam J; Johnson, Curtis L; Bayly, Philip V

2017-05-01

In traumatic brain injury (TBI), membranes such as the dura mater, arachnoid mater, and pia mater play a vital role in transmitting motion from the skull to brain tissue. Magnetic resonance elastography (MRE) is an imaging technique developed for noninvasive estimation of soft tissue material parameters. In MRE, dynamic deformation of brain tissue is induced by skull vibrations during magnetic resonance imaging (MRI); however, skull motion and its mode of transmission to the brain remain largely uncharacterized. In this study, displacements of points in the skull, reconstructed using data from an array of MRI-safe accelerometers, were compared to displacements of neighboring material points in brain tissue, estimated from MRE measurements. Comparison of the relative amplitudes, directions, and temporal phases of harmonic motion in the skulls and brains of six human subjects shows that the skull-brain interface significantly attenuates and delays transmission of motion from skull to brain. In contrast, in a cylindrical gelatin "phantom," displacements of the rigid case (reconstructed from accelerometer data) were transmitted to the gelatin inside (estimated from MRE data) with little attenuation or phase lag. This quantitative characterization of the skull-brain interface will be valuable in the parameterization and validation of computer models of TBI.

A validated methodology for determination of laboratory instrument computer interface efficacy

NASA Astrophysics Data System (ADS)

1984-12-01

This report is intended to provide a methodology for determining when, and for which instruments, direct interfacing of laboratory instrument and laboratory computers is beneficial. This methodology has been developed to assist the Tri-Service Medical Information Systems Program Office in making future decisions regarding laboratory instrument interfaces. We have calculated the time savings required to reach a break-even point for a range of instrument interface prices and corresponding average annual costs. The break-even analyses used empirical data to estimate the number of data points run per day that are required to meet the break-even point. The results indicate, for example, that at a purchase price of $3,000, an instrument interface will be cost-effective if the instrument is utilized for at least 154 data points per day if operated in the continuous mode, or 216 points per day if operated in the discrete mode. Although this model can help to ensure that instrument interfaces are cost effective, additional information should be considered in making the interface decisions. A reduction in results transcription errors may be a major benefit of instrument interfacing.

Assessment of a human computer interface prototyping environment

NASA Technical Reports Server (NTRS)

Moore, Loretta A.

1993-01-01

A Human Computer Interface (HCI) prototyping environment with embedded evaluation capability has been successfully assessed which will be valuable in developing and refining HCI standards and evaluating program/project interface development, especially Space Station Freedom on-board displays for payload operations. The HCI prototyping environment is designed to include four components: (1) a HCI format development tool, (2) a test and evaluation simulator development tool, (3) a dynamic, interactive interface between the HCI prototype and simulator, and (4) an embedded evaluation capability to evaluate the adequacy of an HCI based on a user's performance.

Physics through the 1990s: Scientific interfaces and technological applications

NASA Technical Reports Server (NTRS)

1986-01-01

The volume examines the scientific interfaces and technological applications of physics. Twelve areas are dealt with: biological physics-biophysics, the brain, and theoretical biology; the physics-chemistry interface-instrumentation, surfaces, neutron and synchrotron radiation, polymers, organic electronic materials; materials science; geophysics-tectonics, the atmosphere and oceans, planets, drilling and seismic exploration, and remote sensing; computational physics-complex systems and applications in basic research; mathematics-field theory and chaos; microelectronics-integrated circuits, miniaturization, future trends; optical information technologies-fiber optics and photonics; instrumentation; physics applications to energy needs and the environment; national security-devices, weapons, and arms control; medical physics-radiology, ultrasonics, MNR, and photonics. An executive summary and many chapters contain recommendations regarding funding, education, industry participation, small-group university research and large facility programs, government agency programs, and computer database needs.

Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state.

PubMed

Power, Sarah D; Kushki, Azadeh; Chau, Tom

2011-12-01

Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI) for individuals with severe motor impairments. For the most part, previous research has investigated the development of NIRS-BCIs operating under synchronous control paradigms, which require the user to exert conscious control over their mental activity whenever the system is vigilant. Though functional, this is mentally demanding and an unnatural way to communicate. An attractive alternative to the synchronous control paradigm is system-paced control, in which users are required to consciously modify their brain activity only when they wish to affect the BCI output, and can remain in a more natural, 'no-control' state at all other times. In this study, we investigated the feasibility of a system-paced NIRS-BCI with one intentional control (IC) state corresponding to the performance of either mental arithmetic or mental singing. In particular, this involved determining if these tasks could be distinguished, individually, from the unconstrained 'no-control' state. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while eight able-bodied adults performed mental arithmetic and mental singing to answer multiple-choice questions within a system-paced paradigm. With a linear classifier trained on a six-dimensional feature set, an overall classification accuracy of 71.2% across participants was achieved for the mental arithmetic versus no-control classification problem. While the mental singing versus no-control classification was less successful across participants (62.7% on average), four participants did attain accuracies well in excess of chance, three of which were above 70%. Analyses were performed offline. Collectively, these results are encouraging, and demonstrate the potential of a system-paced NIRS-BCI with one IC state corresponding to either mental arithmetic or mental singing.

Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state

NASA Astrophysics Data System (ADS)

Power, Sarah D.; Kushki, Azadeh; Chau, Tom

2011-10-01

Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI) for individuals with severe motor impairments. For the most part, previous research has investigated the development of NIRS-BCIs operating under synchronous control paradigms, which require the user to exert conscious control over their mental activity whenever the system is vigilant. Though functional, this is mentally demanding and an unnatural way to communicate. An attractive alternative to the synchronous control paradigm is system-paced control, in which users are required to consciously modify their brain activity only when they wish to affect the BCI output, and can remain in a more natural, 'no-control' state at all other times. In this study, we investigated the feasibility of a system-paced NIRS-BCI with one intentional control (IC) state corresponding to the performance of either mental arithmetic or mental singing. In particular, this involved determining if these tasks could be distinguished, individually, from the unconstrained 'no-control' state. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while eight able-bodied adults performed mental arithmetic and mental singing to answer multiple-choice questions within a system-paced paradigm. With a linear classifier trained on a six-dimensional feature set, an overall classification accuracy of 71.2% across participants was achieved for the mental arithmetic versus no-control classification problem. While the mental singing versus no-control classification was less successful across participants (62.7% on average), four participants did attain accuracies well in excess of chance, three of which were above 70%. Analyses were performed offline. Collectively, these results are encouraging, and demonstrate the potential of a system-paced NIRS-BCI with one IC state corresponding to either mental arithmetic or mental singing.

An introduction to neural networks surgery, a field of neuromodulation which is based on advances in neural networks science and digitised brain imaging.

PubMed

Sakas, D E; Panourias, I G; Simpson, B A

2007-01-01

Operative Neuromodulation is the field of altering electrically or chemically the signal transmission in the nervous system by implanted devices in order to excite, inhibit or tune the activities of neurons or neural networks and produce therapeutic effects. The present article reviews relevant literature on procedures or devices applied either in contact with the cerebral cortex or cranial nerves or in deep sites inside the brain in order to treat various refractory neurological conditions such as: a) chronic pain (facial, somatic, deafferentation, phantom limb), b) movement disorders (Parkinson's disease, dystonia, Tourette syndrome), c) epilepsy, d) psychiatric disease, e) hearing deficits, and f) visual loss. These data indicate that in operative neuromodulation, a new field emerges that is based on neural networks research and on advances in digitised stereometric brain imaging which allow precise localisation of cerebral neural networks and their relay stations; this field can be described as Neural networks surgery because it aims to act extrinsically or intrinsically on neural networks and to alter therapeutically the neural signal transmission with the use of implantable electrical or electronic devices. The authors also review neurotechnology literature relevant to neuroengineering, nanotechnologies, brain computer interfaces, hybrid cultured probes, neuromimetics, neuroinformatics, neurocomputation, and computational neuromodulation; the latter field is dedicated to the study of the biophysical and mathematical characteristics of electrochemical neuromodulation. The article also brings forward particularly interesting lines of research such as the carbon nanofibers electrode arrays for simultaneous electrochemical recording and stimulation, closed-loop systems for responsive neuromodulation, and the intracortical electrodes for restoring hearing or vision. The present review of cerebral neuromodulatory procedures highlights the transition from the conventional neurosurgery of resective or ablative techniques to a highly selective "surgery of networks". The dynamics of the convergence of the above biomedical and technological fields with biological restorative approaches have important implications for patients with severe neurological disorders.

Human Machine Interfaces for Teleoperators and Virtual Environments Conference

NASA Technical Reports Server (NTRS)

1990-01-01

In a teleoperator system the human operator senses, moves within, and operates upon a remote or hazardous environment by means of a slave mechanism (a mechanism often referred to as a teleoperator). In a virtual environment system the interactive human machine interface is retained but the slave mechanism and its environment are replaced by a computer simulation. Video is replaced by computer graphics. The auditory and force sensations imparted to the human operator are similarly computer generated. In contrast to a teleoperator system, where the purpose is to extend the operator's sensorimotor system in a manner that facilitates exploration and manipulation of the physical environment, in a virtual environment system, the purpose is to train, inform, alter, or study the human operator to modify the state of the computer and the information environment. A major application in which the human operator is the target is that of flight simulation. Although flight simulators have been around for more than a decade, they had little impact outside aviation presumably because the application was so specialized and so expensive.

Brain communication in the locked-in state.

PubMed

De Massari, Daniele; Ruf, Carolin A; Furdea, Adrian; Matuz, Tamara; van der Heiden, Linda; Halder, Sebastian; Silvoni, Stefano; Birbaumer, Niels

2013-06-01

Patients in the completely locked-in state have no means of communication and they represent the target population for brain-computer interface research in the last 15 years. Although different paradigms have been tested and different physiological signals used, to date no sufficiently documented completely locked-in state patient was able to control a brain-computer interface over an extended time period. We introduce Pavlovian semantic conditioning to enable basic communication in completely locked-in state. This novel paradigm is based on semantic conditioning for online classification of neuroelectric or any other physiological signals to discriminate between covert (cognitive) 'yes' and 'no' responses. The paradigm comprised the presentation of affirmative and negative statements used as conditioned stimuli, while the unconditioned stimulus consisted of electrical stimulation of the skin paired with affirmative statements. Three patients with advanced amyotrophic lateral sclerosis participated over an extended time period, one of which was in a completely locked-in state, the other two in the locked-in state. The patients' level of vigilance was assessed through auditory oddball procedures to study the correlation between vigilance level and the classifier's performance. The average online classification accuracies of slow cortical components of electroencephalographic signals were around chance level for all the patients. The use of a non-linear classifier in the offline classification procedure resulted in a substantial improvement of the accuracy in one locked-in state patient achieving 70% correct classification. A reliable level of performance in the completely locked-in state patient was not achieved uniformly throughout the 37 sessions despite intact cognitive processing capacity, but in some sessions communication accuracies up to 70% were achieved. Paradigm modifications are proposed. Rapid drop of vigilance was detected suggesting attentional variations or variations of circadian period as important factors in brain-computer interface communication with locked-in state and completely locked-in state.

User participation in the development of the human/computer interface for control centers

NASA Technical Reports Server (NTRS)

Broome, Richard; Quick-Campbell, Marlene; Creegan, James; Dutilly, Robert

1996-01-01

Technological advances coupled with the requirements to reduce operations staffing costs led to the demand for efficient, technologically-sophisticated mission operations control centers. The control center under development for the earth observing system (EOS) is considered. The users are involved in the development of a control center in order to ensure that it is cost-efficient and flexible. A number of measures were implemented in the EOS program in order to encourage user involvement in the area of human-computer interface development. The following user participation exercises carried out in relation to the system analysis and design are described: the shadow participation of the programmers during a day of operations; the flight operations personnel interviews; and the analysis of the flight operations team tasks. The user participation in the interface prototype development, the prototype evaluation, and the system implementation are reported on. The involvement of the users early in the development process enables the requirements to be better understood and the cost to be reduced.

Brain-machine interfaces can accelerate clarification of the principal mysteries and real plasticity of the brain.

PubMed

Sakurai, Yoshio

2014-01-01

This perspective emphasizes that the brain-machine interface (BMI) research has the potential to clarify major mysteries of the brain and that such clarification of the mysteries by neuroscience is needed to develop BMIs. I enumerate five principal mysteries. The first is "how is information encoded in the brain?" This is the fundamental question for understanding what our minds are and is related to the verification of Hebb's cell assembly theory. The second is "how is information distributed in the brain?" This is also a reconsideration of the functional localization of the brain. The third is "what is the function of the ongoing activity of the brain?" This is the problem of how the brain is active during no-task periods and what meaning such spontaneous activity has. The fourth is "how does the bodily behavior affect the brain function?" This is the problem of brain-body interaction, and obtaining a new "body" by a BMI leads to a possibility of changes in the owner's brain. The last is "to what extent can the brain induce plasticity?" Most BMIs require changes in the brain's neuronal activity to realize higher performance, and the neuronal operant conditioning inherent in the BMIs further enhances changes in the activity.

Client-Server: What Is It and Are We There Yet?

ERIC Educational Resources Information Center

Gershenfeld, Nancy

1995-01-01

Discusses client-server architecture in dumb terminals, personal computers, local area networks, and graphical user interfaces. Focuses on functions offered by client personal computers: individualized environments; flexibility in running operating systems; advanced operating system features; multiuser environments; and centralized data…

Remotely Accessible Testbed for Software Defined Radio Development

NASA Technical Reports Server (NTRS)

Lux, James P.; Lang, Minh; Peters, Kenneth J.; Taylor, Gregory H.

2012-01-01

Previous development testbeds have assumed that the developer was physically present in front of the hardware being used. No provision for remote operation of basic functions (power on/off or reset) was made, because the developer/operator was sitting in front of the hardware, and could just push the button manually. In this innovation, a completely remotely accessible testbed has been created, with all diagnostic equipment and tools set up for remote access, and using standardized interfaces so that failed equipment can be quickly replaced. In this testbed, over 95% of the operating hours were used for testing without the developer being physically present. The testbed includes a pair of personal computers, one running Linux and one running Windows. A variety of peripherals is connected via Ethernet and USB (universal serial bus) interfaces. A private internal Ethernet is used to connect to test instruments and other devices, so that the sole connection to the outside world is via the two PCs. An important design consideration was that all of the instruments and interfaces used stable, long-lived industry standards, such as Ethernet, USB, and GPIB (general purpose interface bus). There are no plug-in cards for the two PCs, so there are no problems with finding replacement computers with matching interfaces, device drivers, and installation. The only thing unique to the two PCs is the locally developed software, which is not specific to computer or operating system version. If a device (including one of the computers) were to fail or become unavailable (e.g., a test instrument needed to be recalibrated), replacing it is a straightforward process with a standard, off-the-shelf device.

Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study

PubMed Central

2013-01-01

Background Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and biosignals combined. Results fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired biosignals. Combining fNIRS data with biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state. PMID:23336819

Turbo-Satori: a neurofeedback and brain-computer interface toolbox for real-time functional near-infrared spectroscopy.

PubMed

Lührs, Michael; Goebel, Rainer

2017-10-01

Turbo-Satori is a neurofeedback and brain-computer interface (BCI) toolbox for real-time functional near-infrared spectroscopy (fNIRS). It incorporates multiple pipelines from real-time preprocessing and analysis to neurofeedback and BCI applications. The toolbox is designed with a focus in usability, enabling a fast setup and execution of real-time experiments. Turbo-Satori uses an incremental recursive least-squares procedure for real-time general linear model calculation and support vector machine classifiers for advanced BCI applications. It communicates directly with common NIRx fNIRS hardware and was tested extensively ensuring that the calculations can be performed in real time without a significant change in calculation times for all sampling intervals during ongoing experiments of up to 6 h of recording. Enabling immediate access to advanced processing features also allows the use of this toolbox for students and nonexperts in the field of fNIRS data acquisition and processing. Flexible network interfaces allow third party stimulus applications to access the processed data and calculated statistics in real time so that this information can be easily incorporated in neurofeedback or BCI presentations.

Language Model Applications to Spelling with Brain-Computer Interfaces

PubMed Central

Mora-Cortes, Anderson; Manyakov, Nikolay V.; Chumerin, Nikolay; Van Hulle, Marc M.

2014-01-01

Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models applied to them. These language models are classified according to their functionality in the context of BCI-based spelling: the static/dynamic nature of the user interface, the use of error correction and predictive spelling, and the potential to improve their classification performance by using language models. To conclude, the review offers an overview of the advantages and challenges when implementing language models in BCI-based communication systems when implemented in conjunction with other AAL technologies. PMID:24675760

Detection of EEG-patterns associated with real and imaginary movements using detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Pavlov, Alexey N.; Runnova, Anastasiya E.; Maksimenko, Vladimir A.; Grishina, Daria S.; Hramov, Alexander E.

2018-02-01

Authentic recognition of specific patterns of electroencephalograms (EEGs) associated with real and imagi- nary movements is an important stage for the development of brain-computer interfaces. In experiments with untrained participants, the ability to detect the motor-related brain activity based on the multichannel EEG processing is demonstrated. Using the detrended fluctuation analysis, changes in the EEG patterns during the imagination of hand movements are reported. It is discussed how the ability to recognize brain activity related to motor executions depends on the electrode position.

Project SUN (Students Understanding Nature)

NASA Technical Reports Server (NTRS)

Curley, T.; Yanow, G.

1995-01-01

Project SUN is part of NASA's 'Mission to Planet Earth' education outreach effort. It is based on development of low cost, scientifi- cally accurate instrumentation and computer interfacing, coupled with Apple II computers as dedicated data loggers. The project is com- prised of: instruments, interfacing, software, curriculum, a detailed operating manual, and a system of training at the school sites.

Interfacing laboratory instruments to multiuser, virtual memory computers

NASA Technical Reports Server (NTRS)

Generazio, Edward R.; Stang, David B.; Roth, Don J.

1989-01-01

Incentives, problems and solutions associated with interfacing laboratory equipment with multiuser, virtual memory computers are presented. The major difficulty concerns how to utilize these computers effectively in a medium sized research group. This entails optimization of hardware interconnections and software to facilitate multiple instrument control, data acquisition and processing. The architecture of the system that was devised, and associated programming and subroutines are described. An example program involving computer controlled hardware for ultrasonic scan imaging is provided to illustrate the operational features.

CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research

PubMed Central

Sherif, Tarek; Rioux, Pierre; Rousseau, Marc-Etienne; Kassis, Nicolas; Beck, Natacha; Adalat, Reza; Das, Samir; Glatard, Tristan; Evans, Alan C.

2014-01-01

The Canadian Brain Imaging Research Platform (CBRAIN) is a web-based collaborative research platform developed in response to the challenges raised by data-heavy, compute-intensive neuroimaging research. CBRAIN offers transparent access to remote data sources, distributed computing sites, and an array of processing and visualization tools within a controlled, secure environment. Its web interface is accessible through any modern browser and uses graphical interface idioms to reduce the technical expertise required to perform large-scale computational analyses. CBRAIN's flexible meta-scheduling has allowed the incorporation of a wide range of heterogeneous computing sites, currently including nine national research High Performance Computing (HPC) centers in Canada, one in Korea, one in Germany, and several local research servers. CBRAIN leverages remote computing cycles and facilitates resource-interoperability in a transparent manner for the end-user. Compared with typical grid solutions available, our architecture was designed to be easily extendable and deployed on existing remote computing sites with no tool modification, administrative intervention, or special software/hardware configuration. As October 2013, CBRAIN serves over 200 users spread across 53 cities in 17 countries. The platform is built as a generic framework that can accept data and analysis tools from any discipline. However, its current focus is primarily on neuroimaging research and studies of neurological diseases such as Autism, Parkinson's and Alzheimer's diseases, Multiple Sclerosis as well as on normal brain structure and development. This technical report presents the CBRAIN Platform, its current deployment and usage and future direction. PMID:24904400

CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research.

PubMed

Sherif, Tarek; Rioux, Pierre; Rousseau, Marc-Etienne; Kassis, Nicolas; Beck, Natacha; Adalat, Reza; Das, Samir; Glatard, Tristan; Evans, Alan C

2014-01-01

The Canadian Brain Imaging Research Platform (CBRAIN) is a web-based collaborative research platform developed in response to the challenges raised by data-heavy, compute-intensive neuroimaging research. CBRAIN offers transparent access to remote data sources, distributed computing sites, and an array of processing and visualization tools within a controlled, secure environment. Its web interface is accessible through any modern browser and uses graphical interface idioms to reduce the technical expertise required to perform large-scale computational analyses. CBRAIN's flexible meta-scheduling has allowed the incorporation of a wide range of heterogeneous computing sites, currently including nine national research High Performance Computing (HPC) centers in Canada, one in Korea, one in Germany, and several local research servers. CBRAIN leverages remote computing cycles and facilitates resource-interoperability in a transparent manner for the end-user. Compared with typical grid solutions available, our architecture was designed to be easily extendable and deployed on existing remote computing sites with no tool modification, administrative intervention, or special software/hardware configuration. As October 2013, CBRAIN serves over 200 users spread across 53 cities in 17 countries. The platform is built as a generic framework that can accept data and analysis tools from any discipline. However, its current focus is primarily on neuroimaging research and studies of neurological diseases such as Autism, Parkinson's and Alzheimer's diseases, Multiple Sclerosis as well as on normal brain structure and development. This technical report presents the CBRAIN Platform, its current deployment and usage and future direction.

User interface issues in supporting human-computer integrated scheduling

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.; Biefeld, Eric W.

1991-01-01

The topics are presented in view graph form and include the following: characteristics of Operations Mission Planner (OMP) schedule domain; OMP architecture; definition of a schedule; user interface dimensions; functional distribution; types of users; interpreting user interaction; dynamic overlays; reactive scheduling; and transitioning the interface.