WorldWideScience

Sample records for brain computer interfaces

  1. Practical Brain Computer Interfacing

    OpenAIRE

    Valbuena Varon, Diana Alexandra

    2011-01-01

    A brain-computer interface (BCI) is a communication system that enables users to voluntary send messages or commands without movement. The classical goal of BCI research is to support communication and control for users with impaired communication due to illness or injury. Typical BCI applications are the operation of computer cursors, spelling programs or external devices, such as wheelchairs, robots and neural prostheses. The user sends modulated information to the BCI by engaging in mental...

  2. Sistema Brain Computer Interface

    OpenAIRE

    Martín Barraza, Juan Ignacio

    2015-01-01

    En este trabajo de final de grado se realizará una aplicación de un sistema Brain Computer Interface en el cual, a partir del dipositivo Mind Wave de la compañía Neurosky, se pretenderá controlar el prototipo de una mano humana. Esta será controlada a partir de las ondas cerebrales medidas por el sensor que el dispositivo dispone. A continuación, la información captada por nuestro medidor de señales de electroencefalográficas será enviada por radiofrecuencia a un stick USB que viene incorpora...

  3. An optical brain computer interface

    OpenAIRE

    Coyle, S; Ward, Tomas; Markham, Charles

    2004-01-01

    This paper describes a novel approach to brain computer interfacing that uses optical analysis to provide physiological measures of brain function. We describe the optical analysis technique involved and the application of this method to development of our first prototype optical brain computer interface

  4. Metabolic Brain-Computer Interfaces

    OpenAIRE

    Sitaram, Ranganatha

    2010-01-01

    Brain-Computer Interfaces (BCI) utilise neurophysiological signals originating in the brain to activate or deactivate external devices or computers (Donoghue 2002; Wolpaw, Birbaumer et al. 2002; Nicolelis 2003; Birbaumer and Cohen 2007). The neuronal signals can be recorded from inside the brain (invasive BCIs) or outside (non-invasive BCIs) of the brain. Most BCIs developed so far have used operant training of direct neuroelectric responses, Electroencephalography (EEG) waves, event-related ...

  5. Brain-computer interface

    DEFF Research Database (Denmark)

    2014-01-01

    -selectable input; receiving at least one signal indicative of brain activity of the user; and determining, from the received signal, which of the one or more stimuli the user attends to and selecting the user-selectable input associated with the stimulation frequency of the determined stimuli as being a user...

  6. Emotional brain-computer interfaces

    NARCIS (Netherlands)

    Garcia Molina, G.; Tsoneva, T.; Nijholt, A.; Nijholt, A.; Heylen, D.K.J.

    2013-01-01

    Research in brain-computer interface (BCI) has significantly increased during the last few years. Additionally to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As any human-machine interaction system, BCIs can benefit

  7. Brain emotional learning based Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Abdolreza Asadi Ghanbari

    2012-09-01

    Full Text Available A brain computer interface (BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction and classification operations. Classification is crucial as it has a substantial effect on the BCI speed and bit rate. Recent developments of brain-computer interfaces (BCIs bring forward some challenging problems to the machine learning community, of which classification of time-varying electrophysiological signals is a crucial one. Constructing adaptive classifiers is a promising approach to deal with this problem. In this paper, we introduce adaptive classifiers for classify electroencephalogram (EEG signals. The adaptive classifier is brain emotional learning based adaptive classifier (BELBAC, which is based on emotional learning process. The main purpose of this research is to use a structural model based on the limbic system of mammalian brain, for decision making and control engineering applications. We have adopted a network model developed by Moren and Balkenius, as a computational model that mimics amygdala, orbitofrontal cortex, thalamus, sensory input cortex and generally, those parts of the brain thought responsible for processing emotions. The developed method was compared with other methods used for EEG signals classification (support vector machine (SVM and two different neural network types (MLP, PNN. The result analysis demonstrated an efficiency of the proposed approach.

  8. Brain Computer Interfaces, a Review

    Directory of Open Access Journals (Sweden)

    Luis Fernando Nicolas-Alonso

    2012-01-01

    Full Text Available 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.

  9. EEG Based Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Syed M. Saddique

    2009-08-01

    Full Text Available Brain-Computer Interface (BCI has added a new value to efforts being made under human machine interfaces. It has not only introduced new dimensions in machine control but the researchers round the globe are still exploring the possible uses of such applications. BCIs have given a hope where alternative communication channels can be created for the persons having severe motor disabilities. This work is based upon utilizing the brain signals of a human being via scalp Electroencephalography (EEG to get the control of a robot’s navigation which can be visualized as controlling one’s surrounding environment without physical strain. In this work when a person thinks of a motor activity, it gets performed. The procedure includes acquisition and analysis of brain signals via EEG equipment, development of a classification system using AI techniques and propagating the subsequent control signals to Lego-robot via parallel port. This has been depicted in [1] as a generic block diagram.

  10. Brain-Computer Interfacing for Intelligent Systems

    OpenAIRE

    Nijholt, Anton; Tan, Desney; Pfurtscheller, Gert; Brunner, Clemens; R. Millán, del, José; Allison, Brandan; Graimann, Bernhard; Florin POPESCU; Blankertz, Benjamin; Müller, Klaus-R

    2008-01-01

    Advances in cognitive neuroscience and brain-imaging technologies give us the unprecedented ability to interface directly with brain activity. These technologies let us monitor physical processes in the brain that correspond with certain forms of thought. Researchers have begun using these technologies to build brain-computer interfaces (BCIs)—communication systems that don't depend on the brain's normal output pathways of peripheral nerves and muscles. Four short articles provide a quick ove...

  11. Legal Aspects of Brain-Computer Interfaces

    Czech Academy of Sciences Publication Activity Database

    Krausová, Alžběta

    2014-01-01

    Roč. 8, č. 2 (2014). ISSN 1802-5951 Institutional support: RVO:68378122 Keywords : brain-computer interface * human rights * right to privacy, Subject RIV: AG - Legal Sciences http://mujlt.law.muni.cz/index.php

  12. The brain-computer interface cycle

    NARCIS (Netherlands)

    Gerven, M. van; Farquhar, J.D.R.; Schaefer, R.S.; Vlek, R.J.; Geuze, J.; Nijholt, A.; Ramsey, N.F.; Haselager, W.F.G.; Vuurpijl, L.G.; Gielen, S.C.A.M.; Desain, P.W.M.

    2009-01-01

    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 ail overview of the various steps in the BCI cycle, i.e., the loop from the measurement of b

  13. Brain Computer Interface for Virtual Reality Control

    OpenAIRE

    Guger, C; Groenegress, C.; Holzner, C.; Edlinger, G.; Slater, Mel; Sánchez-Vives, María Victoria

    2009-01-01

    A brain-computer interface (BCI) is a new communication channel between the human brain and a computer. Applications of BCI systems comprise the restoration of movements, communication and environmental control. In this study experiments were made that used the BCI system to control or to navigate in virtual environments (VE) just by thoughts. BCI experiments for navigation in VR were conducted so far with synchronous BCI and asynchronous BCI systems. The synchronous BCI analyzes the EEG patt...

  14. The brain-computer interface cycle

    OpenAIRE

    Van Gerven, M.; Farquhar, J.D.R.; Schaefer, R.S.; Vlek, R.J.; Geuze, J.; Nijholt, A.; Ramsey, N.F.; Haselager, W.F.G.; Vuurpijl, L.G.; Gielen, S.C.A.M.; Desain, P.W.M.

    2009-01-01

    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 ail 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 a...

  15. Brain-Computer Interface In Control Systems

    OpenAIRE

    Soukup, Michael

    2014-01-01

    A Brain-Computer Interface (BCI) is a system that allows for direct communication between the brain and an external device. Originally, the motivation for developing BCIs has been to provide severely disabled individuals with a basic communication system. Recent years, BCIs directed at regular consumers in practical control applications have gained popularity as well, for which the ultimate goal is to provide a more natural way of communicating with machines. However, BCIs intended at use in ...

  16. Brain-Computer Interfaces and Quantum Robots

    OpenAIRE

    Pessa, Eliano; Zizzi, Paola

    2009-01-01

    The actual (classical) Brain-Computer Interface attempts to use brain signals to drive suitable actuators performing the actions corresponding to subject's intention. However this goal is not fully reached, and when BCI works, it does only in particular situations. The reason of this unsatisfactory result is that intention cannot be conceived simply as a set of classical input-output relationships. It is therefore necessary to resort to quantum theory, allowing the occurrence of stable cohere...

  17. The Brain-Computer Interface Cycle

    NARCIS (Netherlands)

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

    2009-01-01

    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 br

  18. Brain Computer Interfaces, a Review

    OpenAIRE

    Coyle, Shirley; Ward, Tomas; Markham, Charles

    2003-01-01

    The brain is in many respects the centre of our being, controlling our actions, movements, thoughts and emotions. It is somewhat of a mystery, presenting itself only through the body's exterior façade. It is safeguarded by a thick skull that insulates it from the outside world. Information from the surroundings is relayed to it via the five senses - touch, sight, sound, smell and taste. Its role was underestimated in the past by cardiocentrists who believed that thought, sensation and behavio...

  19. Classification Methods for Brain-Computer Interface

    Czech Academy of Sciences Publication Activity Database

    Bobrov, P.; Frolov, A. A.; Húsek, Dušan

    Ostrava: VŠB - Technical University, 2011 - (Krátký, P.; Dvorský, J.; Moravec, P.), s. 314-319 ISBN 978-80-248-2449-9. [WOFEX 2011. Annual Workshop /9./. Ostrava (CZ), 08.09.2011-09.09.2011] Institutional research plan: CEZ:AV0Z10300504 Keywords : brain computer interface * BCI * EEG * Bayesian classifier * common spatial patterns * CSP * common tensor discriminant analysis * CTDA * classification accuracy Subject RIV: IN - Informatics, Computer Science

  20. Brain computer interface for operating a robot

    Science.gov (United States)

    Nisar, Humaira; Balasubramaniam, Hari Chand; Malik, Aamir Saeed

    2013-10-01

    A Brain-Computer Interface (BCI) is a hardware/software based system that translates the Electroencephalogram (EEG) signals produced by the brain activity to control computers and other external devices. In this paper, we will present a non-invasive BCI system that reads the EEG signals from a trained brain activity using a neuro-signal acquisition headset and translates it into computer readable form; to control the motion of a robot. The robot performs the actions that are instructed to it in real time. We have used the cognitive states like Push, Pull to control the motion of the robot. The sensitivity and specificity of the system is above 90 percent. Subjective results show a mixed trend of the difficulty level of the training activities. The quantitative EEG data analysis complements the subjective results. This technology may become very useful for the rehabilitation of disabled and elderly people.

  1. Competing and collaborating brains: multi-brain computer interfacing

    NARCIS (Netherlands)

    Nijholt, Anton; Hassanieu, Aboul Ella; Azar, Ahmad Taher

    2015-01-01

    In this chapter we survey the possibilities of brain-computer interface applications that assume two or more users, where at least one of the users’ brain activity is used as input to the application. Such ‘applications’ were already explored by artists who introduced artistic EEG applications in th

  2. Brain-Computer Interfaces and Quantum Robots

    CERN Document Server

    Pessa, Eliano

    2009-01-01

    The actual (classical) Brain-Computer Interface attempts to use brain signals to drive suitable actuators performing the actions corresponding to subject's intention. However this goal is not fully reached, and when BCI works, it does only in particular situations. The reason of this unsatisfactory result is that intention cannot be conceived simply as a set of classical input-output relationships. It is therefore necessary to resort to quantum theory, allowing the occurrence of stable coherence phenomena, in turn underlying high-level mental processes such as intentions and strategies. More precisely, within the context of a dissipative Quantum Field Theory of brain operation it is possible to introduce generalized coherent states associated, within the framework of logic, to the assertions of a quantum metalanguage. The latter controls the quantum-mechanical computing corresponding to standard mental operation. It thus become possible to conceive a Quantum Cyborg in which a human mind controls, through a qu...

  3. An Educative Brain-Computer Interface

    CERN Document Server

    Sorudeykin, Kirill A

    2010-01-01

    In this paper we will describe all necessary parts of Brain-Computer Interface (BCI), such as source of signals, hardware, software, analysis, architectures of complete system. We also will go along various applications of BCI, view some subject fields and their specifics. After preface we will consider the main point of this work-concepts of using BCI in education. Represented direction of BCI development has not been reported prior. In this work a computer system, currently being elaborated in author's laboratory, will be specified. A purpose of it is to determine a degree of clearness of studied information for certain user according to their indications of brain electrical signals. On the basis of this information the system is able to find an optimal approach to interact with each single user. Feedback individualization leads to learning effectiveness increasing. Stated investigations will be supplemented by author's analytical reasoning on the nature of thinking process.

  4. Brain Computer Interface Boulevard of Smarter Thoughts

    Directory of Open Access Journals (Sweden)

    Sumit Ghulyani

    2012-10-01

    Full Text Available The Brain Computer Interface is a major breakthrough for the technical industry, medical world, military and the society on a whole. It is concerned with the control of devices around us such as computing gears & even automobiles in the near future without really the physical intervention of the user. It helps bridge the communication gap between the society and the disabled. This mainly lays its focus on people suffering from brainstem stroke, going through a spinal cord injury or even blindness. BCI helps such patients to retain or restore communication with the outside world through intelligent signals from the brain due to the high risk of paralysis under such circumstances. This is achieved by a signal acquisition technique and converting these signals available from the sensors placed on the scalp into real-time computer commands that can be visually operated and understood. It has nothing to do with the natural neural transmission of brain signals but extracts them with the help of sensors to be processed and direct the outputs to an external device. This may also prove to be a major military gadget where troops may communicate their thoughts in highly stressed situations without breaking the hush. But, as every technology have some merits and demerits, so does BCI.

  5. Brain-Computer Interfaces : Beyond Medical Applications

    NARCIS (Netherlands)

    Erp, J.B.F. van; Lotte, F.; Tangermann, M.

    2012-01-01

    Brain-computer interaction has already moved from assistive care to applications such as gaming. Improvements in usability, hardware, signal processing, and system integration should yield applications in other nonmedical areas.

  6. Touch-based Brain Computer Interfaces: State of the art

    NARCIS (Netherlands)

    Erp, J.B.F. van; Brouwer, A.M.

    2014-01-01

    Brain Computer Interfaces (BCIs) rely on the user's brain activity to control equipment or computer devices. Many BCIs are based on imagined movement (called active BCIs) or the fact that brain patterns differ in reaction to relevant or attended stimuli in comparison to irrelevant or unattended stim

  7. Brain-Computer Interfaces Revolutionizing Human-Computer Interaction

    CERN Document Server

    Graimann, Bernhard; Allison, Brendan

    2010-01-01

    A brain-computer interface (BCI) establishes a direct output channel between the human brain and external devices. BCIs infer user intent via direct measures of brain activity and thus enable communication and control without movement. This book, authored by experts in the field, provides an accessible introduction to the neurophysiological and signal-processing background required for BCI, presents state-of-the-art non-invasive and invasive approaches, gives an overview of current hardware and software solutions, and reviews the most interesting as well as new, emerging BCI applications. The book is intended not only for students and young researchers, but also for newcomers and other readers from diverse backgrounds keen to learn about this vital scientific endeavour.

  8. A tactile P300 brain-computer interface

    NARCIS (Netherlands)

    Brouwer, A.M.; Erp, J.B.F. van

    2010-01-01

    De werking van de eerste Brain-Computer-Interface gebaseerd op tactiele EEG response wordt gedemonstreerd en het effect van het aantal gebruikte vibro-tactiele tactoren en stimulus-timing parameters wordt onderzocht

  9. fNIRS-based brain-computer interfaces: a review

    OpenAIRE

    Noman eNaseer; Keum-Shik eHong

    2015-01-01

    A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The mos...

  10. Brain-Computer Interfaces and Human-Computer Interaction

    NARCIS (Netherlands)

    Tan, Desney; Nijholt, Anton; Tan, Desney S.; Nijholt, Anton

    2010-01-01

    Advances in cognitive neuroscience and brain imaging technologies have started to provide us with the ability to interface directly with the human brain. This ability is made possible through the use of sensors that can monitor some of the physical processes that occur within the brain that correspo

  11. Control-display mapping in brain-computer interfaces

    NARCIS (Netherlands)

    Thurlings, M.E.; Erp, J.B.F. van; Brouwer, A.-M.; Blankertz, B.; Werkhoven, P.J.

    2012-01-01

    Event-related potential (ERP) based brain-computer interfaces (BCIs) employ differences in brain responses to attended and ignored stimuli. When using a tactile ERP-BCI for navigation, mapping is required between navigation directions on a visual display and unambiguously corresponding tactile stimu

  12. Brain-Computer Interfaces, Virtual Reality, and Videogames

    OpenAIRE

    Reilly, Richard

    2008-01-01

    PUBLISHED Major challenges must be tackled for brain-computer interfaces to mature into an established communications medium for VR applications, which will range from basic neuroscience studies to developing optimal peripherals and mental gamepads and more efficient brain-signal processing techniques.

  13. A Multi-purpose Brain-Computer Interface Output Device

    OpenAIRE

    Thompson, David E.; Huggins, Jane E

    2011-01-01

    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 inste...

  14. Hybrid Optical–Electrical Brain Computer Interfaces, Practices and Possibilities

    OpenAIRE

    Ward, Tomas

    2013-01-01

    In this chapter we present an overview of the area of electroencephalographyfunctional near infrared spectroscopy (EEG-fNIRS) measurement as an activity monitoring technology for brain computer interfacing applications. Our interest in this compound neural interfacing technology is motivated by a need for a motor cortical conditioning technology suitable for use in a neurorehabilitation setting [15, 50]. Specifically we seek BCI technology that allows a patient with a paretic ...

  15. Towards brain-computer music interfaces: progress and challenges

    OpenAIRE

    Miranda, E. R.; Durrant, Simon; Anders, T.

    2008-01-01

    Brain-Computer Music Interface (BCMI) is a new research area that is emerging at the cross roads of neurobiology,engineering sciences and music. This research involves three major challenging problems: the extraction of meaningful control information from signals emanating directly from the brain, the design of generative music techniques that respond to such information, and the training of subjects to use the system. We have implemented a proof-of-concept BCMI system that is able to use ...

  16. Brain-Computer Interface Games: Towards a Framework

    NARCIS (Netherlands)

    Gürkök, Hayrettin; Nijholt, Anton; Poel, Mannes; Nakatsu, Ryohei; Rauterberg, Matthias; Ciancarini, Paolo

    2015-01-01

    The brain-computer interface (BCI) community has started to consider games as potential applications, while the game community has started to consider BCI as a game controller. However, there is a discrepancy between the BCI games developed by the two communities. This not only adds to the workload

  17. Tutorial: Signal Processing in Brain-Computer Interfaces

    NARCIS (Netherlands)

    Garcia Molina, G.

    2010-01-01

    Research in Electroencephalogram (EEG) based Brain-Computer Interfaces (BCIs) has been considerably expanding during the last few years. Such an expansion owes to a large extent to the multidisciplinary and challenging nature of BCI research. Signal processing undoubtedly constitutes an essential co

  18. Editorial (to: Special issue on affective brain-computer interfaces)

    NARCIS (Netherlands)

    Nijholt, A.; Heylen, D.K.J.; Nijholt, A.; Heylen, D.K.J.

    2013-01-01

    The research in brain–computer interfaces (BCI) has shown that brain activity can be used as an active/voluntary or passive/involuntary control modality in man–machine interaction. Until recently, BCI research aimed almost solely at improving the life of disabled persons in need of communication and

  19. Robot Control Through Brain Computer Interface For Patterns Generation

    Science.gov (United States)

    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.

  20. Brain-Computer Interfaces: A Gentle Introduction

    Science.gov (United States)

    Graimann, Bernhard; Allison, Brendan; Pfurtscheller, Gert

    Stardate 3012.4: The U.S.S. Enterprise has been diverted from its original course to meet its former captain Christopher Pike on Starbase 11. When Captain Jim Kirk and his crew arrive, they find out that Captain Pike has been severely crippled by a radiation accident. As a consequence of this accident Captain Pike is completely paralyzed and confined to a wheelchair controlled by his brain waves. He can only communicate through a light integrated into his wheelchair to signal the answers "yes" or "no". Commodore Mendez, the commander of Starbase 11, describes the condition of Captain Pike as follows: "He is totally unable to move, Jim. His wheelchair is constructed to respond to his brain waves. He can turn it, move it forwards, backwards slightly. Through a flashing light he can say 'yes' or 'no'. But that's it, Jim. That is as much as the poor ever can do. His mind is as active as yours and mine, but it's trapped in a useless vegetating body. He's kept alive mechanically. A battery driven heart. …"

  1. A brain-computer interface to support functional recovery

    DEFF Research Database (Denmark)

    Kjaer, Troels W; Sørensen, Helge Bjarup Dissing

    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...

  2. Wyrm, A Pythonic Toolbox for Brain-Computer Interfacing

    OpenAIRE

    Venthur, Bastian; Blankertz, Benjamin

    2014-01-01

    A Brain-Computer Interface (BCI) is a system that measures central nervous system activity and translates the recorded data into an output suitable for a computer to use as an input signal. Such a BCI system consists of three parts, the signal acquisition, the signal processing and the feedback/stimulus presentation. In this paper we present Wyrm, a signal processing toolbox for BCI in Python. Wyrm is applicable to a broad range of neuroscientific problems and capable for running online exper...

  3. Perspectives and Potential of the Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    MUSSATTO, G. G.

    2014-06-01

    Full Text Available A Brain-Computer Interface (BCI, also known as Brain-Machine Interface, is a system that allows for the interaction between the user and its surroundings using control signals generated by his brain activity. The improvement of the research on BCI correlates mainly with the advances of Neurophisiology and Computer Science. Initial research was dedicated to the development of devices for the communication of individuals who lost voluntary muscle control but had no cognitive impairment. Nowadays, we find applications in the fields of mobility, communication and the treatment of diseases of user who may or may not have movement impairment. Considering the expansion scenario of the BCI applications, this paper presents a pedagogical description of the recent publication on this field of study. Hence, we descrive the basic concepts related to this research area, as well as some of its applications and limitations.

  4. Advances in Neurotechnology for Brain Computer Interfaces

    OpenAIRE

    Fazli, Siamac

    2011-01-01

    Gehirn Computer Schnittstellen haben in den letzten 10 Jahren ein enormes wissenschaftliches Interesse hervorgerufen. Allerdings offenbart diese spannende Technology bei näherer Betrachtung noch einige Hürden, welche bisher die Entwicklung von massentauglichen Anwendungen verhindert haben. Unter Anderem eine lange Vorbereitungszeit eines BCI Systems, die fehlende Steuermöglichkeiten für manche Benutzer, sowie die nicht Stationaritäten innerhalb einer Aufnahme. Diese Dissertation führt eine Re...

  5. Probabilistic co-adaptive brain-computer interfacing

    Science.gov (United States)

    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

  6. Brain-computer interfaces for patients with disorders of consciousness.

    Science.gov (United States)

    Gibson, R M; Owen, A M; Cruse, D

    2016-01-01

    The disorders of consciousness refer to clinical conditions that follow a severe head injury. Patients diagnosed as in a vegetative state lack awareness, while patients diagnosed as in a minimally conscious state retain fluctuating awareness. However, it is a challenge to accurately diagnose these disorders with clinical assessments of behavior. To improve diagnostic accuracy, neuroimaging-based approaches have been developed to detect the presence or absence of awareness in patients who lack overt responsiveness. For the small subset of patients who retain awareness, brain-computer interfaces could serve as tools for communication and environmental control. Here we review the existing literature concerning the sensory and cognitive abilities of patients with disorders of consciousness with respect to existing brain-computer interface designs. We highlight the challenges of device development for this special population and address some of the most promising approaches for future investigations. PMID:27590972

  7. Virtual reality and brain computer interface in neurorehabilitation

    OpenAIRE

    Salisbury, David B.; 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 s...

  8. Collaborative Brain-Computer Interface for Aiding Decision-Making

    OpenAIRE

    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 i...

  9. A Collaborative Brain-Computer Interface for Improving Human Performance

    OpenAIRE

    Wang, Yijun; Jung, Tzyy-Ping

    2011-01-01

    Electroencephalogram (EEG) based brain-computer interfaces (BCI) have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world prac...

  10. BRAIN-COMPUTER INTERFACE (BCI) LITERATURE - A BIBLIOMETRIC STUDY

    OpenAIRE

    Hamadicharef, Brahim

    2010-01-01

    Brain-Computer Interface (BCI) is a relatively young research field which has seen a growing interest with associated number of publications over the last two decades. In this study we present the first bibliometric analysis of the BCI literature (1990–2008) from the Thomson Reuters's Institute for Scientific Information (ISI) Web of Knowledge. Thus, the main objectives of this bibliometric study are: 1) to explore the growth of BCI literature, 2) to assess if it follows Lotka's law of scient...

  11. Robot Animals Based on Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    Yang Xia; Lei Lei; Tie-Jun Liu; De-Zhong Yao

    2009-01-01

    The study of robot animals based on brain-computer interface (BCI) technology is an important field in robots and neuroscience at present.In this paper,the development status at home and abroad of the motion control of robot based on BCI and principle of robot animals are introduced,then a new animals' behavior control method by photostimulation is presented.At last,the application prospect is provided.

  12. Detecting Consciousness with a Brain-computer Interface

    OpenAIRE

    Noirhomme, Quentin; Lesenfants, Damien; Lehembre, Remy; Lugo, Zulay; Chatelle, Camille; Vanhaudenhuyse, Audrey; Laureys, Steven

    2012-01-01

    Recent electrophysiological and neuroimaging studies showed command-specific changes in EEG or fMRI signals of unresponsive patients providing motor-independent evidence of conscious thoughts. These promising results have paved the way for a new application for Brain-computer Interface (BCI): detecting consciousness in patients with disorders of consciousness (DOC). In the present abstract, we review the first results obtained by BCI-like applications in patients with DOC and discuss the chal...

  13. Brain-computer interfacing in disorders of consciousness.

    OpenAIRE

    Chatelle, Camille; Chennu, Srivas; Noirhomme, Quentin; Cruse, Damian; Owen, Adrian M.; Laureys, Steven

    2012-01-01

    Background: Recent neuroimaging research has strikingly demonstrated the existence of covert awareness in some patients with disorders of consciousness (DoC). These findings have highlighted the potential for the development of simple brain-computer interfaces (BCI) as a diagnosis in behaviourally unresponsive patients. Objectives: This study here reviews current EEG-based BCIs that hold potential for assessing and eventually assisting patients with DoC. It highlights key areas for further de...

  14. Effect of mindfulness meditation on brain-computer interface performance

    OpenAIRE

    Tan, Lee-Fan; Dienes, Zoltan; Jansari, Ashok S.; Goh, Sing-Yau

    2014-01-01

    Electroencephalogram based Brain-Computer Interfaces (BCIs) enable stroke and motor neuron disease patients to communicate and control devices. Mindfulness meditation has been claimed to enhance metacognitive regulation. The current study explores whether mindfulness meditation training can thus improve the performance of BCI users. To eliminate the possibility of expectation of improvement influencing the results, we introduced a music training condition. A norming study found that both...

  15. Brain Computer Interface. Comparison of Neural Networks Classifiers.

    OpenAIRE

    Martínez Pérez, Jose Luis; Barrientos Cruz, Antonio

    2008-01-01

    Brain Computer Interface is an emerging technology that allows new output paths to communicate the user’s intentions without use of normal output ways, such as muscles or nerves (Wolpaw, J. R.; et al., 2002).In order to obtain its objective BCI devices shall make use of classifier which translate the inputs provided by user’s brain signal to commands for external devices. The primary uses of this technology will benefit persons with some kind blocking disease as for example: ALS, brainstem st...

  16. Connections that Count: Brain-Computer Interface Enables the Profoundly Paralyzed to Communicate

    Science.gov (United States)

    ... Home Current Issue Past Issues Connections that Count: Brain-Computer Interface Enables the Profoundly Paralyzed to Communicate Past Issues / ... page please turn Javascript on. A brain-computer interface (BCI) system This brain-computer interface (BCI) system assists patients who are ...

  17. Brain-Computer Interface Users Speak Up: The Virtual Users' Forum at the 2013 International Brain-Computer Interface Meeting

    OpenAIRE

    Peters, B; Bieker, G; Heckman, SM; Huggins, JE; Wolf, C.; Zeitlin, D; Fried-Oken, M.

    2015-01-01

    Over 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 two BCI groups. Online surveys and in-person interviews were used to solicit feedback from people wi...

  18. Implants and Decoding for Intracortical Brain Computer Interfaces

    OpenAIRE

    Homer, Mark L.; Nurmikko, Arto V.; Donoghue, John P.; Hochberg, Leigh R.

    2013-01-01

    Intracortical brain computer interfaces (iBCIs) are being developed to enable a person to drive an output device, such as a computer cursor, directly from their neural activity. One goal of the technology is to help people with severe paralysis or limb loss. Key elements of an iBCI are the implanted sensor that records the neural signals and the software which decodes the user’s intended movement from those signals. Here, we focus on recent advances in these two areas, with special attention ...

  19. Comparison of four classification methods for brain-computer interface

    Czech Academy of Sciences Publication Activity Database

    Frolov, A.; Húsek, Dušan; Bobrov, P.

    2011-01-01

    Roč. 21, č. 2 (2011), s. 101-115. ISSN 1210-0552 R&D Projects: GA MŠk(CZ) 1M0567; GA ČR GA201/05/0079; GA ČR GAP202/10/0262 Institutional research plan: CEZ:AV0Z10300504 Keywords : brain computer interface * motor imagery * visual imagery * EEG pattern classification * Bayesian classification * Common Spatial Patterns * Common Tensor Discriminant Analysis Subject RIV: IN - Informatics, Computer Science Impact factor: 0.646, year: 2011

  20. Towards next generation human-computer interaction -- brain-computer interfaces: applications and challenges

    OpenAIRE

    Yang, Yuan; Wiart, Joe; Bloch, Isabelle

    2013-01-01

    Brain-computer interfaces (BCIs) are systems that record brain signals and transfer them into commands to build a direct communication pathway between a human brain and a computer. Decades of development make BCI a promising tool for next generation human-computer interaction (HCI). This paper briefly discusses one of its applications in the HCI field and some key challenges for its widespread adoption.

  1. Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

    International Nuclear Information System (INIS)

    The Brain Computer Interface (BCI) translates brain activity into computer commands. To increase the performance of the BCI, to decode the user intentions it is necessary to get better the feature extraction and classification techniques. In this article the performance of a three linear discriminant analysis (LDA) classifiers ensemble is studied. The system based on ensemble can theoretically achieved better classification results than the individual counterpart, regarding individual classifier generation algorithm and the procedures for combine their outputs. Classic algorithms based on ensembles such as bagging and boosting are discussed here. For the application on BCI, it was concluded that the generated results using ER and AUC as performance index do not give enough information to establish which configuration is better.

  2. A brain-computer interface to support functional recovery.

    Science.gov (United States)

    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. PMID:23859968

  3. Brain Computer Interface Enhancement by Independent Component Analysis

    Czech Academy of Sciences Publication Activity Database

    Bobrov, P.; Frolov, A. A.; Húsek, Dušan

    Heidelberg: Springer, 2013 - (Kudělka, M.; Pokorný, J.; Snášel, V.; Abraham, A.), s. 51-60. (Advances in Intelligent Systems and Computing. 179). ISBN 978-3-642-31602-9. ISSN 2194-5357. [IHCI 2011. International Conference on Intelligent Human Computer Interaction /3./. Prague (CZ), 29.08.2011-31.08.2011] R&D Projects: GA ČR GAP202/10/0262; GA ČR GA205/09/1079 Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070 Institutional research plan: CEZ:AV0Z10300504 Keywords : brain computer interface * EEG patterns classiffication * independent component analysis * classification accuracy * m-rythm identification Subject RIV: IN - Informatics, Computer Science

  4. Virtual reality and brain computer interface in neurorehabilitation.

    Science.gov (United States)

    Salisbury, David B; Dahdah, Marie; Driver, Simon; Parsons, Thomas D; Richter, Kathleen M

    2016-04-01

    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

  5. Hacking the brain: Brain-computer interfacing technology and the ethics of neurosecurity

    NARCIS (Netherlands)

    Ienca, M.; Haselager, W.F.G.

    2016-01-01

    Brain-computer interfacing technologies are used as assistive technologies for patients as well as healthy subjects to control devices solely by brain activity. Yet the risks associated with the misuse of these technologies remain largely unexplored. Recent findings have shown that BCIs are potentia

  6. Using brain-computer interface to steer a humanoid robot

    OpenAIRE

    Gergondet P.; Druon S.; Kheddar A.; Hintermuller C.; Guger C.; Slater M.

    2011-01-01

    This work presents the use of a brain-computer interface (BCI) system in order to steer a humanoid robot. We aim at designing a ready-to-use system allowing the user to manipulate the robot in an unknown environment. This design induces some constraints on the BCI system design that we present in this paper. Given these constraints, two steering paradigms based upon the steady-state visually evoked potentials (SSVEP) phenomenon are proposed, implemented on the HRP-2 humanoid robot, and compar...

  7. Probabilistic Methods in Multi-Class Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    Ping Yang; Xu Lei; Tie-Jun Liu; Peng Xu; De-Zhong Yao

    2009-01-01

    Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface (BCI):support vector machine (SVM) with posteriori probability (PSVM) and Bayesian linear dis-criminant analysis with probabilistic output (PBLDA).A comparative evaluation of these two methods is conducted.The results shows that:1) probabilistic information can improve the performance of BCI for subjects with high kappa coefficient,and 2) PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.

  8. Encoder-decoder optimization for brain-computer interfaces.

    Directory of Open Access Journals (Sweden)

    Josh Merel

    2015-06-01

    Full Text Available 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.

  9. Brain-computer interfaces current trends and applications

    CERN Document Server

    Azar, Ahmad

    2015-01-01

    The success of a BCI system depends as much on the system itself as on the user’s ability to produce distinctive EEG activity. BCI systems can be divided into two groups according to the placement of the electrodes used to detect and measure neurons firing in the brain. These groups are: invasive systems, electrodes are inserted directly into the cortex are used for single cell or multi unit recording, and electrocorticography (EcoG), electrodes are placed on the surface of the cortex (or dura); noninvasive systems, they are placed on the scalp and use electroencephalography (EEG) or magnetoencephalography (MEG) to detect neuron activity. The book is basically divided into three parts. The first part of the book covers the basic concepts and overviews of Brain Computer Interface. The second part describes new theoretical developments of BCI systems. The third part covers views on real applications of BCI systems.

  10. An Exploration on Brain Computer Interface and Its Recent Trends

    Directory of Open Access Journals (Sweden)

    T. Kameswara Rao

    2012-11-01

    Full Text Available Detailed exploration on Brain Computer Interface (BCI and its recent trends has been done in this paper. Work is being done to identify objects, images, videos and their color compositions. Efforts are on the way in understanding speech, words, emotions, feelings and moods. When humans watch the surrounding environment, visual data is processed by the brain, and it is possible to reconstruct the same on the screen with some appreciable accuracy by analyzing the physiological data. This data is acquired by using one of the non-invasive techniques like electroencephalography (EEG in BCI. The acquired signal is to be translated to produce the image on to the screen. This paper also lays suitable directions for future work.

  11. fNIRS-based brain-computer interfaces: a review.

    Science.gov (United States)

    Naseer, Noman; Hong, Keum-Shik

    2015-01-01

    A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis (ICA), multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine (SVM), hidden Markov model (HMM), artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips. PMID:25674060

  12. fNIRS-based brain-computer interfaces: a review

    Directory of Open Access Journals (Sweden)

    Noman eNaseer

    2015-01-01

    Full Text Available A brain-computer interface (BCI is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis, multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine, hidden Markov model, artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.

  13. Collaborative brain-computer interface for aiding decision-making.

    Directory of Open Access Journals (Sweden)

    Riccardo Poli

    Full Text Available 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.

  14. TOPICAL REVIEW: The brain-computer interface cycle

    Science.gov (United States)

    Gerven, Marcel van; 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.

  15. [Brain-Computer Interface: the First Clinical Experience in Russia].

    Science.gov (United States)

    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). PMID:27188145

  16. Papers from the Fifth International Brain-Computer Interface Meeting

    Science.gov (United States)

    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

  17. Brain-Computer Interface Based on Motor Imagery: the Most Relevant Sources of Electrical Brain Activity

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Snášel, V.; Bobrov, P.; Mokienko, O.; Tintěra, J.; Rydlo, J.

    Cham: Springer, 2014 - (Snášel, V.; Krömer, P.; Köppen, M.; Schaefer, G.), s. 153-163. (Advances in Intelligent Systems and Computing. 223). ISBN 978-3-319-00929-2. ISSN 2194-5357. [Online World Conference on Soft Computing in Industrial Applications /17./. Anywhere on Earth, 10.12.2012-21.12.2012)] Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070; GA MŠk(CZ) EE.2.3.20.0073 Institutional support: RVO:67985807 Keywords : Image and Signal Processing * Brain-Computer Interface * Independent Component Analysis * EEG Pattern Classification * fMRI * Motor Image ry * Pattern Recognition Subject RIV: IN - Informatics, Computer Science http://dap.vsb.cz/wsc17conf/brain-computer-interface-based-on-motor- image ry---the-most-relevant-sources-of-electrical-brain-activity

  18. EEG processing and its application in brain-computer interface

    Institute of Scientific and Technical Information of China (English)

    Wang Jing; Xu Guanghua; Xie Jun; Zhang Feng; Li Lili; Han Chengcheng; Li Yeping; Sun Jingjing

    2013-01-01

    Electroencephalogram (EEG) is an efficient tool in exploring human brains.It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI).The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI.First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed.Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton' s rings are introduced.Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated.As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.

  19. A bidirectional brain-computer interface for effective epilepsy control

    Institute of Scientific and Technical Information of China (English)

    Yu QI; Fei-qiang MA; Ting-ting GE; Yue-ming WANG; Jun-ming ZHU; Jian-min ZHANG; Xiao-xiang ZHENG; Zhao-hui WU

    2014-01-01

    Brain-computer interfaces (BCIs) can provide direct bidirectional communication between the brain and a machine. Recently, the BCI technique has been used in seizure control. Usually, a closed-loop system based on BCI is set up which delivers a therapic electrical stimulus only in response to seizure onsets. In this way, the side effects of neurostimulation can be greatly reduced. In this paper, a new BCI-based responsive stimulation system is proposed. With an efficient morphology-based seizure detector, seizure events can be identifi ed in the early stages which trigger electrical stimulations to be sent to the cortex of the brain. The proposed system was tested on rats with penicillin-induced epileptic seizures. Online experiments show that 83%of the seizures could be detected successfully with a short average time delay of 3.11 s. With the therapy of the BCI-based seizure control system, most seizures were suppressed within 10 s. Compared with the control group, the average seizure duration was reduced by 30.7%. Therefore, the proposed system can control epileptic seizures effectively and has potential in clinical applications.

  20. [Artificial Feedback for Invasive Brain-Computer Interfaces].

    Science.gov (United States)

    Badakva, A M; Miller, N V; Zobova, L N

    2016-01-01

    During the last two decades, considerable progress has been made in the studies of brain-computer interfaces (BCIs)--devices in which motor signals from the brain are registered by multi-electrode arrays and transformed into commands for articial actuators such as cursors and robotic devices. This review is focused on one problem. Voluntary motor control is based on neurophysiological processes which depend heavily on the afferent innervation of skin, muscles and joints. Thus, invasive BCI has to be based on a bidirectional system in which motor control signals are registered by multi-channel micro-electrodes implanted in motor areas, while tactile, proprioceptive and other useful signals are transported back to the brain through spatial-temporal patterns of intracortical microstimulation (ICMS) delivered to sensory areas. In general, the studies of invasive BCIs have advanced in several directions. The progress of BCIs with articial sensory feedback will not only help patients, but will also expand knowledge base in the field of human cortical functions. PMID:27188155

  1. A Review of Hybrid Brain-Computer Interface Systems

    Directory of Open Access Journals (Sweden)

    Setare Amiri

    2013-01-01

    Full Text Available Increasing number of research activities and different types of studies in brain-computer interface (BCI systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

  2. Brain-computer interfaces and disability: extending embodiment, reducing stigma?

    Science.gov (United States)

    Aas, Sean; Wasserman, David

    2016-01-01

    Brain-Computer Interfaces (BCIs) now enable an individual without limb function to "move" a detached mechanical arm to perform simple actions, such as feeding herself. This technology may eventually offer almost everyone a way to move objects at a distance, by exercising cognitive control of a mechanical device. At that point, BCIs may be seen less as an assistive technology for disabled people, and more as a tool, like the internet, which can benefit all users. We will argue that BCIs will have a significant but uncertain impact on attitudes toward disabilities and on norms of bodily form and function. It may be liberating, oppressive, or both. Its impact, we argue, will depend - though not in any simple way - on whether BCIs come to be seen as parts of the body itself or as external tools. PMID:26336895

  3. Improved Classification Methods for Brain Computer Interface System

    Directory of Open Access Journals (Sweden)

    YI Fang

    2012-03-01

    Full Text Available Brain computer interface (BCI aims at providing a new communication way without brain’s normal output through nerve and muscle. The electroencephalography (EEG has been widely used for BCI system because it is a non-invasive approach. For the EEG signals of left and right hand motor imagery, the event-related desynchronization (ERD and event-related synchronization(ERS are used as classification features in this paper. The raw data are transformed by nonlinear methods and classified by Fisher classifier. Compared with the linear methods, the classification accuracy can get an obvious increase to 86.25%. Two different nonlinear transform were arised and one of them is under the consideration of the relativity of two channels of EEG signals. With these nonlinear transform, the performance are also stable with the balance of two misclassifications.

  4. Design of an EEG Preamplifier for Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    Xian-Jie Pu; Tie-Jun Liu; De-Zhong Yao

    2009-01-01

    As a non-invasive neurophysiological index for brain-computer interface (BCI),electro-encephalogram (EEG) attracts much attention at present.In order to have a portable BCI,a simple and efficient pre-amplifier is crucial in practice.In this work,a preamplifier based on the characteristics of EEG signals is designed,which consists of a highly symmetrical input stage,low-pass filter,50 Hz notch filter and a post amplifier.A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment.The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.

  5. Bayesian learning in assisted brain-computer interface tasks.

    Science.gov (United States)

    Zhang, Yin; Schwartz, Andrew B; Chase, Steve M; Kass, Robert E

    2012-01-01

    Successful implementation of a brain-computer interface depends critically on the subject's ability to learn how to modulate the neurons controlling the device. However, the subject's learning process is probably the least understood aspect of the control loop. How should training be adjusted to facilitate dexterous control of a prosthetic device? An effective training schedule should manipulate the difficulty of the task to provide enough information to guide improvement without overwhelming the subject. In this paper, we introduce a bayesian framework for modeling the closed-loop BCI learning process that treats the subject as a bandwidth-limited communication channel. We then develop an adaptive algorithm to find the optimal difficulty-schedule for performance improvement. Simulation results demonstrate that our algorithm yields faster learning rates than several other heuristic training schedules, and provides insight into the factors that might affect the learning process. PMID:23366492

  6. Proprioceptive feedback and brain computer interface (BCI based neuroprostheses.

    Directory of Open Access Journals (Sweden)

    Ander Ramos-Murguialday

    Full Text Available Brain computer interface (BCI technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1 motor imagery of the hand movement without any overt movement and without feedback, (2 motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3 passive (the orthosis passively opens and closes the hand without imagery and (4 active (overt movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants. Group 1 (n = 9 received contingent positive feedback (participants' sensorimotor rhythm (SMR desynchronization was directly linked to hand orthosis movements, group 2 (n = 8 contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements and group 3 (n = 7 sham feedback (no link between brain oscillations and orthosis movements. We observed that proprioceptive feedback (feeling and seeing hand movements improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and

  7. User-customized brain computer interfaces using Bayesian optimization

    Science.gov (United States)

    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.

  8. An online semi-supervised brain-computer interface.

    Science.gov (United States)

    Gu, Zhenghui; Yu, Zhuliang; Shen, Zhifang; Li, Yuanqing

    2013-09-01

    Practical brain-computer interface (BCI) systems should require only low training effort for the user, and the algorithms used to classify the intent of the user should be computationally efficient. However, due to inter- and intra-subject variations in EEG signal, intermittent training/calibration is often unavoidable. In this paper, we present an online semi-supervised P300 BCI speller system. After a short initial training (around or less than 1 min in our experiments), the system is switched to a mode where the user can input characters through selective attention. In this mode, a self-training least squares support vector machine (LS-SVM) classifier is gradually enhanced in back end with the unlabeled EEG data collected online after every character input. In this way, the classifier is gradually enhanced. Even though the user may experience some errors in input at the beginning due to the small initial training dataset, the accuracy approaches that of fully supervised method in a few minutes. The algorithm based on LS-SVM and its sequential update has low computational complexity; thus, it is suitable for online applications. The effectiveness of the algorithm has been validated through data analysis on BCI Competition III dataset II (P300 speller BCI data). The performance of the online system was evaluated through experimental results on eight healthy subjects, where all of them achieved the spelling accuracy of 85 % or above within an average online semi-supervised learning time of around 3 min. PMID:23674410

  9. Language Model Applications to Spelling with Brain-Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Anderson Mora-Cortes

    2014-03-01

    Full Text Available 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.

  10. Modern Electrophysiological Methods for Brain-Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Rolando Grave de Peralta Menendez

    2007-01-01

    Full Text Available Modern electrophysiological studies in animals show that the spectrum of neural oscillations encoding relevant information is broader than previously thought and that many diverse areas are engaged for very simple tasks. However, EEG-based brain-computer interfaces (BCI still employ as control modality relatively slow brain rhythms or features derived from preselected frequencies and scalp locations. Here, we describe the strategy and the algorithms we have developed for the analysis of electrophysiological data and demonstrate their capacity to lead to faster accurate decisions based on linear classifiers. To illustrate this strategy, we analyzed two typical BCI tasks. (1 Mu-rhythm control of a cursor movement by a paraplegic patient. For this data, we show that although the patient received extensive training in mu-rhythm control, valuable information about movement imagination is present on the untrained high-frequency rhythms. This is the first demonstration of the importance of high-frequency rhythms in imagined limb movements. (2 Self-paced finger tapping task in three healthy subjects including the data set used in the BCI-2003 competition. We show that by selecting electrodes and frequency ranges based on their discriminative power, the classification rates can be systematically improved with respect to results published thus far.

  11. P300 brain computer interface: current challenges and emerging trends

    Directory of Open Access Journals (Sweden)

    Reza Fazel-Rezai

    2012-07-01

    Full Text Available 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 event-related potential (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.

  12. EEG-Based Brain-Computer Interface for Tetraplegics

    Directory of Open Access Journals (Sweden)

    Laura Kauhanen

    2007-09-01

    Full Text Available Movement-disabled persons typically require a long practice time to learn how to use a brain-computer interface (BCI. Our aim was to develop a BCI which tetraplegic subjects could control only in 30 minutes. Six such subjects (level of injury C4-C5 operated a 6-channel EEG BCI. The task was to move a circle from the centre of the computer screen to its right or left side by attempting visually triggered right- or left-hand movements. During the training periods, the classifier was adapted to the user's EEG activity after each movement attempt in a supervised manner. Feedback of the performance was given immediately after starting the BCI use. Within the time limit, three subjects learned to control the BCI. We believe that fast initial learning is an important factor that increases motivation and willingness to use BCIs. We have previously tested a similar single-trial classification approach in healthy subjects. Our new results show that methods developed and tested with healthy subjects do not necessarily work as well as with motor-disabled patients. Therefore, it is important to use motor-disabled persons as subjects in BCI development.

  13. Brain-computer interface control along instructed paths

    Science.gov (United States)

    Sadtler, P. T.; Ryu, S. I.; Tyler-Kabara, E. C.; Yu, B. M.; Batista, A. P.

    2015-02-01

    Objective. Brain-computer interfaces (BCIs) are being developed to assist paralyzed people and amputees by translating neural activity into movements of a computer cursor or prosthetic limb. Here we introduce a novel BCI task paradigm, intended to help accelerate improvements to BCI systems. Through this task, we can push the performance limits of BCI systems, we can quantify more accurately how well a BCI system captures the user’s intent, and we can increase the richness of the BCI movement repertoire. Approach. We have implemented an instructed path task, wherein the user must drive a cursor along a visible path. The instructed path task provides a versatile framework to increase the difficulty of the task and thereby push the limits of performance. Relative to traditional point-to-point tasks, the instructed path task allows more thorough analysis of decoding performance and greater richness of movement kinematics. Main results. We demonstrate that monkeys are able to perform the instructed path task in a closed-loop BCI setting. We further investigate how the performance under BCI control compares to native arm control, whether users can decrease their movement variability in the face of a more demanding task, and how the kinematic richness is enhanced in this task. Significance. The use of the instructed path task has the potential to accelerate the development of BCI systems and their clinical translation.

  14. An optimal spatial filtering electrode for brain computer interface.

    Science.gov (United States)

    Besio, W G; Kay, S M; Liu, X

    2009-01-01

    There are millions of people in the U.S. and many more worldwide who could benefit from a noninvasive-based electroencephalography (EEG) brain computer interface (BCI). A BCI is an alternative or augmentative communication method for people with severe motor disabilities. However, EEG suffers from poor spatial resolution and signal-to-noise ratio (SNR). To improve the spatial resolution and SNR many researchers have turned to implantable electrodes. We have previously reported on significant improvements in BCI recognition rates using tripolar concentric ring electrodes compared to disc electrodes. We now report on a optimal method for combining the outputs from the independent elements of the tripolar concentric ring electrodes to improve the spatial resolution further. We used minimum variance distortionless look (MVDL), a beamformer, on simulated data to compare the spatial sensitivity of the optimal combination to disc electrodes and the tripolar concentric ring electrode surface Laplacian. The optimal combination shows the highest spatial sensitivity with the Laplacian a close second and disc electrodes resulting in a distant third. Further analysis is necessary with a more realistic computer model and then real signals. however it appears that the optimal combination may improve the spatial resolution of EEG further which in turn can be utilized to improve noninvasive EEG-based BCIs. PMID:19963573

  15. A hypothesis of brain-to-brain coupling in interactive new media art and games using brain-computer interfaces

    OpenAIRE

    Zioga, Polina; Chapman, Paul; Ma, Minhua; Pollick, Frank

    2015-01-01

    Interactive new media art and games belong to distinctive fields, but nevertheless share common grounds, tools, methodologies, challenges, and goals, such as the use of applications and devices for engaging multiple participants and players, and more recently electroencephalography (EEG)-based brain-computer interfaces (BCIs). At the same time, an increasing number of new neuroscientific studies explore the phenomenon of brain-to-brain coupling, the dynamics and processes of the interaction a...

  16. Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology

    Institute of Scientific and Technical Information of China (English)

    Hongyu Sun; Yang Xiang; Mingdao Yang

    2011-01-01

    The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment (P < 0.01), which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.

  17. Papers from the Fifth International Brain-Computer Interface Meeting

    Science.gov (United States)

    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

  18. A collaborative brain-computer interface for improving human performance.

    Directory of Open Access Journals (Sweden)

    Yijun Wang

    Full Text Available Electroencephalogram (EEG based brain-computer interfaces (BCI have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world practices due to technical limits of EEG. To overcome the bottleneck of low single-user BCI performance, this study proposes a collaborative paradigm to improve overall BCI performance by integrating information from multiple users. To test the feasibility of a collaborative BCI, this study quantitatively compares the classification accuracies of collaborative and single-user BCI applied to the EEG data collected from 20 subjects in a movement-planning experiment. This study also explores three different methods for fusing and analyzing EEG data from multiple subjects: (1 Event-related potentials (ERP averaging, (2 Feature concatenating, and (3 Voting. In a demonstration system using the Voting method, the classification accuracy of predicting movement directions (reaching left vs. reaching right was enhanced substantially from 66% to 80%, 88%, 93%, and 95% as the numbers of subjects increased from 1 to 5, 10, 15, and 20, respectively. Furthermore, the decision of reaching direction could be made around 100-250 ms earlier than the subject's actual motor response by decoding the ERP activities arising mainly from the posterior parietal cortex (PPC, which are related to the processing of visuomotor transmission. Taken together, these results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve the overall performance of natural human behavior.

  19. Real-time brain computer interface using imaginary movements

    DEFF Research Database (Denmark)

    El-Madani, Ahmad; Sørensen, Helge Bjarup Dissing; Kjær, Troels W.;

    2015-01-01

    Background: Brain Computer Interface (BCI) is the method of transforming mental thoughts and imagination into actions. A real-time BCI system can improve the quality of life of patients with severe neuromuscular disorders by enabling them to communicate with the outside world. In this paper, the ...... (CAs > 75 %). The overall mean on-line accuracy was found to be 80%. Conclusions: The subject-specific settings applied on the feedback systems have resulted in the development of a successful real-time BCI system with high accuracies...... implementation of a 2-class real-time BCI system based on the event related desynchronization (ERD) of the sensorimotor rhythms (SMR) is described. Methods: Off-line measurements were conducted on 12 healthy test subjects with 3 different feedback systems (cross, basket and bars). From the collected...... classifier (BLC) was developed and used for signal classification. These three subject-specific settings were preserved for the on-line experiments with the same feedback systems. Results: Six of the 12 subjects were qualified for the on-line experiments based on their high off-line classification accuracies...

  20. Self-recalibrating classifiers for intracortical brain-computer interfaces

    Science.gov (United States)

    Bishop, William; Chestek, Cynthia C.; Gilja, Vikash; Nuyujukian, Paul; Foster, Justin D.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

    2014-04-01

    Objective. Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers). Approach. We recorded threshold crossings from 96-electrode arrays implanted in the motor cortex of two rhesus macaques performing center-out reaches in 7 directions over 41 and 36 separate days spanning 48 and 58 days in total for offline analysis. Main results. We show that for the purposes of developing a self-recalibrating classifier, tuning parameters can be considered as fixed within days and that parameters on the same electrode move up and down together between days. Further, drift is constrained across time, which is reflected in the performance of a standard classifier which does not progressively worsen if it is not retrained daily, though overall performance is reduced by more than 10% compared to a daily retrained classifier. Two novel self-recalibrating classifiers produce a \\mathord {\\sim }15% increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier. Significance. We believe that the development of classifiers that require no daily retraining will accelerate the clinical translation of BCI systems. Future work should test these results in a closed-loop setting.

  1. Vibrotactile Feedback for Brain-Computer Interface Operation

    Directory of Open Access Journals (Sweden)

    Febo Cincotti

    2007-01-01

    Full Text Available 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.

  2. Stationary common spatial patterns for brain-computer interfacing

    Science.gov (United States)

    Samek, Wojciech; Vidaurre, Carmen; Müller, Klaus-Robert; Kawanabe, Motoaki

    2012-04-01

    Classifying motion intentions in brain-computer interfacing (BCI) is a demanding task as the recorded EEG signal is not only noisy and has limited spatial resolution but it is also intrinsically non-stationary. The non-stationarities in the signal may come from many different sources, for instance, electrode artefacts, muscular activity or changes of task involvement, and often deteriorate classification performance. This is mainly because features extracted by standard methods like common spatial patterns (CSP) are not invariant to variations of the signal properties, thus should also change over time. Although many extensions of CSP were proposed to, for example, reduce the sensitivity to noise or incorporate information from other subjects, none of them tackles the non-stationarity problem directly. In this paper, we propose a method which regularizes CSP towards stationary subspaces (sCSP) and show that this increases classification accuracy, especially for subjects who are hardly able to control a BCI. We compare our method with the state-of-the-art approaches on different datasets, show competitive results and analyse the reasons for the improvement.

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

    OpenAIRE

    Sebastian Halder; Balint Varkuti; Martin Bogdan; Ranganatha Sitaram

    2013-01-01

    Objective: Brain-computer interfaces (BCIs) 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 ana...

  4. Improving players' control over the NeuroSky brain-computer interface

    OpenAIRE

    Kristín Guðmundsdóttir 1963

    2011-01-01

    Abstract In mid-2009, NeuroSky released the first consumer brain-computer interface (BCI). MindGames, since that time, has been developing games which players control with their powers of concentration and relaxation via consumer brain-computer interfaces. At present, all users of these novel interfaces are inexperienced, and have trouble controlling them. Therefore MindGames would like to develop a method for helping as people to learn as quickly as possible to activate the "relaxation" a...

  5. Guest Editorial: Brain/Neuronal-Computer Game Interfaces and Interaction

    OpenAIRE

    Coyle, Damien; Principe, Jose; Lotte, Fabien; Nijholt, Anton

    2013-01-01

    Games, in general, have been around since ancient times to entertain us. Since the first electronic and video games appeared in the 1940s and 1950s there has been an increasing demand for enhancements to existing games and new ways of interacting with computer games. Brain/neuronal signal controlled games controllers are now satisfying this demand, extending the accessibility of computer games to physically impaired users and enhancing neurofeedback for rehabilitation and other cognitive prob...

  6. Guest Editorial Brain-Computer Interface: Today and Tomorrow

    Institute of Scientific and Technical Information of China (English)

    De-Zhong Yao

    2009-01-01

    @@ As the invited editor of this special issue on brain- computer nterface (BCI), I am pleased to give a comment on the state-of-the-art with the introduction of recent advances made at the Chengdu BCI Group, University of Electronic Science and Technology of China (UESTC).

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

    OpenAIRE

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

    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 th...

  8. Brain-Computer Interface: Common Tensor Discriminant Analysis Classifier Evaluation

    Czech Academy of Sciences Publication Activity Database

    Frolov, A.; Húsek, Dušan; Bobrov, P.

    Piscataway: IEEE, 2011 - (Abraham, A.; Corchado, E.; Berwick, R.; de Carvalho, A.; Zomaya, A.; Yager, R.), s. 614-620 ISBN 978-1-4577-1122-0. [NaBIC 2011. World Congress on Nature and Biologically Inspired Computing /3./. Salamanca (ES), 19.10.2011-21.10.2011] R&D Projects: GA ČR GAP202/10/0262; GA ČR GA205/09/1079; GA MŠk(CZ) 1M0567 Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070 Institutional research plan: CEZ:AV0Z10300504 Keywords : human computer interface * motor imagery * EEG signal classification * Bayesian classification * Common Spatial Patterns * Common Tensor Discriminant Analysis Subject RIV: IN - Informatics, Computer Science

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

    OpenAIRE

    Melissa Jiyoun Hong; Keum-Shik Hong

    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 part...

  10. Interfacing brain with computer to improve communication and rehabilitation after brain damage.

    Science.gov (United States)

    Riccio, A; Pichiorri, F; Schettini, F; Toppi, J; Risetti, M; Formisano, R; Molinari, M; Astolfi, L; Cincotti, F; Mattia, D

    2016-01-01

    Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm. PMID:27590975

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

    Science.gov (United States)

    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. PMID:26982717

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

    Science.gov (United States)

    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

  13. BRAIN-COMPUTER-INTERFACE – SUPPORTED MOTOR IMAGERY TRAININTG FOR PATIENTS WITH HEMIPARESIS

    Directory of Open Access Journals (Sweden)

    O. A. Mokienko

    2013-01-01

    Full Text Available The aim of study was to assess the feasibility of motor imagery supported brain-computer interface in patients with hemiparesis. 13 patients with central paresis of the hand and 15 healthy volunteers were learning to control EEG-based interface with feedback. No differences on interface control quality were found between patients and healthy subjects. The trainings were accompanied by the desynchronization of sensorimotor rhythm. In patients with cortical damage the source of EEG-activity was dislocated.

  14. Improved signal processing approaches in an offline simulation of a hybrid brain-computer interface

    OpenAIRE

    Brunner & C.; Allison B.Z.; Krusienski D.J.; Kaiser V.; Muller-Putz G.R.; Pfurtscheller G.; Neuper C.

    2010-01-01

    In a conventional brain–computer interface (BCI) system, users perform mental tasks that yield specific patterns of brain activity. A pattern recognition system determines which brain activity pattern a user is producing and thereby infers the user’s mental task, allowing users to send messages or commands through brain activity alone. Unfortunately, despite extensive research to improve classification accuracy, BCIs almost always exhibit errors, which are sometimes so severe that effective c...

  15. 4-CLASS MOTOR IMAGERY CLASSIFICATION FOR POST STROKE REHABILITATION USING BRAIN-COMPUTER INTERFACE

    OpenAIRE

    Aarathi Kumar*, Nisha. P. V

    2016-01-01

    Brain-Computer Interface (BCI) is a mechanism that helps in the control/communication of one’s environment through the brain signals obtained directly from the brain via an EEG signal acquisition unit. A BCI incorporating Motor Imagery for post-stroke rehabilitation of upper limbs and knee in fully disabled patients is designed. It helps in restoring some of the activities of the daily living. It aids post-stroke sufferers to carry out functionalities like movement of right an...

  16. Reducing Dataset Size in Frequency Domain for Brain Computer Interface Motor Imagery Classification

    Directory of Open Access Journals (Sweden)

    Ch.Aparna

    2010-12-01

    Full Text Available Brain computer interface is an emerging area of research where the BCI system is able to detect and interpret the mental activity into computer interpretable signals opening a wide area of applications where activities can be completed without using muscular movement. In Brain Computer Interface research, for classification of EEG signals the raw signals captured has to undergo some preprocessing, to obtain the right attributes for classification. In this paper, we present a system which allows for classification of mental tasks based on a statistical data obtained in frequency domain using Discrete cosine transform and extracting useful frequencies from the same with application of decision tree algorithms for classification.

  17. Brain Computer Interface-Controlling Devices Utilizing The Alpha Brain Waves

    OpenAIRE

    Rohan Hundia

    2015-01-01

    Abstract This paper describes the development and testing of an interface system whereby one can control external devices by voluntarily controlling alpha waves that is through eye movement. Such a system may be used for the control of prosthetics robotic arms and external devices like wheelchairs using the alpha brain waves and the Mu rhythm. The response generated through the movement of the eye detecting and controlling the amplitude of the alpha brain waves is interfaced and processed to...

  18. Performance Measurement for Brain-Computer or Brain-Machine Interfaces: A Tutorial

    Science.gov (United States)

    Thompson, David E.; Quitadamo, Lucia R.; Mainardi, Luca; Laghari, Khalil ur Rehman; Gao, Shangkai; Kindermans, Pieter-Jan; Simeral, John D.; Fazel-Rezai, Reza; Matteucci, Matteo; Falk, Tiago H.; Bianchi, Luigi; Chestek, Cynthia A.; Huggins, Jane E.

    2014-01-01

    Objective Brain-Computer Interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research. Approach A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This manuscript contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop. Main Results Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their application to encourage uniform application between laboratories. Significance Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field. PMID:24838070

  19. Personality Trait and Facial Expression Filter-Based Brain-Computer Interface

    OpenAIRE

    Seongah Chin; Chung-Yeon Lee

    2013-01-01

    In this paper, we present technical approaches that bridge the gap in the research related to the use of brain‐computer interfaces for entertainment and facial expressions. Such facial expressions that reflect an individual’s personal traits can be used to better realize artificial facial expressions in a gaming environment based on a brain‐computer interface. First, an emotion extraction filter is introduced in order to classify emotions on the basis of the users’ brain signals in real time....

  20. Brain-computer interface based on generation of visual images.

    Directory of Open Access Journals (Sweden)

    Pavel Bobrov

    Full Text Available This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP classifier.

  1. Brain-computer interface based on generation of visual images.

    Science.gov (United States)

    Bobrov, Pavel; Frolov, Alexander; Cantor, Charles; Fedulova, Irina; Bakhnyan, Mikhail; Zhavoronkov, Alexander

    2011-01-01

    This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier. PMID:21695206

  2. As We May Think and Be: Brain-computer interfaces to expand the substrate of mind

    Directory of Open Access Journals (Sweden)

    Mijail Demian Serruya

    2015-04-01

    Full Text Available Over a half-century ago, the scientist Vannevar Bush explored the conundrum of how to tap the exponentially rising sea of human knowledge for the betterment of humanity. In his description of a hypothetical electronic library he dubbed the memex, he anticipated internet search and online encyclopedias (Bush, 1945. By blurring the boundary between brain and computer, brain-computer interfaces (BCI could lead to more efficient use of electronic resources (Schalk, 2008. We could expand the substrate of the mind itself rather than merely interfacing it to external computers. Components of brain-computer interfaces could be re-arranged to create brain-brain interfaces, or tightly interconnected links between a person’s brain and ectopic neural modules. Such modules – whether sitting in a bubbling Petri dish, rendered in reciprocally linked integrated circuits, or implanted in our belly – would mark the first step on to a path of breaking out of the limitations imposed by our phylogenetic past Novel BCI architectures could generate novel abilities to navigate and access information that might speed translational science efforts and push the boundaries of human knowledge in an unprecedented manner.

  3. An efficient P300-based brain-computer interface for disabled subjects

    OpenAIRE

    Hoffmann, Ulrich; Vesin, Jean-Marc; Ebrahimi, Touradj; Diserens, Karin

    2008-01-01

    A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely dis...

  4. Natural movement with concurrent brain-computer interface control induces persistent dissociation of neural activity

    OpenAIRE

    Bashford, Luke; Wu, Jing; Sarma, Devapratim; Collins, Kelly; Ojemann, Jeff; Mehring, Carsten

    2016-01-01

    As Brain-computer interface (BCI) technology develops it is likely it may be incorporated into protocols that complement and supplement existing movements of the user. Two possible scenarios for such a control could be: the increasing interest to control artificial supernumerary prosthetics, or in cases following brain injury where BCI can be incorporated alongside residual movements to recover ability. In this study we explore the extent to which the human motor cortex is able to concurrentl...

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

    OpenAIRE

    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 im...

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

    OpenAIRE

    Rüdiger Rupp

    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 imp...

  7. Steady state visually evoked potentials based Brain computer interface test outside the lab

    OpenAIRE

    Eduardo Francisco Caicedo Bravo; Jaiber Evelio Cardona Aristizábal

    2016-01-01

    Context: Steady State Visually Evoked Potentials (SSVEP) are brain signals which are one of the most promising signals for Brain Computer Interfaces (BCIs) implementation, however, SSVEP based BCI generally are proven in a controlled environment and there are a few tests in demanding conditions.Method: We present a SSVEP based BCI system that was used outside the lab in a noisy environment with distractions, and with the presence of public. For the tests, we showed a maze in a laptop where th...

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

    OpenAIRE

    Kyhyun Um; Yunsick Sung; Kyungeun Cho

    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...

  9. An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface

    OpenAIRE

    Peng Xu; Ping Yang; Xu Lei; Dezhong Yao

    2011-01-01

    BACKGROUND: There is a growing interest in the study of signal processing and machine learning methods, which may make the brain computer interface (BCI) a new communication channel. A variety of classification methods have been utilized to convert the brain information into control commands. However, most of the methods only produce uncalibrated values and uncertain results. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we presented a probabilistic method "enhanced BLDA" (EBLDA) for multi-c...

  10. The Use of a Brain Computer Interface Remote Control to Navigate a Recreational Device

    OpenAIRE

    Shih Chung Chen; Aaron Raymond See; Yeou Jiunn Chen; Chia Hong Yeng; Chih Kuo Liang

    2013-01-01

    People suffering from paralysis caused by serious neural disorder or spinal cord injury also need to be given a means of recreation other than general living aids. Although there have been a proliferation of brain computer interface (BCI) applications, developments for recreational activities are scarcely seen. The objective of this study is to develop a BCI-based remote control integrated with commercial devices such as the remote controlled Air Swimmer. The brain is visually stimulated usin...

  11. Brain Computer Interfaces for Communication in Paralysis: a Clinical-Experimental Approach

    OpenAIRE

    Hinterberger, T.; F. Nijboer; Kübler, A; Matuz, T.; Furdea, A.; Mochty, U.; Jordan, M.; Lal, T.N; Hill, J.; MELLINGER, J.; Bensch, M.; Tangermann, M.; Widmann, G; Elger, C; Rosenstiel, W.

    2007-01-01

    An overview of different approaches to brain-computer interfaces (BCIs) developed in our laboratory is given. An important clinical application of BCIs is to enable communication or environmental control in severely paralyzed patients. The BCI 'Thought-Translation Device (TTD)' allows verbal communication through the voluntary self-regulation of brain signals (e.g., slow cortical potentials (SCPs)), which is achieved by operant feedback train-ing. Humans' ability to self-regulate their SCPs i...

  12. Editorial: Arts and Brain-Computer Interfaces (BCIs)

    NARCIS (Netherlands)

    Nijholt, Anton; Nam, Chang S.

    2015-01-01

    The primary goal of this special issue is to address contemporary challenges involved in designing BCI applications related to the creation and experience of art. This involves a low level artistic audification and visualization of brain activity patterns, a higher level musification and animation,

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

    DEFF Research Database (Denmark)

    Rohani, Darius Adam; Sørensen, Helge Bjarup Dissing; 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...

  14. Bigger data for Big Data: from Twitter to brain-computer interface

    OpenAIRE

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

    2014-01-01

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

  15. Multi-modal affect induction for affective brain-computer interfaces

    NARCIS (Netherlands)

    Mühl, C.; Broek, E.L. van den; Brouwer, A.M.; Nijboer, F.; Wouwe, N.C. van; Heylen, D.

    2011-01-01

    Reliable applications of affective brain-computer interfaces (aBCI) in realistic, multi-modal environments require a detailed understanding of the processes involved in emotions. To explore the modalityspecific nature of affective responses, we studied neurophysiological responses (i.e., EEG) of 24

  16. Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

    OpenAIRE

    Wilson, J. Adam; Schalk, Gerwin; Walton, Léo M.; Williams, Justin C.

    2009-01-01

    A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is ...

  17. Bottlenecks to Clinical Translation of Direct Brain-Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Mijail Demian Serruya

    2014-12-01

    Full Text Available Despite several decades of research into novel brain-implantable devices to treat a range of diseases, only two- cochlear implants for sensorineural hearing loss and deep brain stimulation for movement disorders- have yielded any appreciable clinical benefit. Obstacles to translation include technical factors (e.g., signal loss due to gliosis or micromotion, lack of awareness of current clinical options for patients that the new therapy must outperform, traversing between federal and corporate funding needed to support clinical trials, and insufficient management expertise. This commentary reviews these obstacles preventing the translation of promising new neurotechnologies into clinical application and suggests some principles that interdisciplinary teams in academia and industry could adopt to enhance their chances of success.

  18. Brain-Computer Interface Based on Generation of Visual Images

    OpenAIRE

    Bobrov, Pavel; Frolov, Alexander; Cantor, Charles; Fedulova, Irina; Bakhnyan, Mikhail; Zhavoronkov, Alexander

    2011-01-01

    This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification durin...

  19. Error-related EEG potentials in brain-computer interfaces

    OpenAIRE

    Ferrez, Pierre W.

    2007-01-01

    People with severe motor disabilities (spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS), etc.) but with intact brain functions are somehow prisoners of their own body. They need alternative ways of communication and control to interact with their environment in their everyday life. These new tools are supposed to increase their quality of life by giving these people the opportunity to recover part of their independence. Therefore, these alternative ways have to be reliable and er...

  20. Event-related brain potentials in emotion perception research, individual cognitive assessment and brain-computer interfaces

    OpenAIRE

    Bostanov, Vladimir

    2003-01-01

    All of the experimental and theoretical work presented in this dissertation has been inspired by the general idea of applying event-related brain potential (ERP) measurement and assessment for practical purposes: cognitive diagnostics and Brain-Computer Interfaces (BCI) for paralyzed people. In Chapter 1, two new ERP paradigms are introduced, which were developed for the diagnostics of a particular cognitive function, the recognition of affective prosody. The affective state of a speaker ...

  1. Using a brain-computer interface for rehabilitation : a case study on a patient with implanted electrodes

    OpenAIRE

    Van Langhenhove, Aurélien; Bekaert, Marie-Hélène; N'Guyen, Jean-Paul

    2008-01-01

    Brain-computer interfaces (BCIs) allow direct communication between men and computers thanks to the analysis of brain activity. Current applications of BCIs in assistive technologies are: palliative communication systems for patients with complete muscular paralysis and restoration of movement for people with a motor in firmity (orthetic or prosthetic devices controlled by the thought). It appears today that brain-computer interfaces can also be used in therapeutic approaches to rehabilitatio...

  2. (r)Evolution in Brain-Computer Interface Technologies for Play: (non)Users in Mind

    OpenAIRE

    Cloyd, Tristan Dane

    2014-01-01

    This dissertation addresses user responses to the introduction of Brain-Computer Interface technologies (BCI) for gaming and consumer applications in the early part of the 21st century. BCI technology has emerged from the contexts of interrelated medical, academic, and military research networks including an established computer and gaming industry. First, I show that the emergence and development of BCI technology are based on specific economic, socio-cultural, and material factors, and seco...

  3. Combining Object Detection And Brain Computer Interfacing: Towards A New Way Of Subject-Environment Interaction

    OpenAIRE

    Robben, Arne; Chumerin, Nikolay; Manyakov, Nikolay V.; Combaz, Adrien; van Vliet, Marijn; Hulle, Marc van

    2011-01-01

    In this paper we propose an application which combines two research disciplines: object detection and brain-computer interfacing. It is in particular useful for patients suffering from a severe motor impairment which prevents them to interact with their surrounding environment. The application shows an image of e.g., the room of the patient, on a computer screen and searches for instances of certain objects in the image. When these are found, a flashing dot appears on top of them, flickering ...

  4. Wyrm: A Brain-Computer Interface Toolbox in Python

    OpenAIRE

    Venthur, Bastian; Dähne, Sven; Höhne, Johannes; Heller, Hendrik; Blankertz, Benjamin

    2015-01-01

    In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm (https://github.com/bbci/wyrm), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used a...

  5. Sources of Electrical Brain Activity Most Relevant to Performance of Brain-Computer Interface Based on Motor Imagery

    Czech Academy of Sciences Publication Activity Database

    Frolov, A.; Húsek, Dušan; Bobrov, P.; Mokienko, O.; Tintěra, J.

    Rijeka: InTech, 2013 - (Fazel-Rezai, R.), s. 175-193 ISBN 978-953-51-1134-4 R&D Projects: GA ČR GAP202/10/0262 Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070 Institutional support: RVO:67985807 Keywords : brain computer interface * BCI * EEG * fMRI * signal separation * inverse EEG task Subject RIV: IN - Informatics, Computer Science

  6. Python executable script for estimating two effective parameters to individualize Brain-Computer Interfaces: Individual alpha frequency & neurophysiological predictor

    OpenAIRE

    Luz Maria Alonso-Valerdi

    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 differen...

  7. Wyrm: A Brain-Computer Interface Toolbox in Python.

    Science.gov (United States)

    Venthur, Bastian; Dähne, Sven; Höhne, Johannes; Heller, Hendrik; Blankertz, Benjamin

    2015-10-01

    In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm ( https://github.com/bbci/wyrm ), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used as a toolbox for analysis and visualization of neurophysiological data and in real-time settings, like an online BCI application. In order to prevent software defects, Wyrm makes extensive use of unit testing. We will explain the key aspects of Wyrm's software architecture and design decisions for its data structure, and demonstrate and validate the use of our toolbox by presenting our approach to the classification tasks of two different data sets from the BCI Competition III. Furthermore, we will give a brief analysis of the data sets using our toolbox, and demonstrate how we implemented an online experiment using Wyrm. With Wyrm we add the final piece to our ongoing effort to provide a complete, free and open source BCI system in Python. PMID:26001643

  8. Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Liqing Zhang

    2007-08-01

    Full Text Available Brain-computer interface (BCI systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

  9. Brain Painting: first evaluation of a new brain-computer interface application with ALS patients and healthy volunteers.

    Directory of Open Access Journals (Sweden)

    SonjaC Kleih

    2010-11-01

    Full Text Available Brain-computer interfaces (BCI 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 coloured matrix tested by a group of ALS-patients (n = 3 and healthy participants (n = 10, and a black & 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 (coloured 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 & 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.

  10. Improved Targeting Through Collaborative Decision-Making and Brain Computer Interfaces

    Science.gov (United States)

    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.

  11. Single-trial EEG classification using in-phase average for brain-computer interface

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Communication signals should be estimated by a single trial in a brain-computer interface.Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro encephalogram(EEG),this paper adopted the time-lock averaged signals from multi-channels as features.200 trials of EEG recordings evoked by target or non-target stimuli were classified by the support vector machine(SVM).Results show that a classification accuracy of higher than 97% can be obtained by merely using the 250-550 ms time section of the averaged signals with channel Cz and Pz as features.It suggests that a possible approach to boost communication speed and simplify the designation of the brain-computer interface(BCI)system is worthy of an attempt in this way.

  12. Brain Computer Interface-Controlling Devices Utilizing The Alpha Brain Waves

    Directory of Open Access Journals (Sweden)

    Rohan Hundia

    2015-01-01

    Full Text Available Abstract This paper describes the development and testing of an interface system whereby one can control external devices by voluntarily controlling alpha waves that is through eye movement. Such a system may be used for the control of prosthetics robotic arms and external devices like wheelchairs using the alpha brain waves and the Mu rhythm. The response generated through the movement of the eye detecting and controlling the amplitude of the alpha brain waves is interfaced and processed to control Robotic systems and smart home control. In order to measure the response of alpha waves over different lobes of the brain initially I measured these signals over 32 regions using silver chloride plated electrodes. By the opening and the closure of the eyes and the movement in the up-down left-right directions and processing these movements measuring them over the occipital region I was able to differentiate the amplitude of the alpha waves generated due to these several movements. In the First session testing period subjects were asked to close and open their eyes and they were able to control limited movements of a Robot and a prosthetic arm. In the Second 2session the movement of the eyes was also considered left-right up-down along with the opening and closure during this time span they were able to control more dimensions of the robot several devices at the same time using different eye movements.

  13. Selective Sensation Based Brain-Computer Interface via Mechanical Vibrotactile Stimulation

    OpenAIRE

    Lin Yao; Jianjun Meng; Dingguo Zhang; Xinjun Sheng; Xiangyang Zhu

    2013-01-01

    In this work, mechanical vibrotactile stimulation was applied to subjects' left and right wrist skins with equal intensity, and a selective sensation perception task was performed to achieve two types of selections similar to motor imagery Brain-Computer Interface. The proposed system was based on event-related desynchronization/synchronization (ERD/ERS), which had a correlation with processing of afferent inflow in human somatosensory system, and attentional effect which modulated the ERD/ER...

  14. Robust Virtual Keyboard for Brain-Computer Interface (ROBIK): An Halfway Update on the Project

    OpenAIRE

    Mayaud, Louis; Congedo, Marco; Filipe, Sabine; Charvet, Guillaume; Schoettel, Remi; Annane, Djillali

    2011-01-01

    The principle of a Brain-Computer Interface or BCI is to control a device through the extraction and interpretation of signal features from electroencephalograms (EEG) collected either from the surface of the scalp or through invasive measurements. This late idea of communication technique (Vidal 1973), offers the advantage of bypassing the need for muscle activity in the control chain and is therefore presented as a promising alternative to restore communication and control in severely disab...

  15. An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

    OpenAIRE

    Yijun Wang; Dan Zhang; Xiaorong Gao; Bo Hong; Shangkai Gao

    2007-01-01

    For a robust brain-computer interface (BCI) system based on motor imagery (MI), it should be able to tell when the subject is not concentrating on MI tasks (the “idle state”) so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this ...

  16. sBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

    OpenAIRE

    Tatsiana Malechka; Tobias Tetzel; Ulrich Krebs; Diana Feuser; Axel Graeser

    2015-01-01

    Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI). In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG) meas...

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

    OpenAIRE

    Kaufmann, Tobias; Herweg, Andreas; Kübler, Andrea

    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 feasi...

  18. Classification of EEG Signals in a Brain-Computer Interface System

    OpenAIRE

    Larsen, Erik Andreas

    2011-01-01

    Electroencephalography (EEG) equipment are becoming more available on thepublic market, which enables more diverse research in a currently narrow field.The Brain-Computer Interface (BCI) community recognize the need for systemsthat makes BCI more user-friendly, real-time, manageable and suited for peoplethat are not forced to use them, like clinical patients, and those who are disabled.Thus, this project is an effort to seek such improvements, having a newly availablemarket product to experim...

  19. Probing command following in patients with disorders of consciousness using a brain-computer interface.

    OpenAIRE

    Lule D.; Noirhomme Q.; Kleih S.C.; Chatelle C.; Halder S.; Demertzi A.; Bruno M.-A.; Gosseries O.; Vanhaudenhuyse A.; Schnakers C.

    2013-01-01

    OBJECTIVE: To determine if brain-computer interfaces (BCIs) could serve as supportive tools for detecting consciousness in patients with disorders of consciousness by detecting response to command and communication. METHODS: We tested a 4-choice auditory oddball EEG-BCI paradigm on 16 healthy subjects and 18 patients in a vegetative state/unresponsive wakefulness syndrome, in a minimally conscious state (MCS), and in locked-in syndrome (LIS). Subjects were exposed to 4 training trials and 10 ...

  20. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials

    OpenAIRE

    Hubert Cecotti; Bertrand Rivet

    2014-01-01

    New paradigms are required in Brain-Computer Interface (BCI) systems for the needs and expectations of healthy people. To solve this issue, we explore the emerging field of cooperative BCIs, which involves several users in a single BCI system. Contrary to classical BCIs that are dependent on the unique subject’s will, cooperative BCIs are used for problem solving tasks where several people shall be engaged by sharing a common goal. Similarly as combining trials over time improves performance,...

  1. Why Standard Brain-Computer Interface (BCI) Training Protocols Should be Changed: An Experimental Study

    OpenAIRE

    Jeunet, Camille; Jahanpour, Emilie; Lotte, Fabien

    2016-01-01

    Objective While promising, ElectroEncephaloGraphy based Brain-Computer Interfaces (BCIs) remain barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users' Motor-Imagery based BCI (MI-BCI) control performance. Approach We performed two experiments...

  2. The Berlin Brain-Computer Interface: Non-Medical Uses of BCI Technology

    OpenAIRE

    BenjaminBlankertz; CarmenVidaurre; LennyERamsey; GabrielCurio

    2010-01-01

    Brain-Computer Interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potent...

  3. Blind Source Separation Based of Brain Computer Interface System: A review

    OpenAIRE

    Ahmed Kareem Abdullah; Zhang Chao Zhu

    2014-01-01

    This study reviews the originality and development of the Brain Computer Interface (BCI) system and focus on the BCI system design based on Blind Source Separation (BSS) techniques. The study also provides the recent trends and discusses some of a new ideas for BSS techniques in BCI architecture, articles which discussing the BCI system development were analysed, types of the BCI systems and the recent BCI design were explored. Since 1970 when the research of BCI system began in the Californi...

  4. Subject-oriented training for motor imagery brain-computer interfaces

    OpenAIRE

    Perdikis, Serafeim; Leeb, Robert; Millán, José del R.

    2014-01-01

    Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the machine-learning aspects of BCI training. This work presents a novel co-adaptive training protocol shifting the focus on subject-related performances and the optimal accommodation of the interactions between the two learning agents of the BC...

  5. Performance assessment in brain-computer interface-based augmentative and alternative communication

    OpenAIRE

    Thompson, David E.; Blain-Moraes, Stefanie; Huggins, Jane E.

    2013-01-01

    A large number of incommensurable metrics are currently used to report the performance of brain-computer interfaces (BCI) used for augmentative and alterative communication (AAC). The lack of standard metrics precludes the comparison of different BCI-based AAC systems, hindering rapid growth and development of this technology. This paper presents a review of the metrics that have been used to report performance of BCIs used for AAC from January 2005 to January 2012. We distinguish between Lev...

  6. Manipulating Attention via Mindfulness Induction Improves P300-based Brain-Computer Interface Performance

    OpenAIRE

    Lakey, Chad E.; Berry, Daniel R.; Sellers, Eric W.

    2011-01-01

    In this study, we examined the effects of a short mindfulness meditation induction (MMI) on the performance of a P300-based brain-computer interface (BCI) task. We expected that MMI would harness present moment attentional resources, resulting in two positive consequences for P300-based BCI use. Specifically, we believed MMI would facilitate increases in task accuracy and promote the production of robust P300 amplitudes. Sixteen-channel electroencephalographic data were recorded from 18 subje...

  7. Affective Interaction with a Virtual Character through an fNIRS Brain-Computer Interface

    OpenAIRE

    Aranyi, Gabor; Pecune, Florian; Charles, Fred; Pelachaud, Catherine; Cavazza, Marc

    2016-01-01

    Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be ...

  8. EXPERIMENTAL AND THEORETICAL FOUNDATIONS AND PRACTICAL IMPLEMENTATION OF TECHNOLOGY BRAIN-COMPUTER INTERFACE

    Directory of Open Access Journals (Sweden)

    A. Ya. Kaplan

    2013-01-01

    Full Text Available Technology brain-computer interface (BCI allow saperson to learn how to control external devices via thevoluntary regulation of own EEG directly from the brain without the involvement in the process of nerves and muscles. At the beginning the main goal of BCI was to replace or restore motor function to people disabled by neuromuscular disorders. Currently, the task of designing the BCI increased significantly, more capturing different aspects of life a healthy person. This article discusses the theoretical, experimental and technological base of BCI development and systematized critical fields of real implementation of these technologies.

  9. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface

    DEFF Research Database (Denmark)

    Mrachacz-Kersting, Natalie; Jiang, Ning; Stevenson, Andrew James Thomas;

    2016-01-01

    Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here, we evaluate the effect and the underlying mechanisms of three BCI training sessions in a double-blind-sham-controlled design. The applied BCI is......-associative group. Fugl-Meyer motor scores (0.8±0.46 point difference p=0.01), foot (but not finger) tapping frequency, and 10-m walking speed improved significantly for the BCIassociative group, indicating clinically relevant improvements. For the BCI as applied here, the precise coupling between the brain command...

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

    Science.gov (United States)

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

    2016-01-01

    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. PMID:26861347

  11. Brain-Computer Interfaces Applying Our Minds to Human-computer Interaction

    CERN Document Server

    Tan, Desney S

    2010-01-01

    For generations, humans have fantasized about the ability to create devices that can see into a person's mind and thoughts, or to communicate and interact with machines through thought alone. Such ideas have long captured the imagination of humankind in the form of ancient myths and modern science fiction stories. Recent advances in cognitive neuroscience and brain imaging technologies have started to turn these myths into a reality, and are providing us with the ability to interface directly with the human brain. This ability is made possible through the use of sensors that monitor physical p

  12. Cognitive assessment of executive functions using brain computer interface and eye-tracking

    Directory of Open Access Journals (Sweden)

    P. Cipresso

    2013-03-01

    Full Text Available New technologies to enable augmentative and alternative communication in Amyotrophic Lateral Sclerosis (ALS have been recently used in several studies. However, a comprehensive battery for cognitive assessment has not been implemented yet. Brain computer interfaces are innovative systems able to generate a control signal from brain responses conveying messages directly to a computer. Another available technology for communication purposes is the Eye-tracker system, that conveys messages from eye-movement to a computer. In this study we explored the use of these two technologies for the cognitive assessment of executive functions in a healthy population and in a ALS patient, also verifying usability, pleasantness, fatigue, and emotional aspects related to the setting. Our preliminary results may have interesting implications for both clinical practice (the availability of an effective tool for neuropsychological evaluation of ALS patients and ethical issues.

  13. Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm

    Directory of Open Access Journals (Sweden)

    Hovagim Bakardjian

    2007-07-01

    Full Text Available We propose a new multistage procedure for a real-time brain-machine/computer interface (BCI. The developed system allows a BCI user to navigate a small car (or any other object on the computer screen in real time, in any of the four directions, and to stop it if necessary. Extensive experiments with five young healthy subjects confirmed the high performance of the proposed online BCI system. The modular structure, high speed, and the optimal frequency band characteristics of the BCI platform are features which allow an extension to a substantially higher number of commands in the near future.

  14. Real-Time Brain-Computer Interface System Based on Motor Imagery

    Institute of Scientific and Technical Information of China (English)

    Tie-Jun Liu; Ping Yang; Xu-Yong Peng; Yu Huang; De-Zhong Yao

    2009-01-01

    A brain-computer interface (BCI) real-time system based on motor imagery translates the user's motor intention into a real-time control signal for peripheral equipments.A key problem to be solved for practical applications is real-time data collection and processing.In this paper,a real-time BCI system is implemented on computer with electroencephalogram amplifier.In our implementation,the on-line voting method is adopted for feedback control strategy,and the voting results are used to control the cursor horizontal movement.Three subjects take part in the experiment.The results indicate that the best accuracy is 90%.

  15. The Self-Paced Graz Brain-Computer Interface: Methods and Applications

    Directory of Open Access Journals (Sweden)

    Alois Schloegl

    2007-08-01

    Full Text Available We present the self-paced 3-class Graz brain-computer interface (BCI which is based on the detection of sensorimotor electroencephalogram (EEG rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control or not (non-control state. The presented system is able to automatically reduce electrooculogram (EOG artifacts, to detect electromyographic (EMG activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.

  16. Mining multi-channel EEG for its information content: An ANN-based method for a brain-computer interface

    DEFF Research Database (Denmark)

    Peters, B.O.; Pfurtscheller, G.; Flyvbjerg, H.

    1998-01-01

    . This high recognition rate makes the classifier suitable for a so-called 'Brain-Computer Interface', a system that allows one to control a computer, or another device, with ones brain waves. Our classifier Laplace filters the EEG spatially, but makes use of its entire frequency range, and automatically...

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

    Directory of Open Access Journals (Sweden)

    Thierry Castermans

    2013-12-01

    Full Text Available 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.

  18. A two-class brain computer interface to freely navigate through virtual worlds.

    Science.gov (United States)

    Ron-Angevin, Ricardo; Díaz-Estrella, Antonio; Velasco-Alvarez, Francisco

    2009-06-01

    A brain computer interface that enables navigation through a virtual environment (VE) using four different navigation commands (turn right, turn left, move forward and move back) is presented. A graphical interface allows subjects to select a specific command. In this interface, the different navigation commands are surrounding a circle. A bar in the center of the circle is continuously rotating. The subject controls, by only two mental tasks, the bar extension to reach the chosen command. In this study, after an initial training based on three sessions, 8 out of 15 naive subjects were able to navigate through the VE discriminating between imagination of right-hand movements and relaxed state. All subjects (except one) improved their performance in each run and a mean error rate of 23.75% was obtained. PMID:19469662

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

    Science.gov (United States)

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

    2014-01-01

    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. PMID:24828128

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

    Science.gov (United States)

    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

  1. Student Teaching and Research Laboratory Focusing on Brain-computer Interface Paradigms - A Creative Environment for Computer Science Students -

    OpenAIRE

    Rutkowski, Tomasz M.

    2015-01-01

    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.

  2. Brain-computer interfaces: an overview of the hardware to record neural signals from the cortex.

    Science.gov (United States)

    Stieglitz, Thomas; Rubehn, Birthe; Henle, Christian; Kisban, Sebastian; Herwik, Stanislav; Ruther, Patrick; Schuettler, Martin

    2009-01-01

    Brain-computer interfaces (BCIs) record neural signals from cortical origin with the objective to control a user interface for communication purposes, a robotic artifact or artificial limb as actuator. One of the key components of such a neuroprosthetic system is the neuro-technical interface itself, the electrode array. In this chapter, different designs and manufacturing techniques will be compared and assessed with respect to scaling and assembling limitations. The overview includes electroencephalogram (EEG) electrodes and epicortical brain-machine interfaces to record local field potentials (LFPs) from the surface of the cortex as well as intracortical needle electrodes that are intended to record single-unit activity. Two exemplary complementary technologies for micromachining of polyimide-based arrays and laser manufacturing of silicone rubber are presented and discussed with respect to spatial resolution, scaling limitations, and system properties. Advanced silicon micromachining technologies have led to highly sophisticated intracortical electrode arrays for fundamental neuroscientific applications. In this chapter, major approaches from the USA and Europe will be introduced and compared concerning complexity, modularity, and reliability. An assessment of the different technological solutions comparable to a strength weaknesses opportunities, and threats (SWOT) analysis might serve as guidance to select the adequate electrode array configuration for each control paradigm and strategy to realize robust, fast, and reliable BCIs. PMID:19660664

  3. Feature Extraction on Brain Computer Interfaces using Discrete Dyadic Wavelet Transform: Preliminary Results

    International Nuclear Information System (INIS)

    The purpose of this work is to evaluate different feature extraction alternatives to detect the event related evoked potential signal on brain computer interfaces, trying to minimize the time employed and the classification error, in terms of sensibility and specificity of the method, looking for alternatives to coherent averaging. In this context the results obtained performing the feature extraction using discrete dyadic wavelet transform using different mother wavelets are presented. For the classification a single layer perceptron was used. The results obtained with and without the wavelet decomposition were compared; showing an improvement on the classification rate, the specificity and the sensibility for the feature vectors obtained using some mother wavelets.

  4. Brain-computer interface research a state-of-the-art summary 3

    CERN Document Server

    Guger, Christoph; Allison, Brendan

    2014-01-01

    This book provides a cutting-edge overview of the latest developments in Brain-Computer-Interfaces (BCIs), reported by leading research groups. As the reader will discover, BCI research is moving ahead rapidly, with many new ideas, research initiatives, and improved technologies, e.g. BCIs that enable people to communicate just by thinking - without any movement at all. Several different groups are helping severely disabled users communicate using BCIs, and BCI technology is also being extended to facilitate recovery from stroke, epilepsy, and other conditions. Each year, hundreds of the top

  5. Using brain-computer interfaces to induce neural plasticity and restore function

    Science.gov (United States)

    Grosse-Wentrup, Moritz; Mattia, Donatella; Oweiss, Karim

    2011-04-01

    Analyzing neural signals and providing feedback in realtime is one of the core characteristics of a brain-computer interface (BCI). As this feature may be employed to induce neural plasticity, utilizing BCI technology for therapeutic purposes is increasingly gaining popularity in the BCI community. In this paper, we discuss the state-of-the-art of research on this topic, address the principles of and challenges in inducing neural plasticity by means of a BCI, and delineate the problems of study design and outcome evaluation arising in this context. We conclude with a list of open questions and recommendations for future research in this field.

  6. Brain-computer interface research a state-of-the-art summary

    CERN Document Server

    Allison, Brendan; Edlinger, Günter; Leuthardt, E C

    Brain-computer interfaces (BCIs) are rapidly developing into a mainstream, worldwide research endeavor. With so many new groups and projects, it can be difficult to identify the best ones. This book summarizes ten leading projects from around the world. About 60 submissions were received in 2011 for the highly competitive BCI Research Award, and an international jury selected the top ten. This Brief gives a concise but carefully illustrated and fully up-to-date description of each of these projects, together with an introduction and concluding chapter by the editors.

  7. Impact of spatial filters during sensor selection in a visual p300 brain-computer interface

    OpenAIRE

    Rivet, Bertrand; Cecotti, Hubert; Maby, Emmanuel; Mattout, Jérémie

    2012-01-01

    A challenge in designing a Brain-Computer Interface (BCI) is the choice of the channels, e.g. the most relevant sensors. Although a setup with many sensors can be more efficient for the detection of Event-Related Potential (ERP) like the P300, it is relevant to consider only a low number of sensors for a commercial or clinical BCI application. Indeed, a reduced number of sensors can naturally increase the user comfort by reducing the time required for the installation of the EEG (electroencep...

  8. Making brain-computer interfaces better: improving usability through post-processing

    OpenAIRE

    Plass-Oude Bos, Danny

    2014-01-01

    Brain-computer interfaces (BCIs) allow you to control things directly with your mind. Unfortunately, such input devices based on observations of the body are plagued by noise, non-stationarities, and ambiguity. In the lab, we can protect systems somewhat from these influences, but in ‘the real world’, BCIs could use a little help. How important is good control anyway? How well can users even assess their level of control? Fourteen participants evaluated three sets of mental tasks each for fiv...

  9. An efficient approach of EEG feature extraction and classification for brain computer interface

    Institute of Scientific and Technical Information of China (English)

    Wu Ting; Yan Guozheng; Yang Banghua

    2009-01-01

    In the study of brain-computer interfaces, a method of feature extraction and classification used for two kinds of imaginations is proposed. It considers Euclidean distance between mean traces recorded from the channels with two kinds of imaginations as a feature, and determines imagination classes using threshold value. It analyzed the background of experiment and theoretical foundation referring to the data sets of BCI 2003, and compared the classification precision with the best result of the competition. The result shows that the method has a high precision and is advantageous for being applied to practical systems.

  10. Classifications of Motor Imagery Tasks in Brain Computer Interface Using Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Roxana Aldea

    2014-07-01

    Full Text Available In this paper, we address a method for motor imagery feature extraction for brain computer interface (BCI. The wavelet coefficients were used to extract the features from the motor imagery EEG and the linear discriminant analysis was utilized to classify the pattern of left or right hand imagery movement and rest. The performance of the proposed method was evaluated using EEG data recorded by us, with 8 g.tec active electrodes by means of g.MOBIlab+ module. The maximum accuracy of classification is 91%.

  11. A robust sensor-selection method for P300 brain-computer interfaces

    Science.gov (United States)

    Cecotti, H.; Rivet, B.; Congedo, M.; Jutten, C.; Bertrand, O.; Maby, E.; Mattout, J.

    2011-02-01

    A brain-computer interface (BCI) is a specific type of human-computer interface that enables direct communication between human and computer through decoding of brain activity. As such, event-related potentials like the P300 can be obtained with an oddball paradigm whose targets are selected by the user. This paper deals with methods to reduce the needed set of EEG sensors in the P300 speller application. A reduced number of sensors yields more comfort for the user, decreases installation time duration, may substantially reduce the financial cost of the BCI setup and may reduce the power consumption for wireless EEG caps. Our new approach to select relevant sensors is based on backward elimination using a cost function based on the signal to signal-plus-noise ratio, after some spatial filtering. We show that this cost function selects sensors' subsets that provide a better accuracy in the speller recognition rate during the test sessions than selected subsets based on classification accuracy. We validate our selection strategy on data from 20 healthy subjects.

  12. Novel hold-release functionality in a P300 brain-computer interface

    Science.gov (United States)

    Alcaide-Aguirre, R. E.; Huggins, J. E.

    2014-12-01

    Assistive technology control interface theory describes interface activation and interface deactivation as distinct properties of any control interface. Separating control of activation and deactivation allows precise timing of the duration of the activation. Objective. We propose a novel P300 brain-computer interface (BCI) functionality with separate control of the initial activation and the deactivation (hold-release) of a selection. Approach. Using two different layouts and off-line analysis, we tested the accuracy with which subjects could (1) hold their selection and (2) quickly change between selections. Main results. Mean accuracy across all subjects for the hold-release algorithm was 85% with one hold-release classification and 100% with two hold-release classifications. Using a layout designed to lower perceptual errors, accuracy increased to a mean of 90% and the time subjects could hold a selection was 40% longer than with the standard layout. Hold-release functionality provides improved response time (6-16 times faster) over the initial P300 BCI selection by allowing the BCI to make hold-release decisions from very few flashes instead of after multiple sequences of flashes. Significance. For the BCI user, hold-release functionality allows for faster, more continuous control with a P300 BCI, creating new options for BCI applications.

  13. The Use of a Brain Computer Interface Remote Control to Navigate a Recreational Device

    Directory of Open Access Journals (Sweden)

    Shih Chung Chen

    2013-01-01

    Full Text Available People suffering from paralysis caused by serious neural disorder or spinal cord injury also need to be given a means of recreation other than general living aids. Although there have been a proliferation of brain computer interface (BCI applications, developments for recreational activities are scarcely seen. The objective of this study is to develop a BCI-based remote control integrated with commercial devices such as the remote controlled Air Swimmer. The brain is visually stimulated using boxes flickering at preprogrammed frequencies to activate a brain response. After acquiring and processing these brain signals, the frequency of the resulting peak, which corresponds to the user’s selection, is determined by a decision model. Consequently, a command signal is sent from the computer to the wireless remote controller via a data acquisition (DAQ module. A command selection training (CST and simulated path test (SPT were conducted by 12 subjects using the BCI control system and the experimental results showed a recognition accuracy rate of 89.51% and 92.31% for the CST and SPT, respectively. The fastest information transfer rate demonstrated a response of 105 bits/min and 41.79 bits/min for the CST and SPT, respectively. The BCI system was proven to be able to provide a fast and accurate response for a remote controller application.

  14. A subject-independent pattern-based Brain-Computer Interface

    Science.gov (United States)

    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

  15. A subject-independent pattern-based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Andreas Markus Ray

    2015-10-01

    Full Text Available 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.

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

    Science.gov (United States)

    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. PMID:26481030

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

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    Kyhyun Um

    2012-11-01

    Full Text Available 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.

  18. Towards Practical Brain-Computer Interfaces Bridging the Gap from Research to Real-World Applications

    CERN Document Server

    Dunne, Stephen; Leeb, Robert; Millán, José; Nijholt, Anton

    2013-01-01

    Brain-computer interfaces (BCIs) are devices that enable people to communicate via thought alone. Brain signals can be directly translated into messages or commands. Until recently, these devices were used primarily to help people who could not move. However, BCIs are now becoming practical tools for a wide variety of people, in many different situations. What will BCIs in the future be like? Who will use them, and why? This book, written by many of the top BCI researchers and developers, reviews the latest progress in the different components of BCIs. Chapters also discuss practical issues in an emerging BCI enabled community. The book is intended both for professionals and for interested laypeople who are not experts in BCI research.

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

    Science.gov (United States)

    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.…

  20. Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface

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    Brittany Mei Young

    2014-07-01

    Full Text Available 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, and the Nine-Hole Peg Test as well as task-based fMRI scans were conducted before, during, after, and one month after therapy administration or at analogous intervals in the absence of therapy. Laterality Index (LI 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 nonlesioned hemisphere and that these brain changes are associated with changes in specific motor functions.

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

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    Seul-Ki Yeom

    Full Text Available 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.

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

    Science.gov (United States)

    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

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

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    Melissa Jiyoun Hong

    2014-04-01

    Full Text Available 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.

  4. Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see?

    Science.gov (United States)

    Bleichner, Martin G; Lundbeck, Micha; Selisky, Matthias; Minow, Falk; Jäger, Manuela; Emkes, Reiner; Debener, Stefan; De Vos, Maarten

    2015-04-01

    Electroencephalography (EEG) allows the study of the brain-behavior relationship in humans. Most of what we have learned with EEG was through observing the brain-behavior relationship under well-controlled laboratory conditions. However, by reducing "normal" behavior to a minimum the ecological validity of the results can be limited. Recent developments toward mobile EEG solutions allow to study the brain-behavior relationship outside the laboratory in more natural situations. Besides mobility and robustness with respect to motion, mobile EEG systems should also interfere as little as possible with the participant's behavior. For example, natural interaction with other people could be hindered when it is obvious that a participant wears an EEG cap. This study evaluates the signal quality obtained with an unobtrusive solution for EEG monitoring through the integration of miniaturized EEG ton-electrodes into both a discreet baseball cap and an individualized ear piece. We show that such mini electrodes located at scalp and ear locations can reliably record event related potentials in a P300 brain-computer-interface application. PMID:25847919

  5. Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application

    Directory of Open Access Journals (Sweden)

    Molina Gary N Garcia

    2003-01-01

    Full Text Available Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.

  6. Design of a Workstation for People with Upper-Limb Disabilities Using a Brain Computer Interface

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    John E. Muñoz-Cardona

    2013-11-01

    Full Text Available  This paper shows the design of work-station for work-related inclusion people upper-limb disability. The system involves the use of novel brain computer interface used to bridge the user-computer interaction. Our hope objective is elucidating functional, technological, ergonomic and procedural aspects to runaway operation station; with propose to scratch barrier to impossibility access to TIC’s tools and work done for individual disability person. We found access facility ergonomics, adaptability and portable issue of workstation are most important design criteria. Prototype implementations in workplace environment have TIR estimate of 43% for retrieve. Finally we list a typology of services that could be the most appropriate for the process of labor including: telemarketing, telesales, telephone surveys, order taking, social assistance in disasters, general information and inquiries, reservations at tourist sites, technical support, emergency, online support and after-sales services.

  7. Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

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    José del R. Millán

    2010-09-01

    Full Text Available In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT. In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication & Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI to improve BCI usability, and the development of novel BCI technology including better EEG devices.

  8. Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000.

    Science.gov (United States)

    Wilson, J Adam; Schalk, Gerwin; Walton, Léo M; Williams, Justin C

    2009-01-01

    A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features that change with the imagined movements. The results from this calibration will show the best channels to use, which are configured so that amplitude changes in the mu and beta frequency bands move the cursor either horizontally or vertically. In this experiment, the general purpose BCI system BCI2000 is used to control signal acquisition, signal processing, and feedback to the user [3]. PMID:19641479

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

    Science.gov (United States)

    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. PMID:26849869

  10. EDITORIAL: Special issue containing contributions from the Fourth International Brain-Computer Interface Meeting Special issue containing contributions from the Fourth International Brain-Computer Interface Meeting

    Science.gov (United States)

    Vaughan, Theresa M.; Wolpaw, Jonathan R.

    2011-04-01

    This special issue of Journal of Neural Engineering is a result of the Fourth International Brain-Computer Interface Meeting, which was held at the Asilomar Conference Center in Monterey, California, USA from 31 May to 4 June, 2010. The meeting was sponsored by the National Institutes of Health, The National Science Foundation and the Department of Defense, and was organized by the Wadsworth Center of the New York State Department of Health. It attracted over 260 participants from 17 countries—including many graduate students and postdoctoral fellows—and featured 19 workshops, platform presentations from 26 research groups, 170 posters, multiple brain-computer interface (BCI) demonstrations, and a keynote address by W Zev Rymer of the Rehabilitation Institute of Chicago. The number of participants and the diversity of the topics covered greatly exceeded those of the previous meeting in 2005, and testified to the continuing rapid expansion and growing sophistication of this exciting and still relatively new research field. BCI research focuses primarily on using brain signals to replace or restore the motor functions that people have lost due to amyotrophic lateral sclerosis (ALS), a brainstem stroke, or some other devastating neuromuscular disorder. In the last few years, attention has also turned towards using BCIs to improve rehabilitation after a stroke, and beyond that to enhancing or supplementing the capabilities of even those without disabilities. These diverse interests were represented in the wide range of topics covered in the workshops. While some workshops addressed broad traditional topics, such as signal acquisition, feature extraction and translation, and software development, many addressed topics that were entirely new or focused sharply on areas that have become important only recently. These included workshops on optimizing P300-based BCIs; improving the mutual adaptations of the BCI and the user; BCIs that can control neuroprostheses

  11. Current trends in hardware and software for brain-computer interfaces (BCIs)

    Science.gov (United States)

    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.

  12. A brain-computer interface method combined with eye tracking for 3D interaction.

    Science.gov (United States)

    Lee, Eui Chul; Woo, Jin Cheol; Kim, Jong Hwa; Whang, Mincheol; Park, Kang Ryoung

    2010-07-15

    With the recent increase in the number of three-dimensional (3D) applications, the need for interfaces to these applications has increased. Although the eye tracking method has been widely used as an interaction interface for hand-disabled persons, this approach cannot be used for depth directional navigation. To solve this problem, we propose a new brain computer interface (BCI) method in which the BCI and eye tracking are combined to analyze depth navigation, including selection and two-dimensional (2D) gaze direction, respectively. The proposed method is novel in the following five ways compared to previous works. First, a device to measure both the gaze direction and an electroencephalogram (EEG) pattern is proposed with the sensors needed to measure the EEG attached to a head-mounted eye tracking device. Second, the reliability of the BCI interface is verified by demonstrating that there is no difference between the real and the imaginary movements for the same work in terms of the EEG power spectrum. Third, depth control for the 3D interaction interface is implemented by an imaginary arm reaching movement. Fourth, a selection method is implemented by an imaginary hand grabbing movement. Finally, for the independent operation of gazing and the BCI, a mode selection method is proposed that measures a user's concentration by analyzing the pupil accommodation speed, which is not affected by the operation of gazing and the BCI. According to experimental results, we confirmed the feasibility of the proposed 3D interaction method using eye tracking and a BCI. PMID:20580646

  13. SSVEP and ANN based optimal speller design for Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Irshad Ahmad Ansari

    2015-07-01

    Full Text Available This work put forwards an optimal BCI (Brain Computer Interface speller design based on Steady State Visual Evoked Potentials (SSVEP and Artificial Neural Network (ANN in order to help the people with severe motor impairments. This work is carried out to enhance the accuracy and communication rate of  BCI system. To optimize the BCI system, the work has been divided into two steps: First, designing of an encoding technique to choose characters from the speller interface and the second is the development and implementation of feature extraction algorithm to acquire optimal features, which is used to train the BCI system for classification using neural network. Optimization of speller interface is focused on representation of character matrix and its designing parameters. Then again, a lot of deliberations made in order to optimize selection of features and user’s time window. Optimized system works nearly the same with the new user and gives character per minute (CPM of 13 ± 2 with an average accuracy of 94.5% by choosing first two harmonics of power spectral density as the feature vectors and using the 2 second time window for each selection. Optimized BCI performs better with experienced users with an average accuracy of 95.1%. Such a good accuracy has not been reported before in account of fair enough CPM.DOI: 10.15181/csat.v2i2.1059

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

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    Rüdiger Rupp

    2014-09-01

    Full Text Available 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.

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

    Science.gov (United States)

    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

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

    International Nuclear Information System (INIS)

    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

  17. A competitive brain computer interface: multi-person car racing system.

    Science.gov (United States)

    Li, Junhua; Liu, Ye; Lu, Zhen; Zhang, Liqing

    2013-01-01

    Brain computer interface (BCI) technique is successfully utilized to bridge the interruption between brain and peripheral nerves and muscles, and to establish a new pathway making brain directly output information (or command). Up to now, a majority of BCI systems are developed to restore communication ability or movement functionality for people with severe disabilities, especially for paralyzed patients. To our best knowledge, other researchers haven't developed a multi-person BCI with competitive mode. Therefore, in this paper, we introduced a multi-person car racing system, which allows more than one person to play game at the same time and they can compete with each other for the aim of first reaching destination. The reason of development of car racing system has two aspects. At one hand, we introduced BCI to entertainment industry and provided a prototype for entertainment. At the other hand, we proposed a competitive mode for BCI. According to practical evaluation, the results demonstrated that our proposed system achieved a good performance. PMID:24110159

  18. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

    Science.gov (United States)

    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

  19. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation

    Directory of Open Access Journals (Sweden)

    Robert eBauer

    2015-02-01

    Full Text Available Restorative brain-computer interfaces (BCI are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation.In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

  20. Personality Trait and Facial Expression Filter-Based Brain-Computer Interface

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    Seongah Chin

    2013-02-01

    Full Text Available In this paper, we present technical approaches that bridge the gap in the research related to the use of brain‐computer interfaces for entertainment and facial expressions. Such facial expressions that reflect an individual’s personal traits can be used to better realize artificial facial expressions in a gaming environment based on a brain‐computer interface. First, an emotion extraction filter is introduced in order to classify emotions on the basis of the users’ brain signals in real time. Next, a personality trait filter is defined to classify extrovert and introvert types, which manifest as five traits: very extrovert, extrovert, medium, introvert and very introvert. In addition, facial expressions derived from expression rates are obtained by an extrovert‐introvert fuzzy model through its defuzzification process. Finally, we confirm this validation via an analysis of the variance of the personality trait filter, a k‐fold cross validation of the emotion extraction filter, an accuracy analysis, a user study of facial synthesis and a test case game.

  1. Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface.

    Science.gov (United States)

    Aranyi, Gabor; Pecune, Florian; Charles, Fred; Pelachaud, Catherine; Cavazza, Marc

    2016-01-01

    Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance. The magnitude of left-asymmetric DL-PFC activity, measured using functional near infrared spectroscopy (fNIRS) and treated as a proxy for approach, is mapped onto a control mechanism for the virtual agent's facial expressions, in which action units (AUs) are activated through a neural network. We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent's responses as realistic and consistent with their projected mental disposition. This interaction paradigm is particularly relevant in the case of affective BCI as it facilitates the volitional activation of specific areas normally not under conscious control. Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment. PMID:27462216

  2. Brain Computer Interface Learning for Systems Based on Electrocorticography and Intracortical Microelectrode Arrays

    Directory of Open Access Journals (Sweden)

    Shivayogi V Hiremath

    2015-06-01

    Full Text Available 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.

  3. A brain-computer interface as input channel for a standard assistive technology software.

    Science.gov (United States)

    Zickler, Claudia; Riccio, Angela; Leotta, Francesco; Hillian-Tress, Sandra; Halder, Sebastian; Holz, Elisa; Staiger-Sälzer, Pit; Hoogerwerf, Evert-Jan; Desideri, Lorenzo; Mattia, Donatella; Kübler, Andrea

    2011-10-01

    Recently brain-computer interface (BCI) control was integrated into the commercial assistive technology product QualiWORLD (QualiLife Inc., Paradiso-Lugano, CH). Usability of the first prototype was evaluated in terms of effectiveness (accuracy), efficiency (information transfer rate and subjective workload/NASA Task Load Index) and user satisfaction (Quebec User Evaluation of Satisfaction with assistive Technology, QUEST 2.0) by four end-users with severe disabilities. Three assistive technology experts evaluated the device from a third person perspective. The results revealed high performance levels in communication and internet tasks. Users and assistive technology experts were quite satisfied with the device. However, none could imagine using the device in daily life without improvements. Main obstacles were the EEG-cap and low speed. PMID:22208121

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

    Directory of Open Access Journals (Sweden)

    Nikolay V. Manyakov

    2011-01-01

    Full Text Available 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.

  5. A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface

    Science.gov (United States)

    Cavrini, Francesco; Quitadamo, Lucia Rita; Saggio, Giovanni

    2016-01-01

    We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI) based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control. PMID:26819595

  6. DARPA-funded efforts in the development of novel brain-computer interface technologies.

    Science.gov (United States)

    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. PMID:25107852

  7. Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications.

    Science.gov (United States)

    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. PMID:26953174

  8. Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control.

    Science.gov (United States)

    Golub, Matthew D; Chase, Steven M; Batista, Aaron P; Yu, Byron M

    2016-04-01

    Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain-computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control. PMID:26796293

  9. Bipolar electrode selection for a motor imagery based brain computer interface

    Science.gov (United States)

    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.

  10. 脑-机接口技术综述%Review of brain-computer interface technology

    Institute of Scientific and Technical Information of China (English)

    沈敏

    2007-01-01

    脑-机接口(brain computer interface,BCI)是在人脑与计算机或其它电子设备之间建立的直接的交流和控制通道,通过这种通道,人就可以直接通过脑来表达想法或操纵设备,而不需要语言或动作.脑-机接口是一种全新的通讯和控制技术.对脑-机接口技术的发展、研究现状、工作原理以及涉及的关键技术进行了较为详细地综述.

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

    Directory of Open Access Journals (Sweden)

    Ali Bashashati

    2007-01-01

    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.

  12. Recursive N-way partial least squares for brain-computer interface.

    Directory of Open Access Journals (Sweden)

    Andrey Eliseyev

    Full Text Available In the article tensor-input/tensor-output blockwise Recursive N-way Partial Least Squares (RNPLS regression is considered. It combines the multi-way tensors decomposition with a consecutive calculation scheme and allows blockwise treatment of tensor data arrays with huge dimensions, as well as the adaptive modeling of time-dependent processes with tensor variables. In the article the numerical study of the algorithm is undertaken. The RNPLS algorithm demonstrates fast and stable convergence of regression coefficients. Applied to Brain Computer Interface system calibration, the algorithm provides an efficient adjustment of the decoding model. Combining the online adaptation with easy interpretation of results, the method can be effectively applied in a variety of multi-modal neural activity flow modeling tasks.

  13. Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    Xu Lei; Ping Yang; Peng Xu; Tie-Jun Liu; De-Zhong Yao

    2009-01-01

    Common spatial pattern (CSP) algorithm is a successful tool in feature estimate of brain-computer interface (BCI).However,CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials.In this paper,we propose a simple yet effective approach,named common spatial pattern ensemble (CSPE) classifier,to improve CSP performance.Through division of recording channels,multiple CSP filters are constructed.By projection,log-operation,and subtraction on the original signal,an ensemble classifier,majority voting,is achieved and outlier contaminations are alleviated.Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.

  14. Coordinated control of an intelligent wheelchair based on a brain-computer interface and speech recognition

    Institute of Scientific and Technical Information of China (English)

    Hong-tao WANG; Yuan-qing LI; Tian-you YU

    2014-01-01

    An intelligent wheelchair is devised, which is controlled by a coordinated mechanism based on a brain-computer interface (BCI) and speech recognition. By performing appropriate activities, users can navigate the wheelchair with four steering behaviors (start, stop, turn left, and turn right). Five healthy subjects participated in an indoor experiment. The results demonstrate the efficiency of the coordinated control mechanism with satisfactory path and time optimality ratios, and show that speech recognition is a fast and accurate supplement for BCI-based control systems. The proposed intelligent wheelchair is especially suitable for patients suffering from paralysis (especially those with aphasia) who can learn to pronounce only a single sound (e.g.,‘ah’).

  15. EEG Signal Denoising and Feature Extraction Using Wavelet Transform in Brain Computer Interface

    Institute of Scientific and Technical Information of China (English)

    WU Ting; YAN Guo-zheng; YANG Bang-hua; SUN Hong

    2007-01-01

    Electroencephalogram (EEG) signal preprocessing is one of the most important techniques in brain computer interface (BCI). The target is to increase signal-to-noise ratio and make it more favorable for feature extraction and pattern recognition. Wavelet transform is a method of multi-resolution time-frequency analysis, it can decompose the mixed signals which consist of different frequencies into different frequency band. EEG signal is analyzed and denoised using wavelet transform. Moreover, wavelet transform can be used for EEG feature extraction. The energies of specific sub-bands and corresponding decomposition coefficients which have maximal separability according to the Fisher distance criterion are selected as features. The eigenvector for classification is obtained by combining the effective features from different channels. The performance is evaluated by separability and pattern recognition accuracy using the data set of BCI 2003 Competition, the final classification results have proved the effectiveness of this technology for EEG denoising and feature extraction.

  16. Flashing characters with famous faces improves ERP-based brain-computer interface performance

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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. PMID:25570771

  18. Impact of spatial filters during sensor selection in a visual P300 brain-computer interface.

    Science.gov (United States)

    Rivet, B; Cecotti, H; Maby, E; Mattout, J

    2012-01-01

    A challenge in designing a Brain-Computer Interface (BCI) is the choice of the channels, e.g. the most relevant sensors. Although a setup with many sensors can be more efficient for the detection of Event-Related Potential (ERP) like the P300, it is relevant to consider only a low number of sensors for a commercial or clinical BCI application. Indeed, a reduced number of sensors can naturally increase the user comfort by reducing the time required for the installation of the EEG (electroencephalogram) cap and can decrease the price of the device. In this study, the influence of spatial filtering during the process of sensor selection is addressed. Two of them maximize the Signal to Signal-plus-Noise Ratio (SSNR) for the different sensor subsets while the third one maximizes the differences between the averaged P300 waveform and the non P300 waveform. We show that the locations of the most relevant sensors subsets for the detection of the P300 are highly dependent on the use of spatial filtering. Applied on data from 20 healthy subjects, this study proves that subsets obtained where sensors are suppressed in relation to their individual SSNR are less efficient than when sensors are suppressed in relation to their contribution once the different selected sensors are combined for enhancing the signal. In other words, it highlights the difference between estimating the P300 projection on the scalp and evaluating the more efficient sensor subsets for a P300-BCI. Finally, this study explores the issue of channel commonality across subjects. The results support the conclusion that spatial filters during the sensor selection procedure allow selecting better sensors for a visual P300 Brain-Computer Interface. PMID:21744296

  19. Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system

    Science.gov (United States)

    Robinson, Neethu; Guan, Cuntai; Vinod, A. P.

    2015-12-01

    Objective. The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. Approach. EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. Main results. The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p {\\lt }0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational

  20. An online three-class Transcranial Doppler ultrasound brain computer interface.

    Science.gov (United States)

    Goyal, Anuja; Samadani, Ali-Akbar; Guerguerian, Anne-Marie; Chau, Tom

    2016-06-01

    Brain computer interfaces (BCI) can provide communication opportunities for individuals with severe motor disabilities. Transcranial Doppler ultrasound (TCD) measures cerebral blood flow velocities and can be used to develop a BCI. A previously implemented TCD BCI system used verbal and spatial tasks as control signals; however, the spatial task involved a visual cue that awkwardly diverted the user's attention away from the communication interface. Therefore, vision-independent right-lateralized tasks were investigated. Using a bilateral TCD BCI, ten participants controlled online, an on-screen keyboard using a left-lateralized task (verbal fluency), a right-lateralized task (fist motor imagery or 3D-shape tracing), and unconstrained rest. 3D-shape tracing was generally more discernible from other tasks than was fist motor imagery. Verbal fluency, 3D-shape tracing and unconstrained rest were distinguished from each other using a linear discriminant classifier, achieving a mean agreement of κ=0.43±0.17. These rates are comparable to the best offline three-class TCD BCI accuracies reported thus far. The online communication system achieved a mean information transfer rate (ITR) of 1.08±0.69bits/min with values reaching up to 2.46bits/min, thereby exceeding the ITR of previous online TCD BCIs. These findings demonstrate the potential of a three-class online TCD BCI that does not require visual task cues. PMID:26795195

  1. sBCI-Headset—Wearable and Modular Device for Hybrid Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Tatsiana Malechka

    2015-02-01

    Full Text Available Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI. In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP-BCI, an Event-related (de-synchronization (ERD/ERS-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a “hands-free” handling of devices is necessary.

  2. Prediction of motor imagery based brain computer interface performance using a reaction time test.

    Science.gov (United States)

    Darvishi, Sam; Abbott, Derek; Baumert, Mathias

    2015-08-01

    Brain computer interfaces (BCIs) enable human brains to interact directly with machines. Motor imagery based BCI (MI-BCI) encodes the motor intentions of human agents and provides feedback accordingly. However, 15-30% of people are not able to perform vivid motor imagery. To save time and monetary resources, a number of predictors have been proposed to screen for users with low BCI aptitude. While the proposed predictors provide some level of correlation with MI-BCI performance, simple, objective and accurate predictors are currently not available. Thus, in this study we have examined the utility of a simple reaction time (SRT) test for predicting MI-BCI performance. We enrolled 10 subjects and measured their motor imagery performance with either visual or proprioceptive feedback. Their reaction time was also measured using a SRT test. The results show a significant negative correlation (r ≈ -0.67) between SRT and MI-BCI performance. Therefore SRT may be used as a simple and reliable predictor of MI-BCI performance. PMID:26736893

  3. A telepresence mobile robot controlled with a noninvasive brain-computer interface.

    Science.gov (United States)

    Escolano, Carlos; Antelis, Javier Mauricio; Minguez, Javier

    2012-06-01

    This paper reports an electroencephalogram-based brain-actuated telepresence system to provide a user with presence in remote environments through a mobile robot, with access to the Internet. This system relies on a P300-based brain-computer interface (BCI) and a mobile robot with autonomous navigation and camera orientation capabilities. The shared-control strategy is built by the BCI decoding of task-related orders (selection of visible target destinations or exploration areas), which can be autonomously executed by the robot. The system was evaluated using five healthy participants in two consecutive steps: 1) screening and training of participants and 2) preestablished navigation and visual exploration telepresence tasks. On the basis of the results, the following evaluation studies are reported: 1) technical evaluation of the device and its main functionalities and 2) the users' behavior study. The overall result was that all participants were able to complete the designed tasks, reporting no failures, which shows the robustness of the system and its feasibility to solve tasks in real settings where joint navigation and visual exploration were needed. Furthermore, the participants showed great adaptation to the telepresence system. PMID:22180512

  4. Brain-computer interfaces in the completely locked-in state and chronic stroke.

    Science.gov (United States)

    Chaudhary, U; Birbaumer, N; Ramos-Murguialday, A

    2016-01-01

    Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery. PMID:27590968

  5. Multi-channel linear descriptors for event-related EEG collected in brain computer interface

    Science.gov (United States)

    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 (Ω), field power (Σ) and frequency of field changes (Φ), 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 Ω, Σ and Φ 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 Ω, Σ and Φ for characterizing event-related EEG. The preliminary results show that Ω, Σ together with Φ 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.

  6. Development of a Wearable Motor-Imagery-Based Brain-Computer Interface.

    Science.gov (United States)

    Lin, Bor-Shing; Pan, Jeng-Shyang; Chu, Tso-Yao; Lin, Bor-Shyh

    2016-03-01

    A motor-imagery-based brain-computer interface (BCI) is a translator that converts the motor intention of the brain into a control command to control external machines without muscles. Numerous motor-imagery-based BCIs have been successfully proposed in previous studies. However, several electroencephalogram (EEG) channels are typically required for providing sufficient information to maintain a specific accuracy and bit rate, and the bulk volume of these EEG machines is also inconvenient. A wearable motor imagery-based BCI system was proposed and implemented in this study. A wearable mechanical design with novel active comb-shaped dry electrodes was developed to measure EEG signals without conductive gels at hair sites, which is easy and convenient for users wearing the EEG machine. In addition, a wireless EEG acquisition module was also designed to measure EEG signals, which provides a user with more freedom of motion. The proposed wearable motor-imagery-based BCI system was validated using an electrical specifications test and a hand motor imagery experiment. Experimental results showed that the proposed wearable motor-imagery-based BCI system provides favorable signal quality for measuring EEG signals and detecting motor imagery. PMID:26748791

  7. Using a cVEP-Based Brain-Computer Interface to Control a Virtual Agent.

    Science.gov (United States)

    Riechmann, Hannes; Finke, Andrea; Ritter, Helge

    2016-06-01

    Brain-computer interfaces provide a means for controlling a device by brain activity alone. One major drawback of noninvasive BCIs is their low information transfer rate, obstructing a wider deployment outside the lab. BCIs based on codebook visually evoked potentials (cVEP) outperform all other state-of-the-art systems in that regard. Previous work investigated cVEPs for spelling applications. We present the first cVEP-based BCI for use in real-world settings to accomplish everyday tasks such as navigation or action selection. To this end, we developed and evaluated a cVEP-based on-line BCI that controls a virtual agent in a simulated, but realistic, 3-D kitchen scenario. We show that cVEPs can be reliably triggered with stimuli in less restricted presentation schemes, such as on dynamic, changing backgrounds. We introduce a novel, dynamic repetition algorithm that allows for optimizing the balance between accuracy and speed individually for each user. Using these novel mechanisms in a 12-command cVEP-BCI in the 3-D simulation results in ITRs of 50 bits/min on average and 68 bits/min maximum. Thus, this work supports the notion of cVEP-BCIs as a particular fast and robust approach suitable for real-world use. PMID:26469340

  8. The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces

    Directory of Open Access Journals (Sweden)

    J. Clark Powers

    2015-08-01

    Full Text Available Individuals with severe neuromuscular impairments face many challenges in communication and manipulation of the environment. Brain-computer interfaces (BCIs show promise in presenting real-world applications that can provide such individuals with the means to interact with the world using only brain waves. Although there has been a growing body of research in recent years, much relates only to technology, and not to technology in use—i.e., real-world assistive technology employed by users. This review examined the literature to highlight studies that implicate the human factors and ergonomics (HFE of P300-based BCIs. We assessed 21 studies on three topics to speak directly to improving the HFE of these systems: (1 alternative signal evocation methods within the oddball paradigm; (2 environmental interventions to improve user performance and satisfaction within the constraints of current BCI systems; and (3 measures and methods of measuring user acceptance. We found that HFE is central to the performance of P300-based BCI systems, although researchers do not often make explicit this connection. Incorporation of measures of user acceptance and rigorous usability evaluations, increased engagement of disabled users as test participants, and greater realism in testing will help progress the advancement of P300-based BCI systems in assistive applications.

  9. A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection.

    Science.gov (United States)

    Wang, Fei; He, Yanbin; Pan, Jiahui; Xie, Qiuyou; Yu, Ronghao; Zhang, Rui; Li, Yuanqing

    2015-01-01

    Currently, detecting awareness in patients with disorders of consciousness (DOC) is a challenging task, which is commonly addressed through behavioral observation scales such as the JFK Coma Recovery Scale-Revised. Brain-computer interfaces (BCIs) provide an alternative approach to detect awareness in patients with DOC. However, these patients have a much lower capability of using BCIs compared to healthy individuals. This study proposed a novel BCI using temporally, spatially, and semantically congruent audiovisual stimuli involving numbers (i.e., visual and spoken numbers). Subjects were instructed to selectively attend to the target stimuli cued by instruction. Ten healthy subjects first participated in the experiment to evaluate the system. The results indicated that the audiovisual BCI system outperformed auditory-only and visual-only systems. Through event-related potential analysis, we observed audiovisual integration effects for target stimuli, which enhanced the discriminability between brain responses for target and nontarget stimuli and thus improved the performance of the audiovisual BCI. This system was then applied to detect the awareness of seven DOC patients, five of whom exhibited command following as well as number recognition. Thus, this audiovisual BCI system may be used as a supportive bedside tool for awareness detection in patients with DOC. PMID:26123281

  10. The Changes in the Hemodynamic Activity of the Brain during Motor Imagery Training with the Use of Brain-Computer Interface1

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Silchenko, A.V.; Tintěra, J.; Rydlo, J.

    2016-01-01

    Roč. 42, č. 1 (2016), s. 1-12. ISSN 0362-1197 R&D Projects: GA MŠk ED1.1.00/02.0070 Grant ostatní: GA MŠk(CZ) EE.2.3.20.0073 Institutional support: RVO:67985807 Keywords : brain-computer interface * motor imagery * hemodynamic activity * brain plasticity * functional MRI Subject RIV: IN - Informatics, Computer Science

  11. Towards Brain-Computer Interface Control of a 6-Degree-of-Freedom Robotic Arm Using Dry EEG Electrodes

    OpenAIRE

    Alexander Astaras; Nikolaos Moustakas; Alkinoos Athanasiou; Aristides Gogoussis

    2013-01-01

    Introduction. Development of a robotic arm that can be operated using an exoskeletal position sensing harness as well as a dry electrode brain-computer interface headset. Design priorities comprise an intuitive and immersive user interface, fast and smooth movement, portability, and cost minimization. Materials and Methods. A robotic arm prototype capable of moving along 6 degrees of freedom has been developed, along with an exoskeletal position sensing harness which was used to control it. C...

  12. Minimizing inter-subject variability in fNIRS based Brain Computer Interfaces via multiple-kernel support vector learning

    OpenAIRE

    Abibullaev, Berdakh; An, Jinung; Lee, Seung-Hyun; Jin, Sang-Hyeon; Moon, Jeon-Il

    2012-01-01

    Brain signal variability in the measurements obtained from different subjects during different sessions significantly deteriorates the accuracy of most brain-computer interface (BCI) systems. Moreover these variabilities, also known as inter-subject or inter-session variabilities, require lengthy calibration sessions before the BCI system can be used. Furthermore, the calibration session has to be repeated for each subject independently and before use of the BCI due to the inter-session varia...

  13. A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions

    OpenAIRE

    Kashihara, Koji

    2014-01-01

    Unlike assistive technology for verbal communication, the brain-machine or brain-computer interface (BMI/BCI) has not been established as a non-verbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial...

  14. An independent SSVEP-based brain-computer interface in locked-in syndrome

    Science.gov (United States)

    Lesenfants, D.; Habbal, D.; Lugo, Z.; Lebeau, M.; Horki, P.; Amico, E.; Pokorny, C.; Gómez, F.; Soddu, A.; Müller-Putz, G.; Laureys, S.; Noirhomme, Q.

    2014-06-01

    Objective. Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) allow healthy subjects to communicate. However, their dependence on gaze control prevents their use with severely disabled patients. Gaze-independent SSVEP-BCIs have been designed but have shown a drop in accuracy and have not been tested in brain-injured patients. In the present paper, we propose a novel independent SSVEP-BCI based on covert attention with an improved classification rate. We study the influence of feature extraction algorithms and the number of harmonics. Finally, we test online communication on healthy volunteers and patients with locked-in syndrome (LIS). Approach. Twenty-four healthy subjects and six LIS patients participated in this study. An independent covert two-class SSVEP paradigm was used with a newly developed portable light emitting diode-based ‘interlaced squares' stimulation pattern. Main results. Mean offline and online accuracies on healthy subjects were respectively 85 ± 2% and 74 ± 13%, with eight out of twelve subjects succeeding to communicate efficiently with 80 ± 9% accuracy. Two out of six LIS patients reached an offline accuracy above the chance level, illustrating a response to a command. One out of four LIS patients could communicate online. Significance. We have demonstrated the feasibility of online communication with a covert SSVEP paradigm that is truly independent of all neuromuscular functions. The potential clinical use of the presented BCI system as a diagnostic (i.e., detecting command-following) and communication tool for severely brain-injured patients will need to be further explored.

  15. Eye-gaze independent EEG-based brain-computer interfaces for communication

    Science.gov (United States)

    Riccio, A.; Mattia, D.; Simione, L.; Olivetti, M.; Cincotti, F.

    2012-08-01

    The present review systematically examines the literature reporting gaze independent interaction modalities in non-invasive brain-computer interfaces (BCIs) for communication. BCIs measure signals related to specific brain activity and translate them into device control signals. This technology can be used to provide users with severe motor disability (e.g. late stage amyotrophic lateral sclerosis (ALS); acquired brain injury) with an assistive device that does not rely on muscular contraction. Most of the studies on BCIs explored mental tasks and paradigms using visual modality. Considering that in ALS patients the oculomotor control can deteriorate and also other potential users could have impaired visual function, tactile and auditory modalities have been investigated over the past years to seek alternative BCI systems which are independent from vision. In addition, various attentional mechanisms, such as covert attention and feature-directed attention, have been investigated to develop gaze independent visual-based BCI paradigms. Three areas of research were considered in the present review: (i) auditory BCIs, (ii) tactile BCIs and (iii) independent visual BCIs. Out of a total of 130 search results, 34 articles were selected on the basis of pre-defined exclusion criteria. Thirteen articles dealt with independent visual BCIs, 15 reported on auditory BCIs and the last six on tactile BCIs, respectively. From the review of the available literature, it can be concluded that a crucial point is represented by the trade-off between BCI systems/paradigms with high accuracy and speed, but highly demanding in terms of attention and memory load, and systems requiring lower cognitive effort but with a limited amount of communicable information. These issues should be considered as priorities to be explored in future studies to meet users’ requirements in a real-life scenario.

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

    NARCIS (Netherlands)

    Horschig, J.M.; Oosterheert, W.; Oostenveld, R.; Jensen, O.

    2014-01-01

    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 direct

  17. A Brain Computer Interface for Robust Wheelchair Control Application Based on Pseudorandom Code Modulated Visual Evoked Potential

    DEFF Research Database (Denmark)

    Mohebbi, Ali; Engelsholm, Signe K.D.; Puthusserypady, Sadasivan;

    2015-01-01

    In this pilot study, a novel and minimalistic Brain Computer Interface (BCI) based wheelchair control application was developed. The system was based on pseudorandom code modulated Visual Evoked Potentials (c-VEPs). The visual stimuli in the scheme were generated based on the Gold code, and the...

  18. Simulation of a real-time brain computer interface for detecting a self-paced hitting task

    DEFF Research Database (Denmark)

    Hammad, Sofyan H.; Kamavuako, Ernest N.; Farina, Dario;

    2016-01-01

    OBJECTIVES: An invasive brain-computer interface (BCI) is a promising neurorehabilitation device for severely disabled patients. Although some systems have been shown to work well in restricted laboratory settings, their utility must be tested in less controlled, real-time environments. Our...

  19. A cell-phone-based brain-computer interface for communication in daily life

    Science.gov (United States)

    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.

  20. Manipulating attention via mindfulness induction improves P300-based brain-computer interface performance

    Science.gov (United States)

    Lakey, Chad E.; Berry, Daniel R.; Sellers, Eric W.

    2011-04-01

    In this study, we examined the effects of a short mindfulness meditation induction (MMI) on the performance of a P300-based brain-computer interface (BCI) task. We expected that MMI would harness present-moment attentional resources, resulting in two positive consequences for P300-based BCI use. Specifically, we believed that MMI would facilitate increases in task accuracy and promote the production of robust P300 amplitudes. Sixteen-channel electroencephalographic data were recorded from 18 subjects using a row/column speller task paradigm. Nine subjects participated in a 6 min MMI and an additional nine subjects served as a control group. Subjects were presented with a 6 × 6 matrix of alphanumeric characters on a computer monitor. Stimuli were flashed at a stimulus onset asynchrony (SOA) of 125 ms. Calibration data were collected on 21 items without providing feedback. These data were used to derive a stepwise linear discriminate analysis classifier that was applied to an additional 14 items to evaluate accuracy. Offline performance analyses revealed that MMI subjects were significantly more accurate than control subjects. Likewise, MMI subjects produced significantly larger P300 amplitudes than control subjects at Cz and PO7. The discussion focuses on the potential attentional benefits of MMI for P300-based BCI performance.

  1. Brain computer interfaces as intelligent sensors for enhancing human-computer interaction

    NARCIS (Netherlands)

    Poel, M.; Nijboer, F.; Broek, E.L. van den; Fairclough, S.; Nijholt, A.

    2012-01-01

    BCIs are traditionally conceived as a way to control apparatus, an interface that allows you to act on" external devices as a form of input control. We propose an alternative use of BCIs, that of monitoring users as an additional intelligent sensor to enrich traditional means of interaction. This vi

  2. Computer interfacing

    CERN Document Server

    Dixey, Graham

    1994-01-01

    This book explains how computers interact with the world around them and therefore how to make them a useful tool. Topics covered include descriptions of all the components that make up a computer, principles of data exchange, interaction with peripherals, serial communication, input devices, recording methods, computer-controlled motors, and printers.In an informative and straightforward manner, Graham Dixey describes how to turn what might seem an incomprehensible 'black box' PC into a powerful and enjoyable tool that can help you in all areas of your work and leisure. With plenty of handy

  3. Brain-Computer Interface Controlled Functional Electrical Stimulation System for Ankle Movement

    Directory of Open Access Journals (Sweden)

    King Christine E

    2011-08-01

    Full Text Available Abstract Background 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. Methods 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. Results 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. Conclusions This study suggests that the integration of a noninvasive BCI with a lower

  4. Steady state visually evoked potentials based Brain computer interface test outside the lab

    Directory of Open Access Journals (Sweden)

    Eduardo Francisco Caicedo Bravo

    2016-06-01

    Full Text Available Context: Steady State Visually Evoked Potentials (SSVEP are brain signals which are one of the most promising signals for Brain Computer Interfaces (BCIs implementation, however, SSVEP based BCI generally are proven in a controlled environment and there are a few tests in demanding conditions.Method: We present a SSVEP based BCI system that was used outside the lab in a noisy environment with distractions, and with the presence of public. For the tests, we showed a maze in a laptop where the user could move an avatar looking for a target that is represented by a house.  In order to move the avatar, the volunteer must stare at one of the four visual stimuli; the four visual stimuli represent the four directions: right, up, left, and down. The system is proven without any calibration procedure.Results: 32 volunteers utilized the system and 20 achieved the target with an accuracy above 60%, including 9 with an accuracy of 100%, 7 achieved the target with an accuracy below 60% and 5 left without achieving the goal. For the volunteers who reached accuracy above 60%, the results of the performance achieved an average of 6,4s for command detections, precision of 79% and information transfer rate (ITR of 8,78 bits/s.Conclusions: We showed a SSVEP based BCI system with low cost, it was proved in a public event, it did not have calibration procedures, it was easy to install, and it was used for people in a wide age range. The results show that it is possible to bring this kind of systems to environments outside the laboratory.

  5. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface.

    Science.gov (United States)

    Mrachacz-Kersting, Natalie; Jiang, Ning; Stevenson, Andrew James Thomas; Niazi, Imran Khan; Kostic, Vladimir; Pavlovic, Aleksandra; Radovanovic, Sasa; Djuric-Jovicic, Milica; Agosta, Federica; Dremstrup, Kim; Farina, Dario

    2016-03-01

    Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here we evaluated the effect and the underlying mechanisms of three BCI training sessions in a double-blind sham-controlled design. The applied BCI is based on Hebbian principles of associativity that hypothesize that neural assemblies activated in a correlated manner will strengthen synaptic connections. Twenty-two chronic stroke patients were divided into two training groups. Movement-related cortical potentials (MRCPs) were detected by electroencephalography during repetitions of foot dorsiflexion. Detection triggered a single electrical stimulation of the common peroneal nerve timed so that the resulting afferent volley arrived at the peak negative phase of the MRCP (BCIassociative group) or randomly (BCInonassociative group). Fugl-Meyer motor assessment (FM), 10-m walking speed, foot and hand tapping frequency, diffusion tensor imaging (DTI) data, and the excitability of the corticospinal tract to the target muscle [tibialis anterior (TA)] were quantified. The TA motor evoked potential (MEP) increased significantly after the BCIassociative intervention, but not for the BCInonassociative group. FM scores (0.8 ± 0.46 point difference, P = 0.01), foot (but not finger) tapping frequency, and 10-m walking speed improved significantly for the BCIassociative group, indicating clinically relevant improvements. Corticospinal tract integrity on DTI did not correlate with clinical or physiological changes. For the BCI as applied here, the precise coupling between the brain command and the afferent signal was imperative for the behavioral, clinical, and neurophysiological changes reported. This association may become the driving principle for the design of BCI rehabilitation in the future. Indeed, no available BCIs can match this degree of functional improvement with such a short intervention. PMID:26719088

  6. A combination strategy based brain-computer interface for two-dimensional movement control

    Science.gov (United States)

    Xia, Bin; Maysam, Oladazimi; Veser, Sandra; Cao, Lei; Li, Jie; Jia, Jie; Xie, Hong; Birbaumer, Niels

    2015-08-01

    Objective. Two-dimensional (2D) movement control is an important issue in brain-computer interfaces (BCIs) research because being able to move, for example, a cursor with the brain will enable patients with motor disabilities to control their environment. However, it is still a challenge to continuously control 2D movement with a non-invasive BCI system. In this paper, we developed a 2D cursor control with motor imagery BCI tasks allowing users to move a cursor to any position by using a combination strategy. With this strategy, a user can combine multiple motor imagery tasks, alternatively or simultaneously, to control 2D movements. Approach. After a training session, six participants took part in the first control strategy experiment (the center-out experiment) to verify the effectiveness of the cursor control. Three of the six participants performed an additional experiment, in which they were required to move the cursor to hit five targets in a given sequence. Main results. The average hit rate was more than 95.6% and the trajectories were close to the shortest path. The average hit rate was more than 95.6% and the trajectories were close to the shortest path in the center-out experiment. In the additional experiment, three participants achieved a 100% hit rate with a short trajectory. Significance. The results demonstrated that users were able to effectively control the 2D movement using the proposed strategy. The present system may be used as a tool to interact with the external world.

  7. Detecting awareness in patients with disorders of consciousness using a hybrid brain-computer interface

    Science.gov (United States)

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

    2014-10-01

    Objective. The bedside detection of potential awareness in patients with disorders of consciousness (DOC) currently relies only on behavioral observations and tests; however, the misdiagnosis rates in this patient group are historically relatively high. In this study, we proposed a visual hybrid brain-computer interface (BCI) combining P300 and steady-state evoked potential (SSVEP) responses to detect awareness in severely brain injured patients. Approach. Four healthy subjects, seven DOC patients who were in a vegetative state (VS, n = 4) or minimally conscious state (MCS, n = 3), and one locked-in syndrome (LIS) patient attempted a command-following experiment. In each experimental trial, two photos were presented to each patient; one was the patient's own photo, and the other photo was unfamiliar. The patients were instructed to focus on their own or the unfamiliar photos. The BCI system determined which photo the patient focused on with both P300 and SSVEP detections. Main results. Four healthy subjects, one of the 4 VS, one of the 3 MCS, and the LIS patient were able to selectively attend to their own or the unfamiliar photos (classification accuracy, 66-100%). Two additional patients (one VS and one MCS) failed to attend the unfamiliar photo (50-52%) but achieved significant accuracies for their own photo (64-68%). All other patients failed to show any significant response to commands (46-55%). Significance. Through the hybrid BCI system, command following was detected in four healthy subjects, two of 7 DOC patients, and one LIS patient. We suggest that the hybrid BCI system could be used as a supportive bedside tool to detect awareness in patients with DOC.

  8. Towards a symbiotic brain-computer interface: exploring the application-decoder interaction

    Science.gov (United States)

    Verhoeven, T.; Buteneers Wiersema, P., Jr.; Dambre, J.; Kindermans, PJ

    2015-12-01

    Objective. State of the art brain-computer interface (BCI) research focuses on improving individual components such as the application or the decoder that converts the user’s brain activity to control signals. In this study, we investigate the interaction between these components in the P300 speller, a BCI for communication. We introduce a synergistic approach in which the stimulus presentation sequence is modified to enhance the machine learning decoding. In this way we aim for an improved overall BCI performance. Approach. First, a new stimulus presentation paradigm is introduced which provides us flexibility in tuning the sequence of visual stimuli presented to the user. Next, an experimental setup in which this paradigm is compared to other paradigms uncovers the underlying mechanism of the interdependence between the application and the performance of the decoder. Main results. Extensive analysis of the experimental results reveals the changing requirements of the decoder concerning the data recorded during the spelling session. When few data is recorded, the balance in the number of target and non-target stimuli shown to the user is more important than the signal-to-noise rate (SNR) of the recorded response signals. Only when more data has been collected, the SNR becomes the dominant factor. Significance. For BCIs in general, knowing the dominant factor that affects the decoder performance and being able to respond to it is of utmost importance to improve system performance. For the P300 speller, the proposed tunable paradigm offers the possibility to tune the application to the decoder’s needs at any time and, as such, fully exploit this application-decoder interaction.

  9. Computational Assessment of Neural Probe and Brain Tissue Interface under Transient Motion

    Directory of Open Access Journals (Sweden)

    Michael Polanco

    2016-06-01

    Full Text Available The functional longevity of a neural probe is dependent upon its ability to minimize injury risk during the insertion and recording period in vivo, which could be related to motion-related strain between the probe and surrounding tissue. A series of finite element analyses was conducted to study the extent of the strain induced within the brain in an area around a neural probe. This study focuses on the transient behavior of neural probe and brain tissue interface with a viscoelastic model. Different stages of the interface from initial insertion of neural probe to full bonding of the probe by astro-glial sheath formation are simulated utilizing analytical tools to investigate the effects of relative motion between the neural probe and the brain while friction coefficients and kinematic frequencies are varied. The analyses can provide an in-depth look at the quantitative benefits behind using soft materials for neural probes.

  10. Selective sensation based brain-computer interface via mechanical vibrotactile stimulation.

    Directory of Open Access Journals (Sweden)

    Lin Yao

    Full Text Available In this work, mechanical vibrotactile stimulation was applied to subjects' left and right wrist skins with equal intensity, and a selective sensation perception task was performed to achieve two types of selections similar to motor imagery Brain-Computer Interface. The proposed system was based on event-related desynchronization/synchronization (ERD/ERS, which had a correlation with processing of afferent inflow in human somatosensory system, and attentional effect which modulated the ERD/ERS. The experiments were carried out on nine subjects (without experience in selective sensation, and six of them showed a discrimination accuracy above 80%, three of them above 95%. Comparative experiments with motor imagery (with and without presence of stimulation were also carried out, which further showed the feasibility of selective sensation as an alternative BCI task complementary to motor imagery. Specifically there was significant improvement ([Formula: see text] from near 65% in motor imagery (with and without presence of stimulation to above 80% in selective sensation on some subjects. The proposed BCI modality might well cooperate with existing BCI modalities in the literature in enlarging the widespread usage of BCI system.

  11. Comparison of dry and gel based electrodes for P300 brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Christoph eGuger

    2012-05-01

    Full Text Available Most brain-computer interfaces (BCI rely on one of three types of signals in the electroencephalogram (EEG: P300s, steady-state visually evoked potentials (SSVEP, and event-related desynchronization (ERD. EEG is typically recorded non-invasively with electrodes mounted on the human scalp using conductive electrode gel for optimal impedance and data quality. The use of electrode gel entails serious problems that are especially pronounced in real-world settings when experts are not available. Some recent work has introduced dry electrode systems that do not require gel, but often introduce new problems such as comfort and signal quality. The principal goal of this study was to assess a new dry electrode BCI system in a very common task: spelling with a P300 BCI. A total of 23 subjects used a P300 BCI to spell the word LUCAS while receiving realtime, closed-loop feedback. The dry system yielded classification accuracies that were similar to those obtained with gel systems. All subjects completed a questionnaire after data recording, and all subjects stated that the dry system was not uncomfortable. This is the first field validation of a dry electrode P300 BCI system, and paves the way for new research and development with EEG recording systems that are much more practical and convenient in field settings than conventional systems.

  12. Improved semisupervised adaptation for a small training dataset in the brain-computer interface.

    Science.gov (United States)

    Meng, Jianjun; Sheng, Xinjun; Zhang, Dingguo; Zhu, Xiangyang

    2014-07-01

    One problem in the development of brain-computer interface (BCI) systems is to minimize the amount of subject training on the premise of accurate classification. Hence, the challenge is how to train the BCI system effectively especially in the scenario with small amount of training data. In this paper, we introduce improved semisupervised adaptation based on common spatial pattern (CSP) features. The feature extraction and classification are performed jointly and iteratively. In the iteration step, training data are expanded by part of the testing data with labels which are predicted by a linear discriminant analysis classifier and/or a Bayesian linear discriminant analysis classifier in the previous iteration. Then CSP features are reextracted from the expanded training data, and the classifiers are retrained. Both self-training and cotraining paradigms are proposed for the improved semisupervised adaptation. Throughout the investigation on different number of initial training trials, we find that when a small number of training trials are used, e.g., a training session contains no more than 30 trials, similar classification performance to that of large training data items (40-50 trials) can be achieved. Effectiveness of the algorithms is verified by two competition datasets. Compared with several existing algorithms, the proposed semisupervised algorithms show improvements in classification accuracy for most of the competition datasets especially in the case of small training data. PMID:24122610

  13. Adaptation in P300 brain-computer interfaces: a two-classifier cotraining approach.

    Science.gov (United States)

    Panicker, Rajesh C; Puthusserypady, Sadasivan; Sun, Ying

    2010-12-01

    A cotraining-based approach is introduced for constructing high-performance classifiers for P300-based brain-computer interfaces (BCIs), which were trained from very little data. It uses two classifiers: Fisher's linear discriminant analysis and Bayesian linear discriminant analysis progressively teaching each other to build a final classifier, which is robust and able to learn effectively from unlabeled data. Detailed analysis of the performance is carried out through extensive cross-validations, and it is shown that the proposed approach is able to build high-performance classifiers from just a few minutes of labeled data and by making efficient use of unlabeled data. An average bit rate of more than 37 bits/min was achieved with just one and a half minutes of training, achieving an increase of about 17 bits/min compared to the fully supervised classification in one of the configurations. This performance improvement is shown to be even more significant in cases where the training data as well as the number of trials that are averaged for detection of a character is low, both of which are desired operational characteristics of a practical BCI system. Moreover, the proposed method outperforms the self-training-based approaches where the confident predictions of a classifier is used to retrain itself. PMID:20639171

  14. A cognitive brain-computer interface for patients with amyotrophic lateral sclerosis.

    Science.gov (United States)

    Hohmann, M R; Fomina, T; Jayaram, V; Widmann, N; Förster, C; Just, J; Synofzik, M; Schölkopf, B; Schöls, L; Grosse-Wentrup, M

    2016-01-01

    Brain-computer interfaces (BCIs) are often based on the control of sensorimotor processes, yet sensorimotor processes are impaired in patients suffering from amyotrophic lateral sclerosis (ALS). We devised a new paradigm that targets higher-level cognitive processes to transmit information from the user to the BCI. We instructed five ALS patients and twelve healthy subjects to either activate self-referential memories or to focus on a process without mnemonic content while recording a high-density electroencephalogram (EEG). Both tasks are designed to modulate activity in the default mode network (DMN) without involving sensorimotor pathways. We find that the two tasks can be distinguished after only one experimental session from the average of the combined bandpower modulations in the theta- (4-7Hz) and alpha-range (8-13Hz), with an average accuracy of 62.5% and 60.8% for healthy subjects and ALS patients, respectively. The spatial weights of the decoding algorithm show a preference for the parietal area, consistent with modulation of neural activity in primary nodes of the DMN. PMID:27590971

  15. Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems

    Directory of Open Access Journals (Sweden)

    Dongrui Gao

    2015-01-01

    Full Text Available Background. Usually the training set of online brain-computer interface (BCI experiment is small. For the small training set, it lacks enough information to deeply train the classifier, resulting in the poor classification performance during online testing. Methods. In this paper, on the basis of Z-LDA, we further calculate the classification probability of Z-LDA and then use it to select the reliable samples from the testing set to enlarge the training set, aiming to mine the additional information from testing set to adjust the biased classification boundary obtained from the small training set. The proposed approach is an extension of previous Z-LDA and is named enhanced Z-LDA (EZ-LDA. Results. We evaluated the classification performance of LDA, Z-LDA, and EZ-LDA on simulation and real BCI datasets with different sizes of training samples, and classification results showed EZ-LDA achieved the best classification performance. Conclusions. EZ-LDA is promising to deal with the small sample size training problem usually existing in online BCI system.

  16. Hybrid Brain-Computer Interface (BCI) based on the EEG and EOG signals.

    Science.gov (United States)

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

    2014-01-01

    Recently, the integration of different electrophysiological signals into an electroencephalogram (EEG) has become an effective approach to improve the practicality of brain-computer interface (BCI) systems, referred to as hybrid BCIs. In this paper, a hybrid BCI was designed by combining an EEG with electrocardiograph (EOG) signals and tested using a target selection experiment. Gaze direction from the EOG and the event-related (de)synchronization (ERD/ERS) induced by motor imagery from the EEG were simultaneously detected as the output of the BCI system. The target selection mechanism was based on the synthesis of the gaze direction and ERD activity. When an ERD activity was detected, the target corresponding to the gaze direction was selected; without ERD activity, no target was selected, even when a subjects gaze was directed at the target. With this mechanism, the operation of the BCI system is more flexible and voluntary. The accuracy and completion time of the target selection tasks during the online testing were 89.3% and 2.4 seconds, respectively. These results show the feasibility and practicality of this hybrid BCI system, which can potentially be used in the rehabilitation of disabled individuals. PMID:25226998

  17. An adaptive filter bank for motor imagery based Brain Computer Interface.

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    Thomas, Kavitha P; Guan, Cuntai; Tong, Lau Chiew; Prasad, Vinod A

    2008-01-01

    Brain Computer Interface (BCI) provides an alternative communication and control method for people with severe motor disabilities. Motor imagery patterns are widely used in Electroencephalogram (EEG) based BCIs. These motor imagery activities are associated with variation in alpha and beta band power of EEG signals called Event Related Desynchronization/synchronization (ERD/ERS). The dominant frequency bands are subject-specific and therefore performance of motor imagery based BCIs are sensitive to both temporal filtering and spatial filtering. As the optimum filter is strongly subject-dependent, we propose a method that selects the subject-specific discriminative frequency components using time-frequency plots of Fisher ratio of two-class motor imagery patterns. We also propose a low complexity adaptive Finite Impulse Response (FIR) filter bank system based on coefficient decimation technique which can realize the subject-specific bandpass filters adaptively depending on the information of Fisher ratio map. Features are extracted only from the selected frequency components. The proposed adaptive filter bank based system offers average classification accuracy of about 90%, which is slightly better than the existing fixed filter bank system. PMID:19162856

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Ju-Chi Liu

    2016-01-01

    Full Text Available 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.

  20. Subject combination and electrode selection in cooperative brain-computer interface based on event related potentials.

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    Cecotti, Hubert; Rivet, Bertrand

    2014-01-01

    New paradigms are required in Brain-Computer Interface (BCI) systems for the needs and expectations of healthy people. To solve this issue, we explore the emerging field of cooperative BCIs, which involves several users in a single BCI system. Contrary to classical BCIs that are dependent on the unique subject's will, cooperative BCIs are used for problem solving tasks where several people shall be engaged by sharing a common goal. Similarly as combining trials over time improves performance, combining trials across subjects can significantly improve performance compared with when only a single user is involved. Yet, cooperative BCIs may only be used in particular settings, and new paradigms must be proposed to efficiently use this approach. The possible benefits of using several subjects are addressed, and compared with current single-subject BCI paradigms. To show the advantages of a cooperative BCI, we evaluate the performance of combining decisions across subjects with data from an event-related potentials (ERP) based experiment where each subject observed the same sequence of visual stimuli. Furthermore, we show that it is possible to achieve a mean AUC superior to 0.95 with 10 subjects and 3 electrodes on each subject, or with 4 subjects and 6 electrodes on each subject. Several emerging challenges and possible applications are proposed to highlight how cooperative BCIs could be efficiently used with current technologies and leverage BCI applications. PMID:24961765

  1. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials

    Directory of Open Access Journals (Sweden)

    Hubert Cecotti

    2014-04-01

    Full Text Available New paradigms are required in Brain-Computer Interface (BCI systems for the needs and expectations of healthy people. To solve this issue, we explore the emerging field of cooperative BCIs, which involves several users in a single BCI system. Contrary to classical BCIs that are dependent on the unique subject’s will, cooperative BCIs are used for problem solving tasks where several people shall be engaged by sharing a common goal. Similarly as combining trials over time improves performance, combining trials across subjects can significantly improve performance compared with when only a single user is involved. Yet, cooperative BCIs may only be used in particular settings, and new paradigms must be proposed to efficiently use this approach. The possible benefits of using several subjects are addressed, and compared with current single-subject BCI paradigms. To show the advantages of a cooperative BCI, we evaluate the performance of combining decisions across subjects with data from an event-related potentials (ERP based experiment where each subject observed the same sequence of visual stimuli. Furthermore, we show that it is possible to achieve a mean AUC superior to 0.95 with 10 subjects and 3 electrodes on each subject, or with 4 subjects and 6 electrodes on each subject. Several emerging challenges and possible applications are proposed to highlight how cooperative BCIs could be efficiently used with current technologies and leverage BCI applications.

  2. Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task

    Science.gov (United States)

    Krumpe, Tanja; Walter, Carina; Rosenstiel, Wolfgang; Spüler, Martin

    2016-08-01

    Objective. In this study, the feasibility of detecting a P300 via an asynchronous classification mode in a reactive EEG-based brain-computer interface (BCI) was evaluated. The P300 is one of the most popular BCI control signals and therefore used in many applications, mostly for active communication purposes (e.g. P300 speller). As the majority of all systems work with a stimulus-locked mode of classification (synchronous), the field of applications is limited. A new approach needs to be applied in a setting in which a stimulus-locked classification cannot be used due to the fact that the presented stimuli cannot be controlled or predicted by the system. Approach. A continuous observation task requiring the detection of outliers was implemented to test such an approach. The study was divided into an offline and an online part. Main results. Both parts of the study revealed that an asynchronous detection of the P300 can successfully be used to detect single events with high specificity. It also revealed that no significant difference in performance was found between the synchronous and the asynchronous approach. Significance. The results encourage the use of an asynchronous classification approach in suitable applications without a potential loss in performance.

  3. Self-calibration algorithm in an asynchronous P300-based brain-computer interface

    Science.gov (United States)

    Schettini, F.; Aloise, F.; Aricò, P.; Salinari, S.; Mattia, D.; Cincotti, F.

    2014-06-01

    Objective. Reliability is a desirable characteristic of brain-computer interface (BCI) systems when they are intended to be used under non-experimental operating conditions. In addition, their overall usability is influenced by the complex and frequent procedures that are required for configuration and calibration. Earlier studies examined the issue of asynchronous control in P300-based BCIs, introducing dynamic stopping and automatic control suspension features. This report proposes and evaluates an algorithm for the automatic recalibration of the classifier's parameters using unsupervised data. Approach. Ten healthy subjects participated in five P300-based BCI sessions throughout a single day. First, we examined whether continuous adaptation of control parameters improved the accuracy of the asynchronous system over time. Then, we assessed the performance of the self-calibration algorithm with respect to the no-recalibration and supervised calibration conditions with regard to system accuracy and communication efficiency. Main results. Offline tests demonstrated that continuous adaptation of the control parameters significantly increased the communication efficiency of asynchronous P300-based BCIs. The self-calibration algorithm correctly assigned labels to unsupervised data with 95% accuracy, effecting communication efficiency that was comparable with that of supervised repeated calibration. Significance. Although additional online tests that involve end-users under non-experimental conditions are needed, these preliminary results are encouraging, from which we conclude that the self-calibration algorithm is a promising solution to improve P300-based BCI usability and reliability.

  4. Prediction of auditory and visual p300 brain-computer interface aptitude.

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    Sebastian Halder

    Full Text Available OBJECTIVE: Brain-computer interfaces (BCIs provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball. METHODS: Forty healthy participants performed an electroencephalography (EEG based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude. RESULTS: Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy. CONCLUSIONS: Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection. SIGNIFICANCE: Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.

  5. Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information

    Science.gov (United States)

    Yuan, Peng; Chen, Xiaogang; Wang, Yijun; Gao, Xiaorong; Gao, Shangkai

    2015-08-01

    Objective. A new training-free framework was proposed for target detection in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) using joint frequency-phase coding. Approach. The key idea is to transfer SSVEP templates from the existing subjects to a new subject to enhance the detection of SSVEPs. Under this framework, transfer template-based canonical correlation analysis (tt-CCA) methods were developed for single-channel and multi-channel conditions respectively. In addition, an online transfer template-based CCA (ott-CCA) method was proposed to update EEG templates by online adaptation. Main results. The efficiency of the proposed framework was proved with a simulated BCI experiment. Compared with the standard CCA method, tt-CCA obtained an 18.78% increase of accuracy with a data length of 1.5 s. A simulated test of ott-CCA further received an accuracy increase of 2.99%. Significance. The proposed simple yet efficient framework significantly facilitates the use of SSVEP BCIs using joint frequency-phase coding. This study also sheds light on the benefits from exploring and exploiting inter-subject information to the electroencephalogram (EEG)-based BCIs.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  8. A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System

    Science.gov (United States)

    Resalat, Seyed Navid; Saba, Valiallah

    2016-01-01

    Introduction: Brain Computer Interface (BCI) systems based on Movement Imagination (MI) are widely used in recent decades. Separate feature extraction methods are employed in the MI data sets and classified in Virtual Reality (VR) environments for real-time applications. Methods: This study applied wide variety of features on the recorded data using Linear Discriminant Analysis (LDA) classifier to select the best feature sets in the offline mode. The data set was recorded in 3-class tasks of the left hand, the right hand, and the foot motor imagery. Results: The experimental results showed that Auto-Regressive (AR), Mean Absolute Value (MAV), and Band Power (BP) features have higher accuracy values,75% more than those for the other features. Discussion: These features were selected for the designed real-time navigation. The corresponding results revealed the subject-specific nature of the MI-based BCI system; however, the Power Spectral Density (PSD) based α-BP feature had the highest averaged accuracy.

  9. A Step towards EEG-based brain computer interface for autism intervention.

    Science.gov (United States)

    Fan, Jing; Wade, Joshua W; Bian, Dayi; Key, Alexandra P; Warren, Zachary E; Mion, Lorraine C; Sarkar, Nilanjan

    2015-08-01

    Autism Spectrum Disorder (ASD) is a prevalent and costly neurodevelopmental disorder. Individuals with ASD often have deficits in social communication skills as well as adaptive behavior skills related to daily activities. We have recently designed a novel virtual reality (VR) based driving simulator for driving skill training for individuals with ASD. In this paper, we explored the feasibility of detecting engagement level, emotional states, and mental workload during VR-based driving using EEG as a first step towards a potential EEG-based Brain Computer Interface (BCI) for assisting autism intervention. We used spectral features of EEG signals from a 14-channel EEG neuroheadset, together with therapist ratings of behavioral engagement, enjoyment, frustration, boredom, and difficulty to train a group of classification models. Seven classification methods were applied and compared including Bayes network, naïve Bayes, Support Vector Machine (SVM), multilayer perceptron, K-nearest neighbors (KNN), random forest, and J48. The classification results were promising, with over 80% accuracy in classifying engagement and mental workload, and over 75% accuracy in classifying emotional states. Such results may lead to an adaptive closed-loop VR-based skill training system for use in autism intervention. PMID:26737113

  10. Goal selection versus process control while learning to use a brain-computer interface

    Science.gov (United States)

    Royer, Audrey S.; Rose, Minn L.; He, Bin

    2011-06-01

    A brain-computer interface (BCI) can be used to accomplish a task without requiring motor output. Two major control strategies used by BCIs during task completion are process control and goal selection. In process control, the user exerts continuous control and independently executes the given task. In goal selection, the user communicates their goal to the BCI and then receives assistance executing the task. A previous study has shown that goal selection is more accurate and faster in use. An unanswered question is, which control strategy is easier to learn? This study directly compares goal selection and process control while learning to use a sensorimotor rhythm-based BCI. Twenty young healthy human subjects were randomly assigned either to a goal selection or a process control-based paradigm for eight sessions. At the end of the study, the best user from each paradigm completed two additional sessions using all paradigms randomly mixed. The results of this study were that goal selection required a shorter training period for increased speed, accuracy, and information transfer over process control. These results held for the best subjects as well as in the general subject population. The demonstrated characteristics of goal selection make it a promising option to increase the utility of BCIs intended for both disabled and able-bodied users.

  11. A brain-computer interface for long-term independent home use.

    Science.gov (United States)

    Sellers, Eric W; Vaughan, Theresa M; Wolpaw, Jonathan R

    2010-10-01

    Our objective was to develop and validate a new brain-computer interface (BCI) system suitable for long-term independent home use by people with severe motor disabilities. The BCI was used by a 51-year-old male with ALS who could no longer use conventional assistive devices. Caregivers learned to place the electrode cap, add electrode gel, and turn on the BCI. After calibration, the system allowed the user to communicate via EEG. Re-calibration was performed remotely (via the internet), and BCI accuracy assessed in periodic tests. Reports of BCI usefulness by the user and the family were also recorded. Results showed that BCI accuracy remained at 83% (r = -.07, n.s.) for over 2.5 years (1.4% expected by chance). The BCI user and his family state that the BCI had restored his independence in social interactions and at work. He uses the BCI to run his NIH-funded research laboratory and to communicate via e-mail with family, friends, and colleagues. In addition to this first user, several other similarly disabled people are now using the BCI in their daily lives. In conclusion, long-term independent home use of this BCI system is practical for severely disabled people, and can contribute significantly to quality of life and productivity. PMID:20583947

  12. Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces

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    Lal Thomas Navin

    2005-01-01

    Full Text Available Most EEG-based brain-computer interface (BCI paradigms come along with specific electrode positions, for example, for a visual-based BCI, electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects, Lal et al. in 2004 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extent their method of recursive channel elimination (RCE can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view were consistently selected whereas task-irrelevant channels were reliably disregarded.

  13. A submatrix-based P300 brain-computer interface stimulus presentation paradigm

    Institute of Scientific and Technical Information of China (English)

    Jin-he SHI; Ji-zhong SHEN; Yu JI; Feng-lei DU

    2012-01-01

    The P300 event-related potential (ERP),with advantages of high stability and no need for initial training,is one of the most commonly used responses in brain-computer interface (BCI) applications.The row/column paradigm (RCP) that flashes an entire colunm or row of a visual matrix has been used successfully to help patients to spell words.However,RCP remains subject to errors that slow down communication,such as adjacency-distraction and double-flash errors.In this paper,a new visual stimulus presentation paradigm called the submatrix-based paradigm (SBP) is proposed.SBP divides a 6×6 matrix into several submatrices.Each submatrix flashes in single cell paradigm (SCP) mode and separately performs an ensemble averaging method according to the sequences.The parameter of sequence number is used to improve further the accuracy and information transfer rate (ITR).SBP has advantages of flexibility in division of the matrix and better expansion capability,which were confirmed with different divisions of the 6×6 matrix and expansion to a 6x9 matrix.Stimulation results show that SBP is superior to RCP in performance and user acceptability.

  14. Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks.

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    Alessio Paolo Buccino

    Full Text Available Non-invasive Brain-Computer Interfaces (BCI have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG and functional Near-Infrared Spectroscopy (fNIRS in an asynchronous Sensory Motor rhythm (SMR-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm-Left-Arm-Right-Hand-Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes.

  15. A brain-computer interface based attention training program for treating attention deficit hyperactivity disorder.

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    Choon Guan Lim

    Full Text Available UNLABELLED: Attention deficit hyperactivity disorder (ADHD symptoms can be difficult to treat. We previously reported that a 20-session brain-computer interface (BCI attention training programme improved ADHD symptoms. Here, we investigated a new more intensive BCI-based attention training game system on 20 unmedicated ADHD children (16 males, 4 females with significant inattentive symptoms (combined and inattentive ADHD subtypes. This new system monitored attention through a head band with dry EEG sensors, which was used to drive a feed forward game. The system was calibrated for each user by measuring the EEG parameters during a Stroop task. Treatment consisted of an 8-week training comprising 24 sessions followed by 3 once-monthly booster training sessions. Following intervention, both parent-rated inattentive and hyperactive-impulsive symptoms on the ADHD Rating Scale showed significant improvement. At week 8, the mean improvement was -4.6 (5.9 and -4.7 (5.6 respectively for inattentive symptoms and hyperactive-impulsive symptoms (both p<0.01. Cohen's d effect size for inattentive symptoms was large at 0.78 at week 8 and 0.84 at week 24 (post-boosters. Further analysis showed that the change in the EEG based BCI ADHD severity measure correlated with the change ADHD Rating Scale scores. The BCI-based attention training game system is a potential new treatment for ADHD. TRIAL REGISTRATION: ClinicalTrials.gov NCT01344044.

  16. A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Francesco Cavrini

    2016-01-01

    Full Text Available We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control.

  17. Research of brain computer interface technology%脑-机接口技术研究

    Institute of Scientific and Technical Information of China (English)

    洪杰; 秦现生; 谭小群; 王文杰; 牛军龙

    2014-01-01

    脑-机接口是在人脑与计算机或其他电子设备之间建立直接的交流和控制的通道,它直接通过脑来表达想法,而不需要语言和动作,为思维正常但有严重运动障碍的患者提供了与外界交流和控制的途径,提高了他们的生活质量.本文对脑-机接口(brain computer interface,BCI)技术、研究方法、分类以及涉及的关键技术、应用领域等进行了较为详细的综述,并在此基础上分析目前BCI存在的问题,最后指出该领域的发展趋势.

  18. Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury

    Directory of Open Access Journals (Sweden)

    Jennifer L. Collinger, PhD

    2013-04-01

    Full Text Available Spinal cord injury (SCI often affects a person’s ability to perform critical activities of daily living and can negatively affect his or her quality of life. Assistive technology aims to bridge this gap in order to augment function and increase independence. It is critical to involve consumers in the design and evaluation process as new technologies such as brain-­computer interfaces (BCIs are developed. In a survey study of 57 veterans with SCI participating in the 2010 National Veterans Wheelchair Games, we found that restoration of bladder and bowel control, walking, and arm and hand function (tetraplegia only were all high priorities for improving quality of life. Many of the participants had not used or heard of some currently available technologies designed to improve function or the ability to interact with their environment. The majority of participants in this study were interested in using a BCI, particularly for controlling functional electrical stimulation to restore lost function. Independent operation was considered to be the most important design criteria. Interestingly, many participants reported that they would consider surgery to implant a BCI even though noninvasiveness was a high-priority design requirement. This survey demonstrates the interest of individuals with SCI in receiving and contributing to the design of BCIs.

  19. Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

    Science.gov (United States)

    Ceballos, G. A.; Hernández, L. F.

    2015-04-01

    Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

  20. Performance predictors of brain-computer interfaces in patients with amyotrophic lateral sclerosis

    Science.gov (United States)

    Geronimo, A.; Simmons, Z.; Schiff, S. J.

    2016-04-01

    Objective. Patients with amyotrophic lateral sclerosis (ALS) may benefit from brain-computer interfaces (BCI), but the utility of such devices likely will have to account for the functional, cognitive, and behavioral heterogeneity of this neurodegenerative disorder. Approach. In this study, a heterogeneous group of patients with ALS participated in a study on BCI based on the P300 event related potential and motor-imagery. Results. The presence of cognitive impairment in these patients significantly reduced the quality of the control signals required to use these communication systems, subsequently impairing performance, regardless of progression of physical symptoms. Loss in performance among the cognitively impaired was accompanied by a decrease in the signal-to-noise ratio of task-relevant EEG band power. There was also evidence that behavioral dysfunction negatively affects P300 speller performance. Finally, older participants achieved better performance on the P300 system than the motor-imagery system, indicating a preference of BCI paradigm with age. Significance. These findings highlight the importance of considering the heterogeneity of disease when designing BCI augmentative and alternative communication devices for clinical applications.

  1. Leveraging anatomical information to improve transfer learning in brain-computer interfaces

    Science.gov (United States)

    Wronkiewicz, Mark; Larson, Eric; Lee, Adrian K. C.

    2015-08-01

    Objective. Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data. Approach. We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions. Main results. Based on simulated and measured electroencephalography activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest. Significance. These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.

  2. EDITORIAL: Special section on gaze-independent brain-computer interfaces Special section on gaze-independent brain-computer interfaces

    Science.gov (United States)

    Treder, Matthias S.

    2012-08-01

    Restoring the ability to communicate and interact with the environment in patients with severe motor disabilities is a vision that has been the main catalyst of early brain-computer interface (BCI) research. The past decade has brought a diversification of the field. BCIs have been examined as a tool for motor rehabilitation and their benefit in non-medical applications such as mental-state monitoring for improved human-computer interaction and gaming has been confirmed. At the same time, the weaknesses of some approaches have been pointed out. One of these weaknesses is gaze-dependence, that is, the requirement that the user of a BCI system voluntarily directs his or her eye gaze towards a visual target in order to efficiently operate a BCI. This not only contradicts the main doctrine of BCI research, namely that BCIs should be independent of muscle activity, but it can also limit its real-world applicability both in clinical and non-medical settings. It is only in a scenario devoid of any motor activity that a BCI solution is without alternative. Gaze-dependencies have surfaced at two different points in the BCI loop. Firstly, a BCI that relies on visual stimulation may require users to fixate on the target location. Secondly, feedback is often presented visually, which implies that the user may have to move his or her eyes in order to perceive the feedback. This special section was borne out of a BCI workshop on gaze-independent BCIs held at the 2011 Society for Applied Neurosciences (SAN) Conference and has then been extended with additional contributions from other research groups. It compiles experimental and methodological work that aims toward gaze-independent communication and mental-state monitoring. Riccio et al review the current state-of-the-art in research on gaze-independent BCIs [1]. Van der Waal et al present a tactile speller that builds on the stimulation of the fingers of the right and left hand [2]. H¨ohne et al analyze the ergonomic aspects

  3. 脑控:基于脑-机接口的人机融合控制%Brain Control: Human-computer Integration Control Based on Brain-computer Interface

    Institute of Scientific and Technical Information of China (English)

    王行愚; 金晶; 张宇; 王蓓

    2013-01-01

    近年来,一类被称之为脑控的新型控制系统发展迅速,这是一种基于脑-机接口(Brain-computer interface,BCI)的人机融合控制系统,也是一种基于人的意念和思维的控制系统.脑控系统己被成功应用于残疾人的生活辅助、中风病人和损伤肢体的康复训练、操作员状态的实时监控、游戏娱乐和智能家居等广泛的领域.本文在简要介绍了脑控的研究背景、基本原理、系统结构和发展概况的基础上,着重对脑电信号(Electroencephalogram,EEG)模式、控制信号转换算法和应用系统研究等主要问题的研究现状,进行了较为详细的论述和分析,并探讨了进一步研究的方向和思路.最后对脑控的未来发展方向和应用前景进行了分析和展望.%Recently, a new system called brain control system has been developed rapidly. Brain control system is a human-computer integration control system based on brain-computer interface (BCI), which relies on human's ideas and thinking. Brain control system has been successfully applied in wide fields, assisting disabled patients daily life, training patients with stroke or limb injury, monitoring the status of human operator, as well as entertainment and smart house etc. In this paper, the background, basic principle, system structure and developments are firstly introduced briefly. The current research status focusing on the problems of electroencephalogram (EEG) signal pattern, control signal transfer algorithm and system application is summarized and analyzed in detail. The further research direction and thoughts are discussed. Finally, the future development of brain control is analyzed and prospects are given.

  4. 뇌-컴퓨터 인터페이스 (Brain-Computer Interfaces) 기술에 대한 국내·외 연구개발 동향 조사 (Research and Development in Brain-Computer Interfacing Technology: A Comprehensive Technical Review). Final Report.

    OpenAIRE

    Nam, Chang Soo; Kim, Sung-Phil; Krusienkki, Dean; Nijholt, Anton

    2015-01-01

    This report commisioned by the Korean American Scientists and Engineers Association (KSEA) and written with the support of the Korea Federation of Science and Technology Societies (KOFST) surveys research and development trends in the area of brain-computer interface (Brain-Computer Interfaces, BCI) technology. The survey was done by taking expert interviews and through conducting a literature review in the period from September until December 2015. Brain-computer interface is a generic name ...

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

    Science.gov (United States)

    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. PMID:25388661

  6. Navigation with a passive brain based interface

    NARCIS (Netherlands)

    Erp, J.B.F. van; Werkhoven, P.J.; Thurlings, M.E.; Brouwer, A.-M.

    2009-01-01

    In this paper, we describe a Brain Computer Interface (BCI) for navigation. The system is based on detecting brain signals that are elicited by tactile stimulation on the torso indicating the desired direction.

  7. An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Yijun Wang

    2007-07-01

    Full Text Available For a robust brain-computer interface (BCI system based on motor imagery (MI, it should be able to tell when the subject is not concentrating on MI tasks (the “idle state” so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this problem. A three-class classifier was constructed by combining two two-class classifiers, one specified for idle-state detection and the other for these two MI tasks. Common spatial subspace decomposition (CSSD was used to extract the features of event-related desynchronization (ERD in two motor imagery tasks. Then Fisher discriminant analysis (FDA was employed in the design of two two-class classifiers for completion of detecting each task, respectively. The algorithm successfully provided a way to solve the problem of “idle-state detection without training samples.” The algorithm was applied to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was obtained on the testing set. This is the winning algorithm in BCI competition III. In addition, the algorithm was also validated by applying to the EEG data of an MI experiment including “idle” task.

  8. An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli

    Science.gov (United States)

    Hill, N. J.; Schölkopf, B.

    2012-04-01

    We report on the development and online testing of an electroencephalogram-based brain-computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare ‘oddball’ stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.

  9. A dry EEG-system for scientific research and brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Thorsten Oliver Zander

    2011-05-01

    Full Text Available Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG still forms the method of choice in a wide variety of clinical and research applications. In the context of Brain-Computer Interfacing (BCI, EEG recently has become a tool to enhance Human-Machine Interaction (HMI. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, Event-Related Potentials (ERP were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.

  10. Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention

    Directory of Open Access Journals (Sweden)

    Schmidt Nico M

    2011-05-01

    Full Text Available Abstract Background Visual brain-computer interfaces (BCIs often yield high performance only when targets are fixated with the eyes. Furthermore, many paradigms use intense visual stimulation, which can be irritating especially in long BCI sessions. However, BCIs can more directly directly tap the neural processes underlying visual attention. Covert shifts of visual attention induce changes in oscillatory alpha activity in posterior cortex, even in the absence of visual stimulation. The aim was to investigate whether different pairs of directions of attention shifts can be reliably differentiated based on the electroencephalogram. To this end, healthy participants (N = 8 had to strictly fixate a central dot and covertly shift visual attention to one out of six cued directions. Results Covert attention shifts induced a prolonged alpha synchronization over posterior electrode sites (PO and O electrodes. Spectral changes had specific topographies so that different pairs of directions could be differentiated. There was substantial variation across participants with respect to the direction pairs that could be reliably classified. Mean accuracy for the best-classifiable pair amounted to 74.6%. Furthermore, an alpha power index obtained during a relaxation measurement showed to be predictive of peak BCI performance (r = .66. Conclusions Results confirm posterior alpha power modulations as a viable input modality for gaze-independent EEG-based BCIs. The pair of directions yielding optimal performance varies across participants. Consequently, participants with low control for standard directions such as left-right might resort to other pairs of directions including top and bottom. Additionally, a simple alpha index was shown to predict prospective BCI performance.

  11. Multiple frequencies sequential coding for SSVEP-based brain-computer interface.

    Directory of Open Access Journals (Sweden)

    Yangsong Zhang

    Full Text Available BACKGROUND: Steady-state visual evoked potential (SSVEP-based brain-computer interface (BCI has become one of the most promising modalities for a practical noninvasive BCI system. Owing to both the limitation of refresh rate of liquid crystal display (LCD or cathode ray tube (CRT monitor, and the specific physiological response property that only a very small number of stimuli at certain frequencies could evoke strong SSVEPs, the available frequencies for SSVEP stimuli are limited. Therefore, it may not be enough to code multiple targets with the traditional frequencies coding protocols, which poses a big challenge for the design of a practical SSVEP-based BCI. This study aimed to provide an innovative coding method to tackle this problem. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we present a novel protocol termed multiple frequencies sequential coding (MFSC for SSVEP-based BCI. In MFSC, multiple frequencies are sequentially used in each cycle to code the targets. To fulfill the sequential coding, each cycle is divided into several coding epochs, and during each epoch, certain frequency is used. Obviously, different frequencies or the same frequency can be presented in the coding epochs, and the different epoch sequence corresponds to the different targets. To show the feasibility of MFSC, we used two frequencies to realize four targets and carried on an offline experiment. The current study shows that: 1 MFSC is feasible and efficient; 2 the performance of SSVEP-based BCI based on MFSC can be comparable to some existed systems. CONCLUSIONS/SIGNIFICANCE: The proposed protocol could potentially implement much more targets with the limited available frequencies compared with the traditional frequencies coding protocol. The efficiency of the new protocol was confirmed by real data experiment. We propose that the SSVEP-based BCI under MFSC might be a promising choice in the future.

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

    Science.gov (United States)

    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

  13. Spatial decoupling of targets and flashing stimuli for visual brain-computer interfaces

    Science.gov (United States)

    Waytowich, Nicholas R.; Krusienski, Dean J.

    2015-06-01

    Objective. Recently, paradigms using code-modulated visual evoked potentials (c-VEPs) have proven to achieve among the highest information transfer rates for noninvasive brain-computer interfaces (BCIs). One issue with current c-VEP paradigms, and visual-evoked paradigms in general, is that they require direct foveal fixation of the flashing stimuli. These interfaces are often visually unpleasant and can be irritating and fatiguing to the user, thus adversely impacting practical performance. In this study, a novel c-VEP BCI paradigm is presented that attempts to perform spatial decoupling of the targets and flashing stimuli using two distinct concepts: spatial separation and boundary positioning. Approach. For the paradigm, the flashing stimuli form a ring that encompasses the intended non-flashing targets, which are spatially separated from the stimuli. The user fixates on the desired target, which is classified using the changes to the EEG induced by the flashing stimuli located in the non-foveal visual field. Additionally, a subset of targets is also positioned at or near the stimulus boundaries, which decouples targets from direct association with a single stimulus. This allows a greater number of target locations for a fixed number of flashing stimuli. Main results. Results from 11 subjects showed practical classification accuracies for the non-foveal condition, with comparable performance to the direct-foveal condition for longer observation lengths. Online results from 5 subjects confirmed the offline results with an average accuracy across subjects of 95.6% for a 4-target condition. The offline analysis also indicated that targets positioned at or near the boundaries of two stimuli could be classified with the same accuracy as traditional superimposed (non-boundary) targets. Significance. The implications of this research are that c-VEPs can be detected and accurately classified to achieve comparable BCI performance without requiring potentially irritating

  14. A comparison of recording modalities of P300 event-related potentials (ERP) for brain-computer interface (BCI) paradigm

    OpenAIRE

    Mayaud, Louis; Congedo, Marco; Van Laghenhove, Aurélien; Figère, Marjorie; Azabou, Eric; Cheliout-Heraut, F.

    2013-01-01

    Aims of the study A brain-Computer Interface aims at restoring communication and control in severely disabled people by identification and classification of EEG features such as Event Related Potentials (ERPs). The aim of this study is to compare different EEG recording modalities for extraction of ERPs. The first comparison evaluates the performance of six disc electrodes with that of the Emotiv headset, while the second evaluates three different electrode types (disc, needle, and large squa...

  15. A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users

    OpenAIRE

    Minkyu Ahn; Mijin Lee; Jinyoung Choi; Sung Chan Jun

    2014-01-01

    In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have bee...

  16. Integrated Real-Time Control And Processing Systems For Multi-Channel Near-Infrared Spectroscopy Based Brain Computer Interfaces

    OpenAIRE

    Matthews, Fiachra

    2010-01-01

    This thesis outlines approaches to improve the signal processing and anal- ysis of Near-infrared spectroscopy (NIRS) based brain-computer interfaces (BCI). These approaches were developed in conjunction with the implemen- tation of a new customized exible multi-channel NIRS based BCI hardware system (Soraghan, 2010). Using a comparable functional imaging modality the assumptions on which NIRS-BCI have been reassessed, with regard to cognitive task selection, active area ...

  17. Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface

    OpenAIRE

    Acqualagna, Laura; Botrel, Loic; Vidaurre, Carmen; Kübler, Andrea; Blankertz, Benjamin

    2016-01-01

    In the last years Brain Computer Interface (BCI) technology has benefited from the development of sophisticated machine leaning methods that let the user operate the BCI after a few trials of calibration. One remarkable example is the recent development of co-adaptive techniques that proved to extend the use of BCIs also to people not able to achieve successful control with the standard BCI procedure. Especially for BCIs based on the modulation of the Sensorimotor Rhythm (SMR) these improveme...

  18. Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications

    OpenAIRE

    Mrinal Pahwa; Matthew Kusner; Hacker, Carl D.; Bundy, David T.; Weinberger, Kilian Q.; Leuthardt, Eric C.

    2015-01-01

    Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoG-based BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging...

  19. Comparison of an open-hardware electroencephalography amplifier with medical grade device in brain-computer interface applications

    OpenAIRE

    Frey, Jérémy

    2016-01-01

    International audience Brain-computer interfaces (BCI) are promising communication devices between humans and machines. BCI based on non-invasive neuroimaging techniques such as electroencephalography (EEG) have many applications , however the dissemination of the technology is limited, in part because of the price of the hardware. In this paper we compare side by side two EEG amplifiers, the consumer grade OpenBCI and the medical grade g.tec g.USBamp. For this purpose, we employed an orig...

  20. EEG Mouse:A Machine Learning-Based Brain Computer Interface

    OpenAIRE

    Alomari, Mohammad H.; Ayman AbuBaker; Aiman Turani; Ali M. Baniyounes; Adnan Manasreh

    2014-01-01

    The main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine l...

  1. A novel mu rhythm-based brain computer interface design that uses a programmable system on chip

    OpenAIRE

    Joshi, R 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...

  2. Sources of EEG activity most relevant to performance of brain-computer interface based on motor imagery

    Czech Academy of Sciences Publication Activity Database

    Frolov, A.; Húsek, Dušan; Bobrov, P.; Korshakov, A.V.; Chernikova, L.; Konovalov, R.; Mokienko, O.

    2012-01-01

    Roč. 22, č. 1 (2012), s. 21-37. ISSN 1210-0552 R&D Projects: GA ČR GAP202/10/0262 Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070 Institutional research plan: CEZ:AV0Z10300504 Keywords : brain-computer interface * independent component analysis * pattern classification * motor imagery * inverse problem * fMRI * EEG Subject RIV: IN - Informatics, Computer Science Impact factor: 0.362, year: 2012

  3. The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.

    Science.gov (United States)

    Blankertz, Benjamin; Dornhege, Guido; Krauledat, Matthias; Müller, Klaus-Robert; Curio, Gabriel

    2007-08-15

    Brain-Computer Interface (BCI) systems establish a direct communication channel from the brain to an output device. These systems use brain signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications. BCI systems that bypass conventional motor output pathways of nerves and muscles can provide novel control options for paralyzed patients. One classical approach to establish EEG-based control is to set up a system that is controlled by a specific EEG feature which is known to be susceptible to conditioning and to let the subjects learn the voluntary control of that feature. In contrast, the Berlin Brain-Computer Interface (BBCI) uses well established motor competencies of its users and a machine learning approach to extract subject-specific patterns from high-dimensional features optimized for detecting the user's intent. Thus the long subject training is replaced by a short calibration measurement (20 min) and machine learning (1 min). We report results from a study in which 10 subjects, who had no or little experience with BCI feedback, controlled computer applications by voluntary imagination of limb movements: these intentions led to modulations of spontaneous brain activity specifically, somatotopically matched sensorimotor 7-30 Hz rhythms were diminished over pericentral cortices. The peak information transfer rate was above 35 bits per minute (bpm) for 3 subjects, above 23 bpm for two, and above 12 bpm for 3 subjects, while one subject could achieve no BCI control. Compared to other BCI systems which need longer subject training to achieve comparable results, we propose that the key to quick efficiency in the BBCI system is its flexibility due to complex but physiologically meaningful features and its adaptivity which respects the enormous inter-subject variability. PMID:17475513

  4. 뇌-컴퓨터 인터페이스 (Brain-Computer Interfaces) 기술에 대한 국내·외 연구개발 동향 조사 (Research and Development in Brain-Computer Interfacing Technology: A Comprehensive Technical Review). Final Report.

    NARCIS (Netherlands)

    Nam, Chang Soo; Kim, Sung-Phil; Krusienkki, Dean; Nijholt, Anton

    2015-01-01

    This report commisioned by the Korean American Scientists and Engineers Association (KSEA) and written with the support of the Korea Federation of Science and Technology Societies (KOFST) surveys research and development trends in the area of brain-computer interface (Brain-Computer Interfaces, BCI)

  5. Implementation of active electrodes on a brain-computer interface and its application as P300 speller

    International Nuclear Information System (INIS)

    A brain computer interface has implemented using open hardware called Modular EEG, created by The OpenEEG Project and distributed by the company Olimex Ltd. That hardware is modified to use active electrodes, instead of passive electrodes, for acquiring electroencephalographic signals. The application has been given to the interface has been a speller P300; for which has used the BC12000 open software that has the necessary configuration for the application. P300 speller has used a protocol in each session so that could be standardize the method to different users. Valuing the results with three neuropsychological tests, was within the objectives; however, has not been achieved by the limitation in time of project implementation. A brain computer interface has been used with passive electrodes; implemented in the same way that the BCI with active electrodes; and has worked better than the interface with active electrodes. One of the major advantages that has been observed of passive electrodes on the actives has been the size of the same, because the liabilities are smaller and therefore, easier to place preventing the hair of the user, which increases the noise in the signal. (author)

  6. An optimized ERP brain-computer interface based on facial expression changes

    Science.gov (United States)

    Jin, Jing; Daly, Ian; Zhang, Yu; Wang, Xingyu; Cichocki, Andrzej

    2014-06-01

    Objective. Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern. Approach. Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures. Main results. The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be

  7. EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces

    Science.gov (United States)

    Ashari, Rehab Bahaaddin

    Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only

  8. Rapid P300 brain-computer interface communication with a head-mounted display

    Directory of Open Access Journals (Sweden)

    Ivo eKäthner

    2015-06-01

    Full Text Available Visual ERP (P300 based brain-computer interfaces (BCIs allow for fast and reliable spelling and are intended as a muscle-independent communication channel for people with severe paralysis. However, they require the presentation of visual stimuli in the field of view of the user. A head mounted display could allow convenient presentation of visual stimuli in situations, where mounting a conventional monitor might be difficult or not feasible (e.g. at a patient’s bedside. To explore if similar accuracies can be achieved with a virtual reality (VR headset compared to a conventional flat screen monitor, we conducted an experiment with 18 healthy participants. We also evaluated it with a person in the locked-in state (LIS to verify that usage of the headset is possible for a severely paralyzed person. Healthy participants performed online spelling with three different display methods. In one condition a 5x5 letter matrix was presented on a conventional 22 inch TFT monitor. Two configurations of the VR headset were tested. In the first (glasses A, the same 5x5 matrix filled the field of view of the user. In the second (glasses B, single letters of the matrix filled the field of view of the user. The participant in the LIS tested the VR headset on 3 different occasions (glasses A condition only. For healthy participants, average online spelling accuracies were 94% (15.5 bits/min using three flash sequences for spelling with the monitor and glasses A and 96% (16.2 bits/min with glasses B. In one session, the participant in the LIS reached an online spelling accuracy of 100% (10 bits/min using the glasses A condition. We also demonstrated that spelling with one flash sequence is possible with the VR headset for healthy users (mean: 32.1 bits/min, maximum reached by one user: 71.89 bits/min at 100% accuracy. We conclude that the VR headset allows for rapid P300 BCI communication in healthy users and may be a suitable display option for severely

  9. Automatic artefact removal in a self-paced hybrid brain- computer interface system

    Directory of Open Access Journals (Sweden)

    Yong Xinyi

    2012-07-01

    Full Text Available Abstract Background A novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard. To select a letter, the user must gaze at the target for at least a specific period of time (dwell time and then activate the BCI by performing a mental task. Unfortunately, electroencephalogram (EEG signals are often contaminated with artefacts. Artefacts change the quality of EEG signals and subsequently degrade the BCI’s performance. Methods To remove artefacts in EEG signals, the proposed algorithm uses the stationary wavelet transform combined with a new adaptive thresholding mechanism. To evaluate the performance of the proposed algorithm and other artefact handling/removal methods, semi-simulated EEG signals (i.e., real EEG signals mixed with simulated artefacts and real EEG signals obtained from seven participants are used. For real EEG signals, the hybrid BCI system’s performance is evaluated in an online-like manner, i.e., using the continuous data from the last session as in a real-time environment. Results With semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains. With real EEG signals, we demonstrate that for dwell time of 0.0s, the number of false-positives/minute is 2 and the true positive rate (TPR achieved by the proposed algorithm is 44.7%, which is more than 15.0% higher compared to other state-of-the-art artefact handling methods. As dwell time increases to 1.0s, the TPR increases to 73.1%. Conclusions The proposed artefact removal algorithm greatly improves the BCI’s performance. It also has the following advantages: a it does not require additional electrooculogram/electromyogram channels, long data segments or a large number of EEG channels, b it allows real-time processing, and c it reduces signal distortion.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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

  12. On the need to better specify the concept of control in brain-computer-interfaces/neurofeedback research

    Directory of Open Access Journals (Sweden)

    Guilherme eWood

    2014-09-01

    Full Text Available Aiming at a better specification of the concept of control in brain-computer-interfaces (BCI and neurofeedback research, we propose to distinguish self-control of brain activity from the broader concept of BCI control, since the first describes a neurocognitive phenomenon and is only one of the many components of BCI control. Based on this distinction, we developed a framework based on dual-processes theory that describes the cognitive determinants of self-control of brain activity as the interplay of automatic vs. controlled information processing. Further, we distinguish between cognitive processes that are necessary and sufficient to achieve a given level of self-control of brain activity and those which are not. We discuss that those cognitive processes which are not necessary for the learning process can hamper self-control because they cannot be completely turned-off at any time. This framework aims at a comprehensive description of the cognitive determinants of the acquisition of self-control of brain activity underlying those classes of BCI which require the user to achieve regulation of brain activity as well as neurofeedback learning.

  13. EEG Mouse:A Machine Learning-Based Brain Computer Interface

    Directory of Open Access Journals (Sweden)

    Mohammad H. Alomari

    2014-05-01

    Full Text Available The main idea of the current work is to use a wireless Electroencephalography (EEG headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested real time implementation of the system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of fists and feet movements in addition to closing eyes for a short period of time.

  14. Adaptive Brain Interfaces

    OpenAIRE

    Millán, José del R.

    2003-01-01

    Severely disabled people are largely excluded from the benefits information and communication technologies have brought to our industries, economies, appliances, and general quality of life. But what if that technology would allow them to communicate their wishes or control electronic devices directly through their thoughts alone? This is the goal and promise of the Adaptive Brain Interfaces (ABI) project, which aims to augment natural human capabilities by enabling people to interact with co...

  15. Task-Dependent Signal Variations in EEG Error-Related Potentials for Brain-Computer Interfaces

    OpenAIRE

    Iturrate, Inaki; Montesano, Luis; Minguez, Javier

    2013-01-01

    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...

  16. Shared-control brain-computer interface for a two dimensional reaching task using EEG error-related potentials

    OpenAIRE

    Iturrate, Inaki; Montesano, Luis; Minguez, Javier

    2013-01-01

    One of the main problems of EEG-based brain computer interfaces (BCIs) is their low information rate, thus for complex tasks the user needs large amounts of time to solve the task. In an attempt to reduce this time and improve the application robustness, recent works have explored shared-control strategies where the device does not only execute the decoded commands, but it is also involved in executing the task. This work proposes a shared-control BCI using error potentials for a 2D reaching ...

  17. 脑机接口技术研究概述%A Review of Brain-Computer Interface Technology

    Institute of Scientific and Technical Information of China (English)

    朱文明; 高诺

    2008-01-01

    脑机接口(Brain-Computer Interface, BCI)是在人脑和外界之间建立不依赖于常规大脑信息输出通路(外周神经和肌肉组织)的一种通讯系统.本文概述了基于脑电信号(EEG)的BCI技术的基本原理、研究方法、类型、研究现状,并分析了目前存在的问题与应用前景.

  18. A Review of the Brain - computer Interface Technology%脑-机接口技术研究概况

    Institute of Scientific and Technical Information of China (English)

    赵慧; 李远清

    2006-01-01

    脑-机接口(BCI-Brain-Computer Interface)是一种全新的通讯和控制技术.首先介绍BCI的定义和工作原理;从输入信号的类型选择、预处理、特征的提取、分类方法等方面论述了BCI系统设计中的关键技术;最后对BCI的应用及在未来的发展作了介绍.

  19. 脑机接口技术研究%The study of brain-computer interface technology

    Institute of Scientific and Technical Information of China (English)

    杨瑞霞

    2009-01-01

    脑机接口(Brain-Computer Interface,BCI)是在人脑和外界之间建立不依赖于常规大脑信息输出通路(外周神经和肌肉组织)的一种通讯系统.该文概述了基于脑电信号(EEG)的BCI技术的基本原理、研究方法、类型、研究现状,并分析了目前存在的问题与应用前景.

  20. Comparison of Classifiers and Statistical Analysis for EEG Signals Used in Brain Computer Interface Motor Task Paradigm

    Directory of Open Access Journals (Sweden)

    Oana Diana Eva

    2015-01-01

    Full Text Available Using the EEG Motor Movement/Imagery database there is proposed an off-line analysis for a brain computer interface (BCI paradigm. The purpose of the quantitative research is to compare classifiers in order to determinate which of them has highest rates of classification. The power spectral density method is used to evaluated the (desynchronizations that appear on Mu rhythm. The features extracted from EEG signals are classified using linear discriminant classifier (LDA, quadratic classifier (QDA and classifier based on Mahalanobis distance (MD. The differences between LDA, QDA and MD are small, but the superiority of QDA was sustained by analysis of variance (ANOVA.

  1. The Development of control system via Brain Computer Interface (BCI - Functional Electrical Stimulation (FES for paraplegic subject

    Directory of Open Access Journals (Sweden)

    K. A. A. Rahman

    2012-12-01

    Full Text Available Brain is known to be one of the powerful systems in human body because of its ability to give command and communicate throughout the body. The spinal cord is the pathway for impulses from the brain to the body as well as from the body to the brain. However, the bounty of this pathway could be lost due to spinal cord injury (SCI and that results in a loss of function especially mobility. A combination of Brain Computer Interface (BCI and Functional Electrical Stimulation (FES is among one of the technique to regain the mobility function of human body which will be the focused area of this research. In this study, Electroencephalography (EEG system will be used to capture the brain signal which will then drive the FES. A paraplegic subject will be involved in this study. The subject will be required to move the knee joint with involvement few muscle contraction. Overall, in this paper the combination of BCI-FES methods for development of rehabilitation system will be proposed. From this preliminary study, it can be summarized that the combination between BCI and FES potentially would provide a better rehabilitation system for SCI patient in comparison to the conventional FES system.

  2. Current challenges facing the translation of brain computer interfaces from preclinical trials to use in human patients

    Directory of Open Access Journals (Sweden)

    Maxwell D. Murphy

    2016-01-01

    Full Text Available Current research in brain computer interface (BCI technology is advancing beyond preclinical studies, with trials beginning in human patients. To date, these trials have been carried out with several different types of recording interfaces. The success of these devices has varied widely, but different factors such as the level of invasiveness, timescale of recorded information, and ability to maintain stable functionality of the device over a long period of time all must be considered in addition to accuracy in decoding intent when assessing the most practical type of device moving forward. Here, we discuss various approaches to BCIs, distinguishing between devices focusing on control of operations extrinsic to the subject (e.g., prosthetic limbs, computer cursors and those focusing on control of operations intrinsic to the brain (e.g. using stimulation or external feedback, including closed-loop or adaptive devices. In this discussion, we consider the current challenges facing the translation of various types of BCI technology to eventual human application.

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

    Science.gov (United States)

    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.

  4. A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem

    Directory of Open Access Journals (Sweden)

    Wilbert A. McClay

    2015-09-01

    Full Text Available Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.

  5. A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem.

    Science.gov (United States)

    McClay, Wilbert A; Yadav, Nancy; Ozbek, Yusuf; Haas, Andy; Attias, Hagaii T; Nagarajan, Srikantan S

    2015-01-01

    Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user's intent for specific keyboard strikes or mouse button presses. The BCI's data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject's MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. PMID:26437432

  6. Usability and Performance Measure of a Consumer-grade Brain Computer Interface System for Environmental Control by Neurological Patients

    Directory of Open Access Journals (Sweden)

    Farzin Deravi

    2015-07-01

    Full Text Available With the increasing incidence and prevalence of chronic brain injury patients and the current financial constraints in healthcare budgets, there is a need for a more intelligent way to realise the current practice of neuro-rehabilitation service provision. Brain-computer Interface (BCI systems have the potential to address this issue to a certain extent only if carefully designed research can demonstrate that these systems are accurate, safe, cost-effective, are able to increase patient/carer satisfaction and enhance their quality of life. Therefore, one of the objectives of the proposed study was to examine whether participants (patients with brain injury and a sample of reference population were able to use a low cost BCI system (Emotiv EPOC to interact with a computer and to communicate via spelling words. Patients participated in the study did not have prior experience in using BCI headsets so as to measure the user experience in the first-exposure to BCI training. To measure emotional arousal of participants we used an ElectroDermal Activity Sensor (Qsensor by Affectiva. For the signal processing and feature extraction of imagery controls the Cognitive Suite of Emotiv's Control Panel was used. Our study reports the key findings based on data obtained from a group of patients and a sample reference population and presents the implications for the design and development of a BCI system for communication and control. The study also evaluates the performance of the system when used practically in context of an acute clinical environment

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

    Science.gov (United States)

    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

  8. An open-source and cross-platform framework for Brain Computer Interface-guided robotic arm control.

    Science.gov (United States)

    Kubben, Pieter L; Pouratian, Nader

    2012-01-01

    Brain Computer Interfaces (BCIs) have focused on several areas, of which motor substitution has received particular interest. Whereas open-source BCI software is available to facilitate cost-effective collaboration between research groups, it mainly focuses on communication and computer control. We developed an open-source and cross-platform framework, which works with cost-effective equipment that allows researchers to enter the field of BCI-based motor substitution without major investments upfront. It is based on the C++ programming language and the Qt framework, and offers a separate class for custom MATLAB/Simulink scripts. It has been tested using a 14-channel wireless electroencephalography (EEG) device and a low-cost robotic arm that offers 5° of freedom. The software contains four modules to control the robotic arm, one of which receives input from the EEG device. Strengths, current limitations, and future developments will be discussed. PMID:23372966

  9. Electroencephalogram-based brain-computer interface system%基于脑电的脑-机接口系统

    Institute of Scientific and Technical Information of China (English)

    任亚莉

    2011-01-01

    BACKGROUND: Brain-computer interfaces (BCI) provide a direct communication and control channel for sending messages and instructions from brain to external computers or other electronic devices. Using the non-muscular channel, subjects with severe neuromuscular dysfunction can directly express their thought and manipulate the external devices without using human language and actions. This greatly enhances the ability of these subjects to manage external event and improves their quality of life.OBJECTIVE: To summarize latest research advances and problems in the BCI and discuss the research direction of BCI.METHODS: The literatures on BCI were searched on the PubMed database published from January 1990 to December 2009 with the key words "brain-computer interface, rehabilitation" in English. In addition, the related articles were also searched on CNKI-KNS published between January 1990 and December 2009 with the key words "brain-computer interface, signal processing and electroencephalography" in Chinese.RESULTS AND CONCLUSION: Researches of BCI system is still at a developing stage. There are some disadvantages, such as low rate of communications instability, especially for algorithm improvement and selection of signal processing.%背景:脑-机接口是在人脑与计算机或其它电子设备之间建立的直接交流和控制通道,通过这种通道,人就可以直接通过脑来表达想法或操纵设备,而不需要语言或动作,这可以有效增强身体严重残疾的患者与外界交流或控制外部环境的能力,以提高患者的生活质量.目的:总结近年来国内外有关脑-机接口系统的研究进展及存在的问题,探讨该领域进一步发展的方向.方法:应用计算机检索PubMed数据库中1990-01/2009-12脑-机接口方面的文献,检索词"brain-computer interface,Rehabilitatian",并限定语言为English;同时检索CNKI-KNS 1990-01/2009-12脑-机接口方面的文献,检索词为"脑-机接口,信号处理,脑电

  10. Classifying Single Trail Electroencephalogram Using Gaussian Smoothened Fast Hartley Transform for Brain Computer Interface during Motor Imagery

    Directory of Open Access Journals (Sweden)

    V. B. Deepa

    2011-01-01

    Full Text Available Problem statement: Brain-Computer Interface (BCI is a emerging research area which translates the brain signals for any motor related actions into computer understandable signals by capturing the signal, processing the signal and classifying the motor imagery. This area of work finds various applications in neuroprosthetics. Mental activity leads to changes of electrophysiological signals like the Electroencephalogram (EEG or Electrocorticogram (ECoG. Approach: The BCI system detects such changes and transforms it into a control signal which can, for example, be used as to control a electric wheel. In this study the BCI paradigm is tested by our proposed Gaussian smoothened Fast Hartley Transform (GS-FHT which is used to compute the energies of different motor imageries the subject thinks after selecting the required frequencies using band pass filter. Results: We apply this procedure to BCI Competition dataset IVA, a publicly available EEG repository. Conclusion: The evaluations of preprocessed signals showed that the extracted features were interpretable and can lead to high classification accuracy by various mining algorithms.

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

    Science.gov (United States)

    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.

  12. P300-based brain-computer interface communication: evaluation and follow-up in amyotrophic lateral sclerosis

    Directory of Open Access Journals (Sweden)

    Stefano Silvoni

    2009-06-01

    Full Text Available Objectives: To describe results of training and one-year follow-up of brain-communication in a larger group of early and middle stage amyotrophic lateral sclerosis (ALS patients using a P300-based brain-computer interface (BCI, and to investigate the relationship between clinical status, age and BCI performance. Methods: A group of 21 ALS patients were tested with a BCI-system using two-dimensional cursor movements. A four choice visual paradigm was employed to training and test the brain-communication abilities. The task consisted of reaching with the cursor one out of four icons representing four basic needs. Five patients performed a follow-up test one year later. The clinical severity in all patients were assessed with a battery of clinical tests. A comparable control group of 9 healthy subjects was employed to investigate performance differences. Results: 19 patients and 9 healthy subjects were able to achieve good and excellent cursor movements’ control, acquiring at least communication abilities above chance level; during follow-up the patients maintained their BCI-skill. We found mild cognitive impairments in the ALS group which may be attributed to motor deficiencies, while no relevant correlation has been found between clinical data and BCI performance. A positive correlation between age and the BCI-skill in patients was found. Conclusion: Time since training acquisition and clinical status did not affect the patients brain-communication skill at early and middle stage of the disease. Significance: A brain communication tool can be used in most ALS patients at early and middle stage of the disease, before entering the locked-in stage.

  13. A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People.

    Science.gov (United States)

    Placidi, Giuseppe; Petracca, Andrea; Spezialetti, Matteo; Iacoviello, Daniela

    2016-01-01

    A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel. PMID:26573655

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

    Science.gov (United States)

    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. PMID:27188144

  15. Control of a Wheelchair in an Indoor Environment Based on a Brain-Computer Interface and Automated Navigation.

    Science.gov (United States)

    Zhang, Rui; Li, Yuanqing; Yan, Yongyong; Zhang, Hao; Wu, Shaoyu; Yu, Tianyou; Gu, Zhenghui

    2016-01-01

    The concept of controlling a wheelchair using brain signals is promising. However, the continuous control of a wheelchair based on unstable and noisy electroencephalogram signals is unreliable and generates a significant mental burden for the user. A feasible solution is to integrate a brain-computer interface (BCI) with automated navigation techniques. This paper presents a brain-controlled intelligent wheelchair with the capability of automatic navigation. Using an autonomous navigation system, candidate destinations and waypoints are automatically generated based on the existing environment. The user selects a destination using a motor imagery (MI)-based or P300-based BCI. According to the determined destination, the navigation system plans a short and safe path and navigates the wheelchair to the destination. During the movement of the wheelchair, the user can issue a stop command with the BCI. Using our system, the mental burden of the user can be substantially alleviated. Furthermore, our system can adapt to changes in the environment. Two experiments based on MI and P300 were conducted to demonstrate the effectiveness of our system. PMID:26054072

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

    Directory of Open Access Journals (Sweden)

    Elisabeth V C Friedrich

    2014-07-01

    Full Text Available 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, 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.

  17. A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem

    Science.gov (United States)

    McClay, Wilbert A.; Yadav, Nancy; Ozbek, Yusuf; Haas, Andy; Attias, Hagaii T.; Nagarajan, Srikantan S.

    2015-01-01

    Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. PMID:26437432

  18. A brain-computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients.

    Science.gov (United States)

    Kübler, Andrea; Furdea, Adrian; Halder, Sebastian; Hammer, Eva Maria; Nijboer, Femke; Kotchoubey, Boris

    2009-03-01

    Using brain-computer interfaces (BCI) humans can select letters or other targets on a computer screen without any muscular involvement. An intensively investigated kind of BCI is based on the recording of visual event-related brain potentials (ERP). However, some severely paralyzed patients who need a BCI for communication have impaired vision or lack control of gaze movement, thus making a BCI depending on visual input no longer feasible. In an effort to render the ERP-BCI usable for this group of patients, the ERP-BCI was adapted to auditory stimulation. Letters of the alphabet were assigned to cells in a 5 x 5 matrix. Rows of the matrix were coded with numbers 1 to 5, and columns with numbers 6 to 10, and the numbers were presented auditorily. To select a letter, users had to first select the row and then the column containing the desired letter. Four severely paralyzed patients in the end-stage of a neurodegenerative disease were examined. All patients performed above chance level. Spelling accuracy was significantly lower with the auditory system as compared with a similar visual system. Patients reported difficulties in concentrating on the task when presented with the auditory system. In future studies, the auditory ERP-BCI should be adjusted by taking into consideration specific features of severely paralyzed patients, such as reduced attention span. This adjustment in combination with more intensive training will show whether an auditory ERP-BCI can become an option for visually impaired patients. PMID:19351359

  19. Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain-computer interface.

    Science.gov (United States)

    Kondo, Toshiyuki; Saeki, Midori; Hayashi, Yoshikatsu; Nakayashiki, Kosei; Takata, Yohei

    2015-10-01

    Event-related desynchronization (ERD) of the electroencephalogram (EEG) from the motor cortex is associated with execution, observation, and mental imagery of motor tasks. Generation of ERD by motor imagery (MI) has been widely used for brain-computer interfaces (BCIs) linked to neuroprosthetics and other motor assistance devices. Control of MI-based BCIs can be acquired by neurofeedback training to reliably induce MI-associated ERD. To develop more effective training conditions, we investigated the effect of static and dynamic visual representations of target movements (a picture of forearms or a video clip of hand grasping movements) during the BCI neurofeedback training. After 4 consecutive training days, the group that performed MI while viewing the video showed significant improvement in generating MI-associated ERD compared with the group that viewed the static image. This result suggests that passively observing the target movement during MI would improve the associated mental imagery and enhance MI-based BCIs skills. PMID:25467185

  20. Design of the multi-channel electroencephalography-based brain-computer interface with novel dry sensors.

    Science.gov (United States)

    Wu, Shang-Lin; Liao, Lun-De; Liou, Chang-Hong; Chen, Shi-An; Ko, Li-Wei; Chen, Bo-Wei; Wang, Po-Sheng; Chen, Sheng-Fu; Lin, Chin-Teng

    2012-01-01

    The traditional brain-computer interface (BCI) system measures the electroencephalography (EEG) signals by the wet sensors with the conductive gel and skin preparation processes. To overcome the limitations of traditional BCI system with conventional wet sensors, a wireless and wearable multi-channel EEG-based BCI system is proposed in this study, including the wireless EEG data acquisition device, dry spring-loaded sensors, a size-adjustable soft cap. The dry spring-loaded sensors are made of metal conductors, which can measure the EEG signals without skin preparation and conductive gel. In addition, the proposed system provides a size-adjustable soft cap that can be used to fit user's head properly. Indeed, the results are shown that the proposed system can properly and effectively measure the EEG signals with the developed cap and sensors, even under movement. In words, the developed wireless and wearable BCI system is able to be used in cognitive neuroscience applications. PMID:23366259

  1. Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment

    Directory of Open Access Journals (Sweden)

    Burke R

    2005-01-01

    Full Text Available This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP generated in response to phase-reversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.

  2. Testing the Self-Similarity Exponent to Feature Extraction in Motor Imagery Based Brain Computer Interface Systems

    Science.gov (United States)

    Rodríguez-Bermúdez, Germán; Sánchez-Granero, Miguel Ángel; García-Laencina, Pedro J.; Fernández-Martínez, Manuel; Serna, José; Roca-Dorda, Joaquín

    2015-12-01

    A Brain Computer Interface (BCI) system is a tool not requiring any muscle action to transmit information. Acquisition, preprocessing, feature extraction (FE), and classification of electroencephalograph (EEG) signals constitute the main steps of a motor imagery BCI. Among them, FE becomes crucial for BCI, since the underlying EEG knowledge must be properly extracted into a feature vector. Linear approaches have been widely applied to FE in BCI, whereas nonlinear tools are not so common in literature. Thus, the main goal of this paper is to check whether some Hurst exponent and fractal dimension based estimators become valid indicators to FE in motor imagery BCI. The final results obtained were not optimal as expected, which may be due to the fact that the nature of the analyzed EEG signals in these motor imagery tasks were not self-similar enough.

  3. A Two-Stage State Recognition Method for Asynchronous SSVEP-Based Brain-Computer Interface System

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zimu; DENG Zhidong

    2013-01-01

    A two-stage state recognition method is proposed for asynchronous SSVEP (steady-state visual evoked potential) based brain-computer interface (SBCI) system.The two-stage method is composed of the idle state (IS) detection and control state (CS) discrimination modules.Based on blind source separation and continuous wavelet transform techniques,the proposed method integrates functions of multi-electrode spatial filtering and feature extraction.In IS detection module,a method using the ensemble IS feature is proposed.In CS discrimination module,the ensemble CS feature is designed as feature vector for control intent classification.Further,performance comparisons are investigated among our IS detection module and other existing ones.Also the experimental results validate the satisfactory performance of our CS discrimination module.

  4. A comparison of classification techniques for a gaze-independent P300-based brain-computer interface

    Science.gov (United States)

    Aloise, F.; Schettini, F.; Aricò, P.; Salinari, S.; Babiloni, F.; Cincotti, F.

    2012-08-01

    This off-line study aims to assess the performance of five classifiers commonly used in the brain-computer interface (BCI) community, when applied to a gaze-independent P300-based BCI. In particular, we compared the results of four linear classifiers and one nonlinear: Fisher's linear discriminant analysis (LDA), stepwise linear discriminant analysis (SWLDA), Bayesian linear discriminant analysis (BLDA), linear support vector machine (LSVM) and Gaussian supported vector machine (GSVM). Moreover, different values for the decimation of the training dataset were tested. The results were evaluated both in terms of accuracy and written symbol rate with the data of 19 healthy subjects. No significant differences among the considered classifiers were found. The optimal decimation factor spanned a range from 3 to 24 (12 to 94 ms long bins). Nevertheless, performance on individually optimized classification parameters is not significantly different from a classification with general parameters (i.e. using an LDA classifier, about 48 ms long bins).

  5. Investigating the role of combined acoustic-visual feedback in one-dimensional synchronous brain computer interfaces, a preliminary study

    Directory of Open Access Journals (Sweden)

    Gargiulo GD

    2012-09-01

    Full Text Available Gaetano D Gargiulo,1–3 Armin Mohamed,1 Alistair L McEwan,1 Paolo Bifulco,2 Mario Cesarelli,2 Craig T Jin,1 Mariano Ruffo,2 Jonathan Tapson,3 André van Schaik31School of Electrical and Information Engineering, The University of Sydney, New South Wales, Australia; 2Dipartimento di Ingegneria Elettronica e delle Telecomunicazioni "Federico II" University of Naples, Naples, Italy; 3BENS Laboratory, MARCS Institute, The University of Western Sydney, New South Wales, AustraliaAbstract: Feedback plays an important role when learning to use a brain computer interface (BCI, particularly in the case of synchronous feedback that relies on the interaction subject. In this preliminary study, we investigate the role of combined auditory-visual feedback during synchronous µ rhythm-based BCI sessions to help the subject to remain focused on the selected imaginary task. This new combined feedback, now integrated within the general purpose BCI2000 software, has been tested on eight untrained and three trained subjects during a monodimensional left-right control task. In order to reduce the setup burden and maximize subject comfort, an electroencephalographic device suitable for dry electrodes that required no skin preparation was used. Quality and index of improvement was evaluated based on a personal self-assessment questionnaire from each subject and quantitative data based on subject performance. Results for this preliminary study show that the combined feedback was well tolerated by the subjects and improved performance in 75% of the naïve subjects compared with visual feedback alone.Keywords: brain computer interface, dry electrodes, subject feedback

  6. Communication and control by listening: towards optimal design of a two-class auditory streaming brain-computer interface

    Directory of Open Access Journals (Sweden)

    N. Jeremy Hill

    2012-12-01

    Full Text Available Most brain-computer interface (BCI systems require users to modulate brain signals in response to visual stimuli. Thus, they may not be useful to people with limited vision, such as those with severe paralysis. One important approach for overcoming this issue is auditory streaming, an approach whereby a BCI system is driven by shifts of attention between two dichotically presented auditory stimulus streams. Motivated by the long-term goal of translating such a system into a reliable, simple yes-no interface for clinical usage, we aim to answer two main questions. First, we asked which of two previously-published variants provides superior performance: a fixed-phase (FP design in which the streams have equal period and opposite phase, or a drifting-phase (DP design where the periods are unequal. We found FP to be superior to DP (p = 0.002: average performance levels were 80% and 72% correct, respectively. We were also able to show, in a pilot with one subject, that auditory streaming can support continuous control and neurofeedback applications: by shifting attention between ongoing left and right auditory streams, the subject was able to control the position of a paddle in a computer game. Second, we examined whether the system is dependent on eye movements, since it is known that eye movements and auditory attention may influence each other, and any dependence on the ability to move one’s eyes would be a barrier to translation to paralyzed users. We discovered that, despite instructions, some subjects did make eye movements that were indicative of the direction of attention. However, there was no correlation, across subjects, between the reliability of the eye movement signal and the reliability of the BCI system, indicating that our system was configured to work independently of eye movement. Together, these findings are an encouraging step forward toward BCIs that provide practical communication and control options for the most severely

  7. Universal computer interfaces

    CERN Document Server

    Dheere, RFBM

    1988-01-01

    Presents a survey of the latest developments in the field of the universal computer interface, resulting from a study of the world patent literature. Illustrating the state of the art today, the book ranges from basic interface structure, through parameters and common characteristics, to the most important industrial bus realizations. Recent technical enhancements are also included, with special emphasis devoted to the universal interface adapter circuit. Comprehensively indexed.

  8. Non-Invasive Brain Computer Interface for Mental Control of a Simulated Wheelchair

    OpenAIRE

    Lew, Eileen; Nuttin, Marnix; Ferrez, Pierre W.; Degeest, A.; Buttfield, Anna; Vanacker, G.; Millán, José del R.

    2006-01-01

    This poster presents results obtained from experiments of driving a brain-actuated simulated wheelchair that incorporates the shared-control intelligence method. The simulated wheelchair is controlled offline using band power features. The task is to drive the wheelchair along a corridor avoiding two obstacles. We have analyzed data from 4 na�ve subjects during 25 sessions carried out in two days. To measure the performance of the brain-actuated wheelchair we have compared the final positio...

  9. Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study

    Science.gov (United States)

    Jeunet, Camille; Jahanpour, Emilie; Lotte, Fabien

    2016-06-01

    Objective. While promising, electroencephaloraphy based brain–computer interfaces (BCIs) are barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users’ motor imagery based BCI (MI-BCI) control performance. Approach. We performed two experiments. The first consisted in evaluating the efficiency of a standard BCI training protocol for the acquisition of non-BCI related skills in a BCI-free context, which enabled us to rule out the possible impact of BCIs on the training outcome. Thus, participants (N = 54) were asked to perform simple motor tasks. The second experiment was aimed at measuring the correlations between motor tasks and MI-BCI performance. The ten best and ten worst performers of the first study were recruited for an MI-BCI experiment during which they had to learn to perform two MI tasks. We also assessed users’ spatial ability and pre-training μ rhythm amplitude, as both have been related to MI-BCI performance in the literature. Main results. Around 17% of the participants were unable to learn to perform the motor tasks, which is close to the BCI illiteracy rate. This suggests that standard training protocols are suboptimal for skill teaching. No correlation was found between motor tasks and MI-BCI performance. However, spatial ability played an important role in MI-BCI performance. In addition, once the spatial ability covariable had been controlled for, using an ANCOVA, it appeared that participants who faced difficulty during the first experiment improved during the second while the others did not. Significance. These studies suggest that (1) standard MI-BCI training protocols are suboptimal for skill teaching, (2) spatial ability is confirmed as impacting on MI-BCI performance, and (3) when faced

  10. A high frequency steady-state visually evoked potential based brain computer interface using consumer-grade EEG headset.

    Science.gov (United States)

    Białas, Piotr; Milanowski, Piotr

    2014-01-01

    This work evaluates a possibility of creating a high-frequency, SSVEP-based brain computer interface using a low cost EEG recording hardware - an Emotiv EEG Neuro-headset. Both above aspects are crucial to enable deploying the BCI technology in the consumer market. High frequencies can be used to create a non-tiring and more pleasant interface. Commercial EEG systems, as the Emotiv EEG, although demonstrating large underperformance, are much more affordable than standard, clinical-grade EEG amplifiers. A system classifying between two stimuli and rest is designed and tested in two experiments: on five and ten subject respectively. First, the accuracy of the system is compared for frequencies in lower range (17Hz, 19Hz, 23Hz, 25Hz) and higher range (31Hz, 33Hz, 37Hz, 40Hz). The mean online accuracy is 80%±15% for the former and 67%±12% for the latter. Second, a more thorough investigation is done by evaluating the system for frequencies within a set of 35Hz-40Hz. Although the mean accuracy, 64% ± 22%, is relatively low, most of the users were able to achieve satisfying accuracy, with the mean reaching 82%±5%, which would allow for an efficient, and yet pleasant, usage of the BCI system. In each case a user dependent approach is applied, with a calibration session lasting about five minutes. EEG feature extraction is done using common spatial pattern (CSP) filtering, canonical correlation analysis (CCA), and linear discrimination analysis (LDA). PMID:25571225

  11. Influence of P300 latency jitter on event related potential-based brain-computer interface performance

    Science.gov (United States)

    Aricò, P.; Aloise, F.; Schettini, F.; Salinari, S.; Mattia, D.; Cincotti, F.

    2014-06-01

    Objective. Several ERP-based brain-computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality. This study aims to investigate if this decrement (i) is fully explained by the lack of VEP contribution to the classification accuracy; (ii) correlates with lower temporal stability of the single-trial P300 potentials elicited in the covert attention modality. Approach. We evaluated the latency jitter of P300 evoked potentials in three BCI interfaces exploiting either overt or covert attention modalities in 20 healthy subjects. The effect of attention modality on the P300 jitter, and the relative contribution of VEPs and P300 jitter to the classification accuracy have been analyzed. Main results. The P300 jitter is higher when the BCI is controlled in covert attention. Classification accuracy negatively correlates with jitter. Even disregarding short-latency VEPs, overt-attention BCI yields better accuracy than covert. When the latency jitter is compensated offline, the difference between accuracies is not significant. Significance. The lower temporal stability of the P300 evoked potential generated during the tasks performed in covert attention modality should be regarded as the main contributing explanation of lower accuracy of covert-attention ERP-based BCIs.

  12. The computer graphics interface

    CERN Document Server

    Steinbrugge Chauveau, Karla; Niles Reed, Theodore; Shepherd, B

    2014-01-01

    The Computer Graphics Interface provides a concise discussion of computer graphics interface (CGI) standards. The title is comprised of seven chapters that cover the concepts of the CGI standard. Figures and examples are also included. The first chapter provides a general overview of CGI; this chapter covers graphics standards, functional specifications, and syntactic interfaces. Next, the book discusses the basic concepts of CGI, such as inquiry, profiles, and registration. The third chapter covers the CGI concepts and functions, while the fourth chapter deals with the concept of graphic obje

  13. Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain-computer interface

    Science.gov (United States)

    Widge, Alik S.; Moritz, Chet T.

    2014-04-01

    Objective. There is great interest in closed-loop neurostimulators that sense and respond to a patient's brain state. Such systems may have value for neurological and psychiatric illnesses where symptoms have high intraday variability. Animal models of closed-loop stimulators would aid preclinical testing. We therefore sought to demonstrate that rodents can directly control a closed-loop limbic neurostimulator via a brain-computer interface (BCI). Approach. We trained rats to use an auditory BCI controlled by single units in prefrontal cortex (PFC). The BCI controlled electrical stimulation in the medial forebrain bundle, a limbic structure involved in reward-seeking. Rigorous offline analyses were performed to confirm volitional control of the neurostimulator. Main results. All animals successfully learned to use the BCI and neurostimulator, with closed-loop control of this challenging task demonstrated at 80% of PFC recording locations. Analysis across sessions and animals confirmed statistically robust BCI control and specific, rapid modulation of PFC activity. Significance. Our results provide a preliminary demonstration of a method for emotion-regulating closed-loop neurostimulation. They further suggest that activity in PFC can be used to control a BCI without pre-training on a predicate task. This offers the potential for BCI-based treatments in refractory neurological and mental illness.

  14. A Link between the Increase in Electroencephalographic Coherence and Performance Improvement in Operating a Brain-Computer Interface.

    Science.gov (United States)

    Angulo-Sherman, Irma Nayeli; Gutiérrez, David

    2015-01-01

    We study the relationship between electroencephalographic (EEG) coherence and accuracy in operating a brain-computer interface (BCI). In our case, the BCI is controlled through motor imagery. Hence, a number of volunteers were trained using different training paradigms: classical visual feedback, auditory stimulation, and functional electrical stimulation (FES). After each training session, the volunteers' accuracy in operating the BCI was assessed, and the event-related coherence (ErCoh) was calculated for all possible combinations of pairs of EEG sensors. After at least four training sessions, we searched for significant differences in accuracy and ErCoh using one-way analysis of variance (ANOVA) and multiple comparison tests. Our results show that there exists a high correlation between an increase in ErCoh and performance improvement, and this effect is mainly localized in the centrofrontal and centroparietal brain regions for the case of our motor imagery task. This result has a direct implication with the development of new techniques to evaluate BCI performance and the process of selecting a feedback modality that better enhances the volunteer's capacity to operate a BCI system. PMID:26290661

  15. Brain Computer Interface human platform to control a 4-limb exoskeleton based on the ECoG-recording implant WIMAGINE®: preliminary results

    OpenAIRE

    Corinne Mestais

    2015-01-01

    The goal of CLINATEC® Brain Computer Interface Project is to improve tetraplegic subjects’ quality of life by allowing them to interact with their environment through the control of effectors with multiple degrees of freedom after training. Thanks to a long-term wireless 64-channel ECoG recording implant WIMAGINE® (Wireless Implantable Multi-channel Acquisition system for Generic Interface with NEurons) [1] and an innovative signal processing, the subject should be able to control a 4-limb ex...

  16. 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

    OpenAIRE

    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.

    2013-01-01

    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 o...

  17. Massively parallel signal processing using the graphics processing unit for real-time brain-computer interface feature extraction

    Directory of Open Access Journals (Sweden)

    J. Adam Wilson

    2009-07-01

    Full Text Available The clock speeds of modern computer processors have nearly plateaued in the past five 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 (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 CPU-based implementation that uses multi-threading. Significant performance gains were obtained with GPU processing: the current implementation processed 1000 channels in 933 ms, while the new GPU method took only 27 ms, an improvement of nearly 35 times.

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

    Science.gov (United States)

    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. PMID:19636394

  19. Increasing session-to-session transfer in a brain-computer interface with on-site background noise acquisition

    Science.gov (United States)

    Cho, Hohyun; Ahn, Minkyu; Kim, Kiwoong; Jun, Sung Chan

    2015-12-01

    Objective. A brain-computer interface (BCI) usually requires a time-consuming training phase during which data are collected and used to generate a classifier. Because brain signals vary dynamically over time (and even over sessions), this training phase may be necessary each time the BCI system is used, which is impractical. However, the variability in background noise, which is less dependent on a control signal, may dominate the dynamics of brain signals. Therefore, we hypothesized that an understanding of variations in background noise may allow existing data to be reused by incorporating the noise characteristics into the feature extraction framework; in this way, new session data are not required each time and this increases the feasibility of the BCI systems. Approach. In this work, we collected background noise during a single, brief on-site acquisition session (approximately 3 min) immediately before a new session, and we found that variations in background noise were predictable to some extent. Then we implemented this simple session-to-session transfer strategy with a regularized spatiotemporal filter (RSTF), and we tested it with a total of 20 cross-session datasets collected over multiple days from 12 subjects. We also proposed and tested a bias correction (BC) in the RSTF. Main results. We found that our proposed session-to-session strategies yielded a slightly less or comparable performance to the conventional paradigm (each session training phase is needed with an on-site training dataset). Furthermore, using an RSTF only and an RSTF with a BC outperformed existing approaches in session-to-session transfers. Significance. We inferred from our results that, with an on-site background noise suppression feature extractor and pre-existing training data, further training time may be unnecessary.

  20. Toward more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy.

    Science.gov (United States)

    Hwang, Han-Jeong; Choi, Han; Kim, Jeong-Youn; Chang, Won-Du; Kim, Do-Won; Kim, Kiwoong; Jo, Sungho; Im, Chang-Hwan

    2016-09-01

    In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to “yes” or “no” intentions (e.g., mental arithmetic calculation for “yes”). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient’s internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an “fNIRS-based direct intention decoding” paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing “yes” or “no” intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ± 1.39 and 74.08% ± 2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for p brain regions are associated with the processing of binary implicit intentions. Our experimental results demonstrated that direct decoding of internal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities. PMID:27050535

  1. Development of a Brain Computer Interface (BCI) Speller System Based on SSVEP Signals

    OpenAIRE

    Movahedi, Mohammad Mehdi; Mehdizadeh, Alireza; Alipour, Abolfazl

    2013-01-01

    BCI is one of the most intriguing technologies among other HCI systems, mostly because of its capability of recording brain activities. Spelling BCIs, which help paralyzed people to maintain communication, are one of the striking topics in the field of BCI. In this scientific a spelling BCI system with high transfer rate and accuracy that uses SSVEP signals is proposed. In addition, we suggested that LED light sources can provide proper signals for speller BCIs and they can be used in future.

  2. Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device

    Directory of Open Access Journals (Sweden)

    Brittany Mei Young

    2014-07-01

    Full Text Available Brain-computer interface (BCI technology is being incorporated into new stroke rehabilitation devices, but little is known about brain changes associated with its use. We collected anatomical and functional MRI of 9 stroke patients with persistent upper extremity motor impairment before, during, and after therapy using a BCI system. Subjects were asked to perform finger tapping of the impaired hand during fMRI. Action Research Arm Test (ARAT, 9-Hole Peg Test (9-HPT, and Stroke Impact Scale (SIS domains of Hand Function (HF and Activities of Daily Living (ADL were also assessed. Group-level analyses examined changes in whole-brain task-based functional connectivity (FC to seed regions in the motor network observed during and after BCI therapy. Whole-brain FC analyses seeded in each thalamus showed FC increases from baseline at mid-therapy and post-therapy (p< 0.05. Changes in FC between seeds at both the network and the connection levels were examined for correlations with changes in behavioral measures. Average motor network FC was increased post-therapy, and changes in average network FC correlated (p < 0.05 with changes in performance on ARAT (R2=0.21, 9-HPT (R2=0.41, SIS HF (R2=0.27, and SIS ADL (R2=0.40. Multiple individual connections within the motor network were found to correlate in change from baseline with changes in behavioral measures. Many of these connections involved the thalamus, with change in each of four behavioral measures significantly correlating with change from baseline FC of at least one thalamic connection. These preliminary results show changes in FC that occur with the administration of rehabilitative therapy using a BCI system. The correlations noted between changes in FC measures and changes in behavioral outcomes indicate that both adaptive and maladaptive changes in FC may develop with this therapy and also suggest a brain-behavior relationship that may be stimulated by the neuromodulatory component of BCI therapy.

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

    Science.gov (United States)

    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. PMID:24187246

  4. [A wireless smart home system based on brain-computer interface of steady state visual evoked potential].

    Science.gov (United States)

    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. PMID:25764705

  5. Single Trial Predictors for Gating Motor-Imagery Brain-Computer Interfaces Based on Sensorimotor Rhythm and Visual Evoked Potentials

    Science.gov (United States)

    Geronimo, Andrew; Kamrunnahar, Mst; Schiff, Steven J.

    2016-01-01

    For brain-computer interfaces (BCIs) that utilize visual cues to direct the user, the neural signals extracted by the computer are representative of ongoing processes, visual evoked responses, and voluntary modulation. We proposed to use three brain signatures for predicting success on a single trial of a BCI task. The first two features, the amplitude and phase of the pre-trial mu amplitude, were chosen as a correlate for cortical excitability. The remaining feature, related to the visually evoked response to the cue, served as a possible measure of fixation and attention to the task. Of these three features, mu rhythm amplitude over the central electrodes at the time of cue presentation and to a lesser extent the single trial visual evoked response were correlated with the success on the subsequent imagery task. Despite the potential for gating trials using these features, an offline gating simulation was limited in its ability to produce an increase in device throughput. This discrepancy highlights a distinction between the identification of predictive features, and the use of this knowledge in an online BCI. Using such a system, we cannot assume that the user will respond similarly when faced with a scenario where feedback is altered by trials that are gated on a regular basis. The results of this study suggest the possibility of using individualized, pre-task neural signatures for personalized, and asynchronous (self-paced) BCI applications, although these effects need to be quantified in a real-time adaptive scenario in a future study. PMID:27199630

  6. 脑计算机接口技术与应用前景%Technology and Appl ication Prospect of Brain-Computer Interface

    Institute of Scientific and Technical Information of China (English)

    贾花萍; 赵俊龙

    2015-01-01

    Brain-Computer interface (BCI)technology is a communication system between human brain and computer or other electronic equipments,the system understands the people's thinking through the EEG signal record,then controls the computer,equipments,intelli-gent household or unmanned vehicles by thinking.The technology involves in neuroscience,psychology of cognitive science,rehabilitation engineering,biomedical engineering and computer science and so on.Currently,brain-computer interface system is becoming a hot re-search,the paper introduces the structure,working principle,problems and prospect of brain-computer interface system.%脑计算机接口(Brain-Computer Interface,BCI)技术是在人脑和计算机或其他电子设备之间建立通信系统,该系统通过记录人的脑电信号来了解人的思维,用思维来控制计算机,操纵设备、智能家居、无人驾驶交通工具等。该技术涉及神经科学、心理认知科学、康复工程、生物医学工程和计算机科学等多种学科。目前,脑计算机接口系统正在成为研究热点,本文介绍了脑计算机接口系统的结构、工作原理、存在问题及发展前景。

  7. Conditional associative learning examined in a paralyzed patient with amyotrophic lateral sclerosis using brain-computer interface technology

    Directory of Open Access Journals (Sweden)

    Birbaumer N

    2008-11-01

    Full Text Available Abstract Background Brain-computer interface methodology based on self-regulation of slow-cortical potentials (SCPs of the EEG (electroencephalogram was used to assess conditional associative learning in one severely paralyzed, late-stage ALS patient. After having been taught arbitrary stimulus relations, he was evaluated for formation of equivalence classes among the trained stimuli. Methods A monitor presented visual information in two targets. The method of teaching was matching to sample. Three types of stimuli were presented: signs (A, colored disks (B, and geometrical shapes (C. The sample was one type, and the choice was between two stimuli from another type. The patient used his SCP to steer a cursor to one of the targets. A smiley was presented as a reward when he hit the correct target. The patient was taught A-B and B-C (sample – comparison matching with three stimuli of each type. Tests for stimulus equivalence involved the untaught B-A, C-B, A-C, and C-A relations. An additional test was discrimination between all three stimuli of one equivalence class presented together versus three unrelated stimuli. The patient also had sessions with identity matching using the same stimuli. Results The patient showed high accuracy, close to 100%, on identity matching and could therefore discriminate the stimuli and control the cursor correctly. Acquisition of A-B matching took 11 sessions (of 70 trials each and had to be broken into simpler units before he could learn it. Acquisition of B-C matching took two sessions. The patient passed all equivalence class tests at 90% or higher. Conclusion The patient may have had a deficit in acquisition of the first conditional association of signs and colored disks. In contrast, the patient showed clear evidence that A-B and B-C training had resulted in formation of equivalence classes. The brain-computer interface technology combined with the matching to sample method is a useful way to assess various

  8. Motor-related brain activity during action observation: a neural substrate for electrocorticographic brain-computer interfaces after spinal cord injury

    Directory of Open Access Journals (Sweden)

    Jennifer L Collinger

    2014-02-01

    Full Text Available After spinal cord injury (SCI, motor commands from the brain are unable to reach peripheral nerves and muscles below the level of the lesion. Action observation, in which a person observes someone else performing an action, has been used to augment traditional rehabilitation paradigms. Similarly, action observation can be used to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface (BCI even when the user cannot generate overt movements. BCIs use brain signals to control external devices to replace functions that have been lost due to SCI or other motor impairment. Previous studies have reported congruent motor cortical activity during observed and overt movements using magnetoencephalography (MEG and functional magnetic resonance imaging (fMRI. Recent single-unit studies using intracortical microelectrodes also demonstrated that a large number of motor cortical neurons had similar firing rate patterns between overt and observed movements. Given the increasing interest in electrocorticography (ECoG-based BCIs, our goal was to identify whether action observation-related cortical activity could be recorded using ECoG during grasping tasks. Specifically, we aimed to identify congruent neural activity during observed and executed movements in both the sensorimotor rhythm (10-40 Hz and the high-gamma band (65-115 Hz which contains significant movement-related information. We observed significant motor-related high-gamma band activity during action observation in both able-bodied individuals and one participant with a complete C4 SCI. Furthermore, in able-bodied participants, both the low and high frequency bands demonstrated congruent activity between action execution and observation. Our results suggest that action observation could be an effective and critical procedure for deriving the mapping from ECoG signals to intended movement for an ECoG-based BCI system for individuals with

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

    Science.gov (United States)

    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. PMID:26340647

  10. Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface

    Science.gov (United States)

    Acqualagna, Laura; Botrel, Loic; Vidaurre, Carmen; Kübler, Andrea; Blankertz, Benjamin

    2016-01-01

    In the last years Brain Computer Interface (BCI) technology has benefited from the development of sophisticated machine leaning methods that let the user operate the BCI after a few trials of calibration. One remarkable example is the recent development of co-adaptive techniques that proved to extend the use of BCIs also to people not able to achieve successful control with the standard BCI procedure. Especially for BCIs based on the modulation of the Sensorimotor Rhythm (SMR) these improvements are essential, since a not negligible percentage of users is unable to operate SMR-BCIs efficiently. In this study we evaluated for the first time a fully automatic co-adaptive BCI system on a large scale. A pool of 168 participants naive to BCIs operated the co-adaptive SMR-BCI in one single session. Different psychological interventions were performed prior the BCI session in order to investigate how motor coordination training and relaxation could influence BCI performance. A neurophysiological indicator based on the Power Spectral Density (PSD) was extracted by the recording of few minutes of resting state brain activity and tested as predictor of BCI performances. Results show that high accuracies in operating the BCI could be reached by the majority of the participants before the end of the session. BCI performances could be significantly predicted by the neurophysiological indicator, consolidating the validity of the model previously developed. Anyway, we still found about 22% of users with performance significantly lower than the threshold of efficient BCI control at the end of the session. Being the inter-subject variability still the major problem of BCI technology, we pointed out crucial issues for those who did not achieve sufficient control. Finally, we propose valid developments to move a step forward to the applicability of the promising co-adaptive methods. PMID:26891350

  11. Towards increasing the number of commands in a hybrid brain-computer interface with combination of gaze and motor imagery.

    Science.gov (United States)

    Meena, Yogesh Kumar; Cecotti, Hubert; KongFatt Wong-Lin; Prasad, Girijesh

    2015-08-01

    Non-invasive brain-computer interface (BCI) provides a novel means of communication. This can be achieved by measuring electroencephalogram (EEG) signal over the sensory motor cortex of a person performing motor imagery (MI) tasks. However, the performance of BCI remains currently too low to be of wide practical use. A hybrid BCI system could improve the performance by combining two or more modalities such as eye tracking, and the detection of brain activity responses. In this paper, first, we propose a simultaneous hybrid BCI that combines an event-related de-synchronization (ERD) BCI and an eye tracker. Second, we aim to further improve performance by increasing the number of commands (i.e., the number of choices accessible to the user). In particular, we show a significant improvement in performance for a simultaneous gaze-MI system using a total of eight commands. The experimental task requires subjects to search for spatially located items using gaze, and select an item using MI signals. This experimental task studied visuomotor compatible and incompatible conditions. As incorporating incompatible conditions between gaze direction and MI can increase the number of choices in the hybrid BCI, our experimental task includes single-trial detection for average, compatible and incompatible conditions, using seven different classification methods. The mean accuracy for MI, and the information transfer rate (ITR) for the compatible condition is found to be higher than the average and the incompatible conditions. The results suggest that gaze-MI hybrid BCI systems can increase the number of commands, and the location of the items should be taken into account for designing the system. PMID:26736310

  12. Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment

    Science.gov (United States)

    Zander, T. O.; Jatzev, S.

    2012-02-01

    Brain-computer interface (BCI) systems are usually applied in highly controlled environments such as research laboratories or clinical setups. However, many BCI-based applications are implemented in more complex environments. For example, patients might want to use a BCI system at home, and users without disabilities could benefit from BCI systems in special working environments. In these contexts, it might be more difficult to reliably infer information about brain activity, because many intervening factors add up and disturb the BCI feature space. One solution for this problem would be adding context awareness to the system. We propose to augment the available information space with additional channels carrying information about the user state, the environment and the technical system. In particular, passive BCI systems seem to be capable of adding highly relevant context information—otherwise covert aspects of user state. In this paper, we present a theoretical framework based on general human-machine system research for adding context awareness to a BCI system. Building on that, we present results from a study on a passive BCI, which allows access to the covert aspect of user state related to the perceived loss of control. This study is a proof of concept and demonstrates that context awareness could beneficially be implemented in and combined with a BCI system or a general human-machine system. The EEG data from this experiment are available for public download at www.phypa.org. Parts of this work have already been presented in non-journal publications. This will be indicated specifically by appropriate references in the text.

  13. Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Michael H. Thaut

    2005-11-01

    Full Text Available Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as “yes” or “no” or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct “yes”/“no” BCI from a single session. Blind source separation (BSS and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm (GA wrapped around a support vector machine (SVM classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also, BSS transformations of the EEG outperformed the original time series, particularly in conjunction with a subset search of both spaces. The results suggest that BSS and feature selection can be used to improve the performance of even a “direct,” single-session BCI.

  14. Localizing Sources of Brain Activity Relevant to Motor Imagery Brain-Computer Interface Performance, Using Individual Head Geometry

    Czech Academy of Sciences Publication Activity Database

    Frolov, A.; Húsek, Dušan; Bobrov, P.; Korshakov, A.V.; Chernikova, L.; Konovalov, R.; Mokienko, O.

    Vol. 1. Berlin: Springer, 2012 - (Wang, J.; Yen, G.; Polycarpou, M.), s. 369-378. (Lecture Notes in Computer Science. 7367). ISBN 978-3-642-31345-5. ISSN 0302-9743. [ISNN 2012. International Symposium on Neural Networks /9./. Shenyang (CN), 11.07.2012-14.07.2012] Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070 Institutional research plan: CEZ:AV0Z10300504 Keywords : BCI * ICA * Bayesian classification * EEG inverse problem * motor imagery * mu rhythm * fMRI Subject RIV: IN - Informatics, Computer Science

  15. Investigating the role of combined acoustic-visual feedback in one-dimensional synchronous brain computer interfaces, a preliminary study.

    Science.gov (United States)

    Gargiulo, Gaetano D; Mohamed, Armin; McEwan, Alistair L; Bifulco, Paolo; Cesarelli, Mario; Jin, Craig T; Ruffo, Mariano; Tapson, Jonathan; van Schaik, André

    2012-01-01

    Feedback plays an important role when learning to use a brain computer interface (BCI), particularly in the case of synchronous feedback that relies on the interaction subject. In this preliminary study, we investigate the role of combined auditory-visual feedback during synchronous μ rhythm-based BCI sessions to help the subject to remain focused on the selected imaginary task. This new combined feedback, now integrated within the general purpose BCI2000 software, has been tested on eight untrained and three trained subjects during a monodimensional left-right control task. In order to reduce the setup burden and maximize subject comfort, an electroencephalographic device suitable for dry electrodes that required no skin preparation was used. Quality and index of improvement was evaluated based on a personal self-assessment questionnaire from each subject and quantitative data based on subject performance. Results for this preliminary study show that the combined feedback was well tolerated by the subjects and improved performance in 75% of the naïve subjects compared with visual feedback alone. PMID:23152713

  16. Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface.

    Science.gov (United States)

    Zhang, Yu; Zhou, Guoxu; Jin, Jing; Zhao, Qibin; Wang, Xingyu; Cichocki, Andrzej

    2014-02-01

    Two main issues for event-related potential (ERP) classification in brain-computer interface (BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may deteriorate classification performance, especially with insufficient training samples resulted from limited calibration time. This study introduces an aggregation of sparse linear discriminant analyses (ASLDA) to overcome these problems. In the ASLDA, multiple sparse discriminant vectors are learned from differently l1-regularized least-squares regressions by exploiting the equivalence between LDA and least-squares regression, and are subsequently aggregated to form an ensemble classifier, which could not only implement automatic feature selection for dimensionality reduction to alleviate curse-of-dimensionality, but also decrease the variance to improve generalization capacity for new test samples. Extensive investigation and comparison are carried out among the ASLDA, the ordinary LDA and other competing ERP classification algorithms, based on different three ERP datasets. Experimental results indicate that the ASLDA yields better overall performance for single-trial ERP classification when insufficient training samples are available. This suggests the proposed ASLDA is promising for ERP classification in small sample size scenario to improve the practicability of BCI. PMID:24344691

  17. Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.

    Science.gov (United States)

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

    2016-02-01

    There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a number of parameters that must be carefully chosen. However, new non-linear classifiers were developed over the last decade. One of them is the random forest (RF) classifier. Although popular in other fields of science, RFs are not common in BCI research. In this work, we address three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA). We found that the performance of RF is constant over a large range of parameter values. We demonstrate - for the first time - that RFs are applicable online in SMR-BCIs. Further, we show in an offline BCI simulation that RFs statistically significantly outperform regularized LDA by about 3%. These results confirm that RFs are practical and convenient non-linear classifiers for SMR-BCIs. Taking into account further properties of RFs, such as independence from feature distributions, maximum margin behavior, multiclass and advanced data mining capabilities, we argue that RFs should be taken into consideration for future BCIs. PMID:25830903

  18. Attention modulations of posterior alpha as a control signal for two-dimensional brain-computer interfaces.

    Science.gov (United States)

    van Gerven, Marcel; Jensen, Ole

    2009-04-30

    Research on brain-computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a tool in cognitive neuroscience. Often, motor imagery is used to produce (binary) control signals. However, finding other types of control signals that allow the discrimination of multiple classes would help to increase the applicability of BCIs. We have investigated if modulation of posterior alpha activity by means of covert spatial attention in two dimensions can be reliably classified in single trials. Magnetoencephalography (MEG) data were collected for 15 subjects who were engaged in a task where they covertly had to visually attend left, right, up or down during a period of 2500 ms. We then classified the trials using support vector machines. The four orientations of covert attention could be reliably classified up to a maximum of 69% correctly classified trials (25% chance level) without the need for lengthy and burdensome subject training. Low classification performance in some subjects was explained by a low alpha signal. These findings support the case that modulation of alpha activity by means of covert spatial attention is promising as a control signal for a two-dimensional BCI. PMID:19428515

  19. Brain computer interface technology research review%脑机接口技术研究综述

    Institute of Scientific and Technical Information of China (English)

    李勃

    2013-01-01

    脑机接口技术(brain computer interface,BCI)不依赖于常规大脑信息输出通路,该技术建立了一种直接的信息交流和控制通道,为人脑和外界之间提供了一种全新的交互方式.简要介绍了BCI技术的定义和基本组成及发展现状,并对皮层慢电位、视觉诱发电位、眼动产生的α波、P300电位和基于运动想象的μ节律及β波5种脑机接口技术的研究方向作了简要阐述,最后指出目前BCI研究面临的挑战及未来的应用前景.

  20. Language-Model Assisted Brain Computer Interface for Typing: A Comparison of Matrix and Rapid Serial Visual Presentation.

    Science.gov (United States)

    Moghadamfalahi, Mohammad; Orhan, Umut; Akcakaya, Murat; Nezamfar, Hooman; Fried-Oken, Melanie; Erdogmus, Deniz

    2015-09-01

    Noninvasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) popularly utilize event-related potential (ERP) for intent detection. Specifically, for EEG-based BCI typing systems, different symbol presentation paradigms have been utilized to induce ERPs. In this manuscript, through an experimental study, we assess the speed, recorded signal quality, and system accuracy of a language-model-assisted BCI typing system using three different presentation paradigms: a 4 × 7 matrix paradigm of a 28-character alphabet with row-column presentation (RCP) and single-character presentation (SCP), and rapid serial visual presentation (RSVP) of the same. Our analyses show that signal quality and classification accuracy are comparable between the two visual stimulus presentation paradigms. In addition, we observe that while the matrix-based paradigm can be generally employed with lower inter-trial-interval (ITI) values, the best presentation paradigm and ITI value configuration is user dependent. This potentially warrants offering both presentation paradigms and variable ITI options to users of BCI typing systems. PMID:25775495

  1. High-frequency combination coding-based steady-state visual evoked potential for brain computer interface

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Feng; Zhang, Xin; Xie, Jun; Li, Yeping; Han, Chengcheng; Lili, Li; Wang, Jing [School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049 (China); Xu, Guang-Hua [School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049 (China); State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710054 (China)

    2015-03-10

    This study presents a new steady-state visual evoked potential (SSVEP) paradigm for brain computer interface (BCI) systems. The goal of this study is to increase the number of targets using fewer stimulation high frequencies, with diminishing subject’s fatigue and reducing the risk of photosensitive epileptic seizures. The new paradigm is High-Frequency Combination Coding-Based High-Frequency Steady-State Visual Evoked Potential (HFCC-SSVEP).Firstly, we studied SSVEP high frequency(beyond 25 Hz)response of SSVEP, whose paradigm is presented on the LED. The SNR (Signal to Noise Ratio) of high frequency(beyond 40 Hz) response is very low, which is been unable to be distinguished through the traditional analysis method; Secondly we investigated the HFCC-SSVEP response (beyond 25 Hz) for 3 frequencies (25Hz, 33.33Hz, and 40Hz), HFCC-SSVEP produces n{sup n} with n high stimulation frequencies through Frequence Combination Code. Further, Animproved Hilbert-huang transform (IHHT)-based variable frequency EEG feature extraction method and a local spectrum extreme target identification algorithmare adopted to extract time-frequency feature of the proposed HFCC-SSVEP response.Linear predictions and fixed sifting (iterating) 10 time is used to overcome the shortage of end effect and stopping criterion,generalized zero-crossing (GZC) is used to compute the instantaneous frequency of the proposed SSVEP respondent signals, the improved HHT-based feature extraction method for the proposed SSVEP paradigm in this study increases recognition efficiency, so as to improve ITR and to increase the stability of the BCI system. what is more, SSVEPs evoked by high-frequency stimuli (beyond 25Hz) minimally diminish subject’s fatigue and prevent safety hazards linked to photo-induced epileptic seizures, So as to ensure the system efficiency and undamaging.This study tests three subjects in order to verify the feasibility of the proposed method.

  2. High-frequency combination coding-based steady-state visual evoked potential for brain computer interface

    International Nuclear Information System (INIS)

    This study presents a new steady-state visual evoked potential (SSVEP) paradigm for brain computer interface (BCI) systems. The goal of this study is to increase the number of targets using fewer stimulation high frequencies, with diminishing subject’s fatigue and reducing the risk of photosensitive epileptic seizures. The new paradigm is High-Frequency Combination Coding-Based High-Frequency Steady-State Visual Evoked Potential (HFCC-SSVEP).Firstly, we studied SSVEP high frequency(beyond 25 Hz)response of SSVEP, whose paradigm is presented on the LED. The SNR (Signal to Noise Ratio) of high frequency(beyond 40 Hz) response is very low, which is been unable to be distinguished through the traditional analysis method; Secondly we investigated the HFCC-SSVEP response (beyond 25 Hz) for 3 frequencies (25Hz, 33.33Hz, and 40Hz), HFCC-SSVEP produces nn with n high stimulation frequencies through Frequence Combination Code. Further, Animproved Hilbert-huang transform (IHHT)-based variable frequency EEG feature extraction method and a local spectrum extreme target identification algorithmare adopted to extract time-frequency feature of the proposed HFCC-SSVEP response.Linear predictions and fixed sifting (iterating) 10 time is used to overcome the shortage of end effect and stopping criterion,generalized zero-crossing (GZC) is used to compute the instantaneous frequency of the proposed SSVEP respondent signals, the improved HHT-based feature extraction method for the proposed SSVEP paradigm in this study increases recognition efficiency, so as to improve ITR and to increase the stability of the BCI system. what is more, SSVEPs evoked by high-frequency stimuli (beyond 25Hz) minimally diminish subject’s fatigue and prevent safety hazards linked to photo-induced epileptic seizures, So as to ensure the system efficiency and undamaging.This study tests three subjects in order to verify the feasibility of the proposed method

  3. Application of Brain -computer Interface Technology in Medical Field%脑-计算机接口技术在医学领域的应用

    Institute of Scientific and Technical Information of China (English)

    赵俊龙; 贾花萍

    2015-01-01

    介绍脑-计算机接口系统的结构、工作原理,重点阐述该技术在医学领域中的应用,包括癫痫自动检测及分类、康复训练、麻醉深度检测等方面,指出脑-计算机接口技术面临的挑战。%The paper introduces the structure and working principle of brain -computer interface system, elaborates the application of the technology in medical field, including automatic detection and classification of epilepsy, rehabilitation training and anesthetic depth monitoring, pointing out the challenges brain -computer interface technology faces.

  4. Computer Interfaced Gauss Meter

    OpenAIRE

    Lo, Steven; Lai, Alan; Dao, Christine; Hung Vu, Hung

    2013-01-01

    Goal: Gauss Meter Model X01.  Gauss meter model X01 is the hand-held device designed to meet the needs of magnetic industry to measure magnetic fields accurately, provided high-end functionality and performance in an affordable laptop instrument. Magnet testing and sorting have never been easier. Additional features including calculating magnetic field intensity versus time and displaying magnetic field direction on a Graphical User Interface on Computer.  Introduction/Background:  Magnetic f...

  5. Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR

    Directory of Open Access Journals (Sweden)

    Eva Maria Hammer

    2014-08-01

    Full Text Available Modulation of sensorimotor rhythms (SMR was suggested as a control signal for brain-computer interfaces (BCI. Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80-100% performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning. Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1 A measure for the accuracy of fine motor skills, i.e. a trade for a person’s visuo-motor control ability and (2 subject’s attentional impulsivity. In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1 failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject the present predictors.

  6. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface

    Science.gov (United States)

    Chen, Xiaogang; Wang, Yijun; Gao, Shangkai; Jung, Tzyy-Ping; Gao, Xiaorong

    2015-08-01

    Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ˜33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min-1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

  7. Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload

    OpenAIRE

    Estepp, Justin R.; Christensen, James C.

    2015-01-01

    The passive brain-computer interface (pBCI) framework has been shown to be a very promising construct for assessing cognitive and affective state in both individuals and teams. There is a growing body of work that focuses on solving the challenges of transitioning pBCI systems from the research laboratory environment to practical, everyday use. An interesting issue is what impact methodological variability may have on the ability to reliably identify (neuro)physiological patterns that are use...

  8. Brain-computer interfaces based on event-related potentials: toward fast, reliable and easy-to-use communication systems for people with neurodegenerative disease

    OpenAIRE

    Kaufmann, Tobias

    2013-01-01

    Objective: Brain Computer Interfaces (BCI) provide a muscle independent interaction channel making them particularly valuable for individuals with severe motor impairment. Thus, different BCI systems and applications have been proposed as assistive technology (AT) solutions for such patients. The most prominent system for communication utilizes event-related potentials (ERP) obtained from the electroencephalogram (EEG) to allow for communication on a character-by-character basis. Yet in their...

  9. A Comparison of Two Spelling Brain-Computer Interfaces Based on Visual P3 and SSVEP in Locked-In Syndrome

    OpenAIRE

    Combaz, Adrien; Chatelle, Camille; Robben, Arne; Vanhoof, Gertie; Goeleven, Ann; Thijs, Vincent; Hulle, Marc van; Laureys, Steven

    2013-01-01

    Objectives: We study the applicability of a visual P3-based and a Steady State Visually Evoked Potentials (SSVEP)-based Brain-Computer Interfaces (BCIs) for mental text spelling on a cohort of patients with incomplete Locked-In Syndrome (LIS). Methods: Seven patients performed repeated sessions with each BCI. We assessed BCI performance, mental workload and overall satisfaction for both systems. We also investigated the effect of the quality of life and level of motor impairment on ...

  10. Comparison of eye tracking, electrooculography and an auditory brain-computer interface for binary communication: a case study with a participant in the locked-in state

    OpenAIRE

    Käthner, Ivo; Kübler, Andrea; Halder, Sebastian

    2015-01-01

    Background In this study, we evaluated electrooculography (EOG), an eye tracker and an auditory brain-computer interface (BCI) as access methods to augmentative and alternative communication (AAC). The participant of the study has been in the locked-in state (LIS) for 6 years due to amyotrophic lateral sclerosis. He was able to communicate with slow residual eye movements, but had no means of partner independent communication. We discuss the usability of all tested access methods and the pros...

  11. Advanced Technology in Brain-computer Interface%无创高通讯速率的实时脑-机接口系统

    Institute of Scientific and Technical Information of China (English)

    高上凯

    2007-01-01

    @@ 脑-机接口(brain computer interface,简称BCI)是通过实时记录人脑的脑电波,在一定程度上解读人的简单思维,并将其翻译成控制命令,来实现对计算机、家用电器、机器人等设备的控制(参见图1).

  12. Correction: Cecotti, H. and Rivet, B. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials. Brain Sci. 2014, 4, 335–355

    Directory of Open Access Journals (Sweden)

    Hubert Cecotti

    2014-09-01

    Full Text Available The authors wish to make the following correction to this paper (Cecotti, H.; Rivet, B. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials. Brain Sci. 2014, 4, 335–355: Due to an internal error, the reference numbers in the original published paper were not shown, and the error was not due to the authors. The former main text should be replaced as below.

  13. Neuro-navegática: software desenvolvido para interação com Brain Computer Interface para auxiliar o processo de inclusão escolar de pessoas com paralisia cerebral

    OpenAIRE

    Regina de Oliveira Heidrich; Marsal Avila Alves Branco; João Batista Mossmann; Anderson Schuh

    2014-01-01

    A Brain-Computer Interface (BCI), allows a person to transfer commands to a computer directly. Instead of using a keyboard, mouse or other input device, the user of this interface simply sends commands via brain waves and the computer responds to them. This paper aims to present a game developed to assist the process of educational inclusion of people with cerebral palsy. Qualitative research approach. To develop this research, we chose the case study, because it is a multifaceted res...

  14. Human-computer interface

    Science.gov (United States)

    Anderson, Thomas G.

    2004-12-21

    The present invention provides a method of human-computer interfacing. Force feedback allows intuitive navigation and control near a boundary between regions in a computer-represented space. For example, the method allows a user to interact with a virtual craft, then push through the windshield of the craft to interact with the virtual world surrounding the craft. As another example, the method allows a user to feel transitions between different control domains of a computer representation of a space. The method can provide for force feedback that increases as a user's locus of interaction moves near a boundary, then perceptibly changes (e.g., abruptly drops or changes direction) when the boundary is traversed.

  15. Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods

    Science.gov (United States)

    Schreuder, Martijn; Höhne, Johannes; Blankertz, Benjamin; Haufe, Stefan; Dickhaus, Thorsten; Tangermann, Michael

    2013-06-01

    Objective. In brain-computer interface (BCI) research, systems based on event-related potentials (ERP) are considered particularly successful and robust. This stems in part from the repeated stimulation which counteracts the low signal-to-noise ratio in electroencephalograms. Repeated stimulation leads to an optimization problem, as more repetitions also cost more time. The optimal number of repetitions thus represents a data-dependent trade-off between the stimulation time and the obtained accuracy. Several methods for dealing with this have been proposed as ‘early stopping’, ‘dynamic stopping’ or ‘adaptive stimulation’. Despite their high potential for BCI systems at the patient's bedside, those methods are typically ignored in current BCI literature. The goal of the current study is to assess the benefit of these methods. Approach. This study assesses for the first time the existing methods on a common benchmark of both artificially generated data and real BCI data of 83 BCI sessions, allowing for a direct comparison between these methods in the context of text entry. Main results. The results clearly show the beneficial effect on the online performance of a BCI system, if the trade-off between the number of stimulus repetitions and accuracy is optimized. All assessed methods work very well for data of good subjects, and worse for data of low-performing subjects. Most methods, however, are robust in the sense that they do not reduce the performance below the baseline of a simple no stopping strategy. Significance. Since all methods can be realized as a module between the BCI and an application, minimal changes are needed to include these methods into existing BCI software architectures. Furthermore, the hyperparameters of most methods depend to a large extend on only a single variable—the discriminability of the training data. For the convenience of BCI practitioners, the present study proposes linear regression coefficients for directly estimating

  16. Research of brain-computer interface automatic vehicle system based on SSVEP%基于SSVEP的脑-机接口自动车系统研究

    Institute of Scientific and Technical Information of China (English)

    赵丽; 孙永; 马彦臻; 何洋

    2011-01-01

    This paper mainly carried out proposes the research of SSVEP brain-computer interface automatic vehicle control systems,which describes the principles of the visual evoked potentials that used in brain-computer interface,and the single-chip is used to designs visual stimulation. Base on the LABVIEW platform, it also uses Hilbert Huang Transform to extract evoked potential vector continuously,which produces brain-computer interface control signals that can be applied to automatic vehicle control system to control the car around before and after exercise. According to a lot of experiments to verify,this sistem can send out the control commands that the correct rate is higher than 83% and can also send a command less than 5 seconds compared with the average time based on SSVEP,so it proves that the system is feasible and has a high application value.%阐述了视觉诱发电位用于脑-机接口的原理,系统采用单片机设计视觉刺激器,同时在LABVIEW平台上,利用希尔伯特黄变换实时提取诱发电位向量,产生脑机接口控制信号,并用于自动车控制系统,从而控制小车的前后左右运动.通过大量实验验证,设计的基于稳态视觉诱发电位的脑-机接口自动车控制系统,发送控制命令正确率高于83%,发送一个命令的平均时间低于5 s,证明该系统的方案是可行的,具有较高的应用价值.

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

    Science.gov (United States)

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

    2011-11-10

    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. PMID:21976021

  18. Statues and Challenges in Brain- Computer Interface Technologies%脑-机接口的研究现状与挑战

    Institute of Scientific and Technical Information of China (English)

    张莉; 何传红; 何为

    2007-01-01

    脑-机接口(Brain-computer interface,BCI)为人们提供了全新的与外界进行信息交流和控制的手段,具有非常重要的科学意义、学术价值和广阔的应用前景,是当今世界研究的热点.本文介绍了BCI的结构、类型和研究现状,分析了现有BCI系统存在的问题以及今后的发展方向.

  19. 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.

    Science.gov (United States)

    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 robotics, addresses limitations of current BMIs. PMID:24760914

  20. Real-time fMRI brain-computer interface: Development of a "motivational feedback" subsystem for the regulation of visual cue reactivity

    Directory of Open Access Journals (Sweden)

    Moses O. Sokunbi

    2014-11-01

    Full Text Available Here we present a novel neurofeedback subsystem for the presentation of motivationally relevant visual feedback during the self-regulation of functional brain activation. Our motivational neurofeedback approach uses functional magnetic resonance imaging (fMRI signals elicited by visual cues (pictures and related to motivational processes such as craving or hunger. The visual feedback subsystem provides simultaneous feedback through these images as their size corresponds to the magnitude of fMRI signal change from a target brain area. During self-regulation of cue-evoked brain responses, decreases and increases in picture size thus provide real motivational consequences in terms of cue approach versus cue avoidance, which increases face validity of the approach in applied settings. Further, the outlined approach comprises of neurofeedback (regulation and mirror runs that allow to control for non-specific and task-unrelated effects, such as habituation or neural adaptation. The approach was implemented in the Python programming language. Pilot data from 10 volunteers showed that participants were able to successfully down-regulate individually defined target areas, demonstrating feasibility of the approach. The newly developed visual feedback subsystem can be integrated into protocols for imaging-based brain-computer interfaces (BCI and may facilitate neurofeedback research and applications into healthy and dysfunctional motivational processes, such food craving or addiction.

  1. Real-time fMRI brain-computer interface: development of a "motivational feedback" subsystem for the regulation of visual cue reactivity.

    Science.gov (United States)

    Sokunbi, Moses O; Linden, David E J; Habes, Isabelle; Johnston, Stephen; Ihssen, Niklas

    2014-01-01

    Here we present a novel neurofeedback subsystem for the presentation of motivationally relevant visual feedback during the self-regulation of functional brain activation. Our "motivational neurofeedback" approach uses functional magnetic resonance imaging (fMRI) signals elicited by visual cues (pictures) and related to motivational processes such as craving or hunger. The visual feedback subsystem provides simultaneous feedback through these images as their size corresponds to the magnitude of fMRI signal change from a target brain area. During self-regulation of cue-evoked brain responses, decreases and increases in picture size thus provide real motivational consequences in terms of cue approach vs. cue avoidance, which increases face validity of the approach in applied settings. Further, the outlined approach comprises of neurofeedback (regulation) and "mirror" runs that allow to control for non-specific and task-unrelated effects, such as habituation or neural adaptation. The approach was implemented in the Python programming language. Pilot data from 10 volunteers showed that participants were able to successfully down-regulate individually defined target areas, demonstrating feasibility of the approach. The newly developed visual feedback subsystem can be integrated into protocols for imaging-based brain-computer interfaces (BCI) and may facilitate neurofeedback research and applications into healthy and dysfunctional motivational processes, such as food craving or addiction. PMID:25505392

  2. Non-invasive brain-to-brain interface (BBI: establishing functional links between two brains.

    Directory of Open Access Journals (Sweden)

    Seung-Schik Yoo

    Full Text Available Transcranial focused ultrasound (FUS is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI. In conjunction with the use of brain-to-computer interface (BCI techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat, thus creating a brain-to-brain interface (BBI. The implementation was aimed to non-invasively translate the human volunteer's intention to stimulate a rat's brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer's intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.

  3. New Heuristics for Interfacing Human Motor System using Brain Waves

    Directory of Open Access Journals (Sweden)

    Mohammed El-Dosuky

    2012-09-01

    Full Text Available There are many new forms of interfacing human users to machines. We persevere here electric-mechanical form of interaction between human and machine. The emergence of brain-computer interface allows mind-to-movement systems. The story of the Pied Piper inspired us to devise some new heuristics for interfacing human motor system using brain waves, by combining head helmet and LumbarMotionMonitor. For the simulation we use java GridGain. Brain responses of classified subjects during training indicates that Probe can be the best stimulus to rely on in distinguishing between knowledgeable and not knowledgeable

  4. New Heuristics for Interfacing Human Motor System using Brain Waves

    OpenAIRE

    Mohammed El-Dosuky; Ahmed El-Bassiouny; Taher Hamza; Magdy Rashad

    2012-01-01

    There are many new forms of interfacing human users to machines. We persevere here electric-mechanical form of interaction between human and machine. The emergence of brain-computer interface allows mind-to-movement systems. The story of the Pied Piper inspired us to devise some new heuristics for interfacing human motor system using brain waves, by combining head helmet and LumbarMotionMonitor. For the simulation we use java GridGain. Brain responses of classified subjects during training in...

  5. A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke.

    Science.gov (United States)

    Remsik, Alexander; Young, Brittany; Vermilyea, Rebecca; Kiekhoefer, Laura; Abrams, Jessica; Evander Elmore, Samantha; Schultz, Paige; Nair, Veena; Edwards, Dorothy; Williams, Justin; Prabhakaran, Vivek

    2016-05-01

    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

  6. Researches of Key Techniques on Brain-Computer Interface (BCI)%脑机接口关键技术研究

    Institute of Scientific and Technical Information of China (English)

    杨帮华; 颜国正; 丁国清; 于莲芝

    2005-01-01

    脑机接口(brain-computer interface,BCI)利用脑电信号实现人脑与计算机或其他电子设备的通讯和控制,是一种新的人机接口方式.它在康复医学和控制工程等领域有应用前景.本文介绍了BCIs系统的工作原理,从系统设计、数据获取及处理方法两方面论述了BCIs系统设计中的关键技术,最后指出了BCIs存在的主要问题和发展趋势.这些探讨为BCIs的设计与研究提供了指导.

  7. Brain computer interface for robot control research status%脑机接口在机器人控制中的研究现状

    Institute of Scientific and Technical Information of China (English)

    周春雨; 张毅

    2013-01-01

    脑机接口(brain-computer interface,BCI)是一种直接通过大脑与外部设备进行信息交流的技术.首先,介绍BCI系统的基本原理,BCI系统由信号采集、信号处理、输出设备和操作协议构成;其次,详述BCI的研究现状,重点介绍BCI在机器人假肢、轮式机器人及智能仿人机器人上的典型应用.最后指出:BCI这种非肌肉的通信与控制方法是可行的,这种方法可为无法使用传统方法的残障人士提供新的对外交流手段.

  8. Brain enabled mechanized speech synthesizer using Brain Mobile Interface

    Directory of Open Access Journals (Sweden)

    N.R.Raajan

    2013-02-01

    Full Text Available Communication is easily evoked when the necessity to carry out the thoughts and vision arises. As the communication technology developed it cultivated the threats of information security, on the otherhand physically challenged people have no possibility to communicate freely hence the urge for development in the field of communication is necessary in present scenario. Aim of this work is to propose a system that improves the present communication system. Here we had put forward the concept of Brain Mobile Interface (BMI from the basis of Brain Computer Interface (BCI. BMI serves as a device to translate human thoughts about speech without the need of physical movement. Wireless EEG headsets are used to acquire the speech signals directly from the brain and after signal processing it is given to the mobile which consist of inbuilt speller application. With the help of speller application the message to be conveyed is acquired as text which is then converted into speech by means of text to speech converter.

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

    OpenAIRE

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

    2015-01-01

    Background 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 inabilit...

  10. What will this do to me and my brain? Ethical issues in brain-to-brain interfacing

    Directory of Open Access Journals (Sweden)

    Elisabeth eHildt

    2015-02-01

    Full Text Available For several years now, brain-computer interfaces (BCIs in which brain signals are used to navigate a computer, a prostheses or a technical device, have been developed in various experimental contexts (Wolpaw & Wolpaw 2012; Grübler & Hildt 2014. Researchers have recently taken the next step and run experiments based on connections between two brains. These so-called brain-to-brain interfaces (abbreviation: BBIs or BTBIs involve not only a BCI component deriving information from a brain and sending it to a computer, but also a computer-brain interface (CBI component delivering information to another brain. What results is technology-mediated brain-to-brain communication (B2B communication, i.e. direct communication between two brains that does not involve any activity of the peripheral nervous system. In what follows, ethical issues that arise in neural interfacing will be discussed after a short introduction to recent BBI experiments. In this, the focus will be on the implications BBIs may have on the individual at the CBI side of the BBI, i.e. on the recipient.

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

    Science.gov (United States)

    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. PMID:24110169

  12. Systematic evaluation of non-invasive brain-computer interfaces as assistive devices for persons with severe motor impairment based on a user-centred approach – in controlled settings and independent use

    OpenAIRE

    Holz, Elisa Mira

    2016-01-01

    Brain-computer interfaces (BCIs) are devices that translate signals from the brain into control commands for applications. Within the last twenty years, BCI applications have been developed for communication, environmental control, entertainment, and substitution of motor functions. Since BCIs provide muscle independent communication and control of the environment by circumventing motor pathways, they are considered as assistive technologies for persons with neurological and neurodegenerative...

  13. Interfaz cerebro computador basada en P300 para la comunicación alternativa: estudio de caso en dos adolescentes en situación de discapacidad motora [P300 based Brain Computer Interface for alternative communication: a case study with two teenagers with motor disabilities

    OpenAIRE

    Garcia Cossio, E.; Fernandez, C; Gaviria, M.E.; Palacio, C.; Alvaran, L.; Torres Villa, R.A.

    2011-01-01

    Brain computer interface systems use brain signals to enable the control of external devices, such as: wheelchairs, communicators, neuro-prosthesis, among others; in people with severe motor disabilities. In this study two young men with motor disabilities were trained to learn how to control a brain computer interface (BCI) using the P300 evoked potential recorded by electroencephalography (EEG). This interface enables the verbal communication through a stimulation matrix (4x3 rows and colum...

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

    Science.gov (United States)

    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. PMID:27590973

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

    Science.gov (United States)

    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

  16. Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control.

    Science.gov (United States)

    Huang, Dandan; Qian, Kai; Fei, Ding-Yu; Jia, Wenchuan; Chen, Xuedong; Bai, Ou

    2012-05-01

    This study aims to propose an effective and practical paradigm for a brain-computer interface (BCI)-based 2-D virtual wheelchair control. The paradigm was based on the multi-class discrimination of spatiotemporally distinguishable phenomenon of event-related desynchronization/synchronization (ERD/ERS) in electroencephalogram signals associated with motor execution/imagery of right/left hand movement. Comparing with traditional method using ERD only, where bilateral ERDs appear during left/right hand mental tasks, the 2-D control exhibited high accuracy within a short time, as incorporating ERS into the paradigm hypothetically enhanced the spatiotemoral feature contrast of ERS versus ERD. We also expected users to experience ease of control by including a noncontrol state. In this study, the control command was sent discretely whereas the virtual wheelchair was moving continuously. We tested five healthy subjects in a single visit with two sessions, i.e., motor execution and motor imagery. Each session included a 20 min calibration and two sets of games that were less than 30 min. Average target hit rate was as high as 98.4% with motor imagery. Every subject achieved 100% hit rate in the second set of wheelchair control games. The average time to hit a target 10 m away was about 59 s, with 39 s for the best set. The superior control performance in subjects without intensive BCI training suggested a practical wheelchair control paradigm for BCI users. PMID:22498703

  17. EEG-Based Classification of New Imagery Tasks Using Three-Layer Feedforward Neural Network Classifier for Brain-Computer Interface

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    2006-10-01

    In this paper proposes the classification method of new imagery tasks for simple binary commands approach to a brain-computer interface (BCI). An analysis of imaginary tasks as “yes/no” have been proposed. Since BCI is very helpful technology for the patients who are suffering from severe motor disabilities. The BCI applications can be realized by using an electroencephalogram (EEG) signals recording at the scalp surface through the electrodes. Six healthy subjects (three males and three females), aged 23-30 years, were volunteered to participate in the experiment. During the experiment, 10-questions were used to be stimuli. The feature extraction of the event-related synchronization and event-related desynchronization (ERD/ERS) responses can be determined by the slope coefficient and Euclidian distance (SCED) method. The method uses the three-layer feedforward neural network based on a simple backpropagation algorithm to classify the two feature vectors. The experimental results of the proposed method show the average accuracy rates of 81.5 and 78.8% when the subjects imagine to “yes” and “no”, respectively.

  18. Effects of Brain-Computer Interface-controlled Functional Electrical Stimulation Training on Shoulder Subluxation for Patients with Stroke: A Randomized Controlled Trial.

    Science.gov (United States)

    Jang, Yun Young; Kim, Tae Hoon; Lee, Byoung Hee

    2016-06-01

    The purpose of this study was to investigate the effects of brain-computer interface (BCI)-controlled functional electrical stimulation (FES) training on shoulder subluxation of patients with stroke. Twenty subjects were randomly divided into two groups: the BCI-FES group (n = 10) and the FES group (n = 10). Patients in the BCI-FES group were administered conventional therapy with the BCI-FES on the shoulder subluxation area of the paretic upper extremity, five times per week during 6 weeks, while the FES group received conventional therapy with FES only. All patients were assessed for shoulder subluxation (vertical distance, VD; horizontal distance, HD), pain (visual analogue scale, VAS) and the Manual Function Test (MFT) at the time of recruitment to the study and after 6 weeks of the intervention. The BCI-FES group demonstrated significant improvements in VD, HD, VAS and MFT after the intervention period, while the FES group demonstrated significant improvements in HD, VAS and MFT. There were also significant differences in the VD and two items (shoulder flexion and abduction) of the MFT between the two groups. The results of this study suggest that BCI-FES training may be effective in improving shoulder subluxation of patients with stroke by facilitating motor recovery. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26876690

  19. Spelling is just a click away – a user-centered brain-computer interface including auto-calibration and predictive text entry

    Directory of Open Access Journals (Sweden)

    Tobias eKaufmann

    2012-05-01

    Full Text Available 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 interference, 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-centred ERP-BCI application on their own without expert interference. 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.

  20. Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application.

    Science.gov (United States)

    Naseer, Noman; Noori, Farzan M; Qureshi, Nauman K; Hong, Keum-Shik

    2016-01-01

    In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness and kurtosis-are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI. PMID:27252637

  1. Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application

    Directory of Open Access Journals (Sweden)

    Noman Naseer

    2016-05-01

    Full Text Available In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS signals with the best accuracies for development of a two-class brain-computer interface (BCI. Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR features — mean, slope, variance, peak, skewness and kurtosis — are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic versus rest using linear discriminant analysis (LDA. It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05 classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic versus rest for a two-class BCI.

  2. China's Brain -Computer Interface Research Progress and International Status%中国脑-机接口研究现状及其在国际中地位

    Institute of Scientific and Technical Information of China (English)

    欧阳昭连; 杨国忠; 池慧

    2010-01-01

    通过对我国脑-机接口(brain-computer Interface,BCI)研究现状的介绍、技术实验方法 的对比,进而探讨BCI研究面临的困难和挑战.本文从研究规模、地区侧重、研究机构分布、研究成果和驱动因素的角度,对WOS中的SCI论文和DII中的国际专利进行定量分析,描绘出我国BCI的研究现状及其所处国际地位.研究数据表明,我国BCI研究具有良好的基础,发展现状处于国际前沿,与美国、奥地利、德国等发达国家的研究机构同处国际领先地位.

  3. Interaction Technology in Brain-Computer Interface Systems%脑机接口系统中的交互技术研究

    Institute of Scientific and Technical Information of China (English)

    岳敬伟; 葛瑜; 周宗潭; 胡德文

    2008-01-01

    首先介绍了脑机接口(brain-computer interface,BCI)系统的概念和基本组成,交互技术在系统中的重要作用,以及目前国际上比较典型的交互技术;其次鉴于当前交互技术中存在的不足,分析了如何使人在脑机接口系统发挥更重要的作用,提出了一种新的脑机接口实验范式,该范式中随着被试操作技能的提高系统性能可以得到明显提升;之后建立了该实验范式的原型机系统--基于BCI的倒摆交互控制系统;最后对脑机交互技术的发展及脑机接口技术未来的应用傲了展望.

  4. Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application

    Science.gov (United States)

    Naseer, Noman; Noori, Farzan M.; Qureshi, Nauman K.; Hong, Keum-Shik

    2016-01-01

    In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features—mean, slope, variance, peak, skewness and kurtosis—are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI. PMID:27252637

  5. Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface

    Institute of Scientific and Technical Information of China (English)

    Bang-hua YANG; Liang-fei HE; Lin LIN; Qian WANG

    2015-01-01

    Ocular artifacts cause the main interfering signals within electroencephalogram (EEG) signal measurements. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject. To remove ocular artifacts from EEG in brain-computer interfaces (BCIs), a method named spatial constraint independent component analysis based recursive least squares (SCICA-RLS) is proposed. The method consists of two stages. In the first stage, independent component analysis (ICA) is used to decompose multiple EEG channels into an equal number of independent components (ICs). Ocular ICs are identified by an automatic artifact detection method based on kurtosis. Then empirical mode decomposition (EMD) is employed to remove any cerebral activity from the identified ocular ICs to obtain exact artifact ICs. In the second stage, first, SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal. These extracted ICs are called spatial constraint ICs (SC-ICs). Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference, which avoids the need for parallel EOG recordings. In addition, the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA. Based on the EEG data recorded from seven subjects, the new approach can lead to average classification accuracies of 3.3% and 12.6% higher than those of the standard ICA and raw EEG, respectively. In addition, the proposed method has 83.5% and 83.8% reduction in time-consumption compared with the standard ICA and ICA-RLS, respectively, which demonstrates a better and faster OA reduction.

  6. Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

    Directory of Open Access Journals (Sweden)

    Chanan S. Syan

    2010-01-01

    Full Text Available The P300 component of Event Related Brain Potentials (ERP is commonly used in Brain Computer Interfaces (BCI to translate the intentions of an individual into commands for external devices. The P300 response, however, resides in a signal environment of high background noise. Consequently, the main problem in developing a P300-based BCI lies in identifying the P300 response in the presence of this noise. Traditionally, attenuating the background activity of P300 data is done by averaging multiple trials of recorded signals. This method, though effective, suffers two drawbacks. First, collecting multiple trials of data is time consuming and delays the BCI response. Second, latency distortions may appear in the averaged result due to variable time-locking of the P300 in the individual trials. Problem statement: The use of single-trial P300 data overcomes both these shortcomings. However, single-trial data must be properly denoised to allow for reliable BCI operation. Single-trial P300-based BCIs have been implemented using a variety of signal processing techniques and classification methodologies. However, comparing the accuracies of these systems to other multi-trial systems is likely to include the comparison of more than just the trial format (single-trial/multi-trial as the data quality and recording circumstances are likely to be dissimilar. Approach: This issue was directly addressed by comparing the performance comparison of three different preprocessing agents and three classification methodologies on the same data set over both the single-trial and multi-trial settings. The P300 data set of BCI Competition II was used to facilitate this comparison. Results: The LDA classifier exhibited the best performance in classifying unseen P300 spatiotemporal features in both the single-trial (74.19% and multi-trial format (100%. It is also very efficient in terms of computational and memory requirements. Conclusion: This study can serve as a general

  7. Brain-Computer Interfaces for 1-D and 2-D Cursor Control: Designs Using Volitional Control of the EEG Spectrum or Steady-State Visual Evoked Potentials

    Science.gov (United States)

    Trejo, Leonard J.; Matthews, Bryan; Rosipal, Roman

    2005-01-01

    We have developed and tested two EEG-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KF LS classifier to map power spectra of 30-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display. Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject s average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions. Target Practice also implements online control of two artifact sources: a) removal of ocular artifact by linear subtraction of wavelet-smoothed vertical and horizontal EOG signals, b) control of muscle artifact by inhibition of BCI training during periods of relatively high power in the 40-64 Hz band. The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from eight electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point. As for Target Practice, Think Pointer also implements wavelet-based online removal of ocular artifact; however, in Think Pointer muscle

  8. Neuro-navegática: software desenvolvido para interação com Brain Computer Interface para auxiliar o processo de inclusão escolar de pessoas com paralisia cerebral

    Directory of Open Access Journals (Sweden)

    Regina de Oliveira Heidrich

    2014-08-01

    Full Text Available A Brain-Computer Interface (BCI, allows a person to transfer commands to a computer directly. Instead of using a keyboard, mouse or other input device, the user of this interface simply sends commands via brain waves and the computer responds to them. This paper aims to present a game developed to assist the process of educational inclusion of people with cerebral palsy. Qualitative research approach. To develop this research, we chose the case study, because it is a multifaceted research, in depth, of a single social phenomenon. Development of the Game Neuro-Navegática, with different forms of scanning speed, the software allows the user through the blink of an eye to choose the subject. In this example we opted for mathematics. The study on Brain Computer Interface (BCI seeks to improve the way of interaction between humans and machines and allows people with cerebral palsy to may be benefited in the inclusive education process as well as their peers without disabilities.

  9. An auditory multiclass brain-computer interface with natural stimuli: usability evaluation with healthy participants and a motor impaired end user

    Directory of Open Access Journals (Sweden)

    Nadine eSimon

    2015-01-01

    Full Text Available Brain-computer interfaces (BCIs can serve as muscle independent communication aids. Persons, who are unable to control their eye muscles (e.g. in the completely locked-in state or have severe visual impairments for other reasons, need BCI systems that do not rely on the visual modality. For this reason, BCIs that employ auditory stimuli were suggested. In this study, a multiclass BCI spelling system was implemented that uses animal voices with directional cues to code rows and columns of a letter matrix. To reveal possible training effects with the system, 11 healthy participants performed spelling tasks on two consecutive days. In a second step, the system was tested by a participant with amyotrophic lateral sclerosis (ALS in two sessions. In the first session, healthy participants spelled with an average accuracy of 76% (3.29 bits/min that increased to 90% (4.23 bits/min on the second day. Spelling accuracy by the participant with ALS was 20% in the first and 47% in the second session. The results indicate a strong training effect for both the healthy participants and the participant with ALS. While healthy participants reached high accuracies in the first session and second session, accuracies for the participant with ALS were not sufficient for satisfactory communication in both sessions. More training sessions might be needed to improve spelling accuracies. The study demonstrated the feasibility of the auditory BCI with healthy users and stresses the importance of training with auditory multiclass BCIs, especially for potential end-users of BCI with disease.

  10. Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload

    Directory of Open Access Journals (Sweden)

    Justin Ronald Estepp

    2015-03-01

    Full Text Available The passive brain-computer interface (pBCI framework has been shown to be a very promising construct for assessing cognitive and affective state in both individuals and teams. There is a growing body of work that focuses on solving the challenges of transitioning pBCI systems from the research laboratory environment to practical, everyday use. An interesting issue is what impact methodological variability may have on the ability to reliably identify (neurophysiological patterns that are useful for state assessment. This work aimed at quantifying the effects of methodological variability in a pBCI design for detecting changes in cognitive workload. Specific focus was directed toward the effects of replacing electrodes over dual sessions (thus inducing changes in placement, electromechanical properties, and/or impedance between the electrode and skin surface on the accuracy of several machine learning approaches in a binary classification problem. In investigating these methodological variables, it was determined that the removal and replacement of the electrode suite between sessions does not impact the accuracy of a number of learning approaches when trained on one session and tested on a second. This finding was confirmed by comparing to a control group for which the electrode suite was not replaced between sessions. This result suggests that sensors (both neurological and peripheral may be removed and replaced over the course of many interactions with a pBCI system without affecting its performance. Future work on multi-session and multi-day pBCI system use should seek to replicate this (lack of effect between sessions in other tasks, temporal time courses, and data analytic approaches while also focusing on non-stationarity and variable classification performance due to intrinsic factors.

  11. Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs.

    Science.gov (United States)

    Grimaldi, Giuliana; Manto, Mario; Jdaoudi, Yassin

    2013-01-01

    Tremor is the most common movement disorder encountered during daily neurological practice. Tremor in the upper limbs causes functional disability and social inconvenience, impairing daily life activities. The response of tremor to pharmacotherapy is variable. Therefore, a combination of drugs is often required. Surgery is considered when the response to medications is not sufficient. However, about one third of patients are refractory to current treatments. New bioengineering therapies are emerging as possible alternatives. Our study was carried out in the framework of the European project "Tremor" (ICT-2007-224051). The main purpose of this challenging project was to develop and validate a new treatment for upper limb tremor based on the combination of functional electrical stimulation (FES; which has been shown to reduce upper limb tremor) with a brain-computer interface (BCI). A BCI-driven detection of voluntary movement is used to trigger FES in a closed-loop approach. Neurological tremor is detected using a matrix of EMG electrodes and inertial sensors embedded in a wearable textile. The identification of the intentionality of movement is a critical aspect to optimize this complex system. We propose a multimodal detection of the intentionality of movement by fusing signals from EEG, EMG and kinematic sensors (gyroscopes and accelerometry). Parameters of prediction of movement are extracted in order to provide global prediction plots and trigger FES properly. In particular, quality parameters (QPs) for the EEG signals, corticomuscular coherence and event-related desynchronization/synchronization (ERD/ERS) parameters are combined in an original algorithm which takes into account the refractoriness/responsiveness of tremor. A simulation study of the relationship between the threshold of ERD/ERS of artificial EEG traces and the QPs is also provided. Very interestingly, values of QPs were much greater than those obtained for the corticomuscular module alone. PMID

  12. Motor imagery-induced EEG patterns in individuals with spinal cord injury and their impact on brain-computer interface accuracy

    Science.gov (United States)

    Müller-Putz, G. R.; Daly, I.; Kaiser, V.

    2014-06-01

    Objective. Assimilating the diagnosis complete spinal cord injury (SCI) takes time and is not easy, as patients know that there is no ‘cure' at the present time. Brain-computer interfaces (BCIs) can facilitate daily living. However, inter-subject variability demands measurements with potential user groups and an understanding of how they differ to healthy users BCIs are more commonly tested with. Thus, a three-class motor imagery (MI) screening (left hand, right hand, feet) was performed with a group of 10 able-bodied and 16 complete spinal-cord-injured people (paraplegics, tetraplegics) with the objective of determining what differences were present between the user groups and how they would impact upon the ability of these user groups to interact with a BCI. Approach. Electrophysiological differences between patient groups and healthy users are measured in terms of sensorimotor rhythm deflections from baseline during MI, electroencephalogram microstate scalp maps and strengths of inter-channel phase synchronization. Additionally, using a common spatial pattern algorithm and a linear discriminant analysis classifier, the classification accuracy was calculated and compared between groups. Main results. It is seen that both patient groups (tetraplegic and paraplegic) have some significant differences in event-related desynchronization strengths, exhibit significant increases in synchronization and reach significantly lower accuracies (mean (M) = 66.1%) than the group of healthy subjects (M = 85.1%). Significance. The results demonstrate significant differences in electrophysiological correlates of motor control between healthy individuals and those individuals who stand to benefit most from BCI technology (individuals with SCI). They highlight the difficulty in directly translating results from healthy subjects to participants with SCI and the challenges that, therefore, arise in providing BCIs to such individuals.

  13. Beyond maximum speed—a novel two-stimulus paradigm for brain-computer interfaces based on event-related potentials (P300-BCI)

    Science.gov (United States)

    Kaufmann, Tobias; Kübler, Andrea

    2014-10-01

    Objective. The speed of brain-computer interfaces (BCI), based on event-related potentials (ERP), is inherently limited by the commonly used one-stimulus paradigm. In this paper, we introduce a novel paradigm that can increase the spelling speed by a factor of 2, thereby extending the one-stimulus paradigm to a two-stimulus paradigm. Two different stimuli (a face and a symbol) are presented at the same time, superimposed on different characters and ERPs are classified using a multi-class classifier. Here, we present the proof-of-principle that is achieved with healthy participants. Approach. Eight participants were confronted with the novel two-stimulus paradigm and, for comparison, with two one-stimulus paradigms that used either one of the stimuli. Classification accuracies (percentage of correctly predicted letters) and elicited ERPs from the three paradigms were compared in a comprehensive offline analysis. Main results. The accuracies slightly decreased with the novel system compared to the established one-stimulus face paradigm. However, the use of two stimuli allowed for spelling at twice the maximum speed of the one-stimulus paradigms, and participants still achieved an average accuracy of 81.25%. This study introduced an alternative way of increasing the spelling speed in ERP-BCIs and illustrated that ERP-BCIs may not yet have reached their speed limit. Future research is needed in order to improve the reliability of the novel approach, as some participants displayed reduced accuracies. Furthermore, a comparison to the most recent BCI systems with individually adjusted, rapid stimulus timing is needed to draw conclusions about the practical relevance of the proposed paradigm. Significance. We introduced a novel two-stimulus paradigm that might be of high value for users who have reached the speed limit with the current one-stimulus ERP-BCI systems.

  14. Effects of Action Observational Training Plus Brain-Computer Interface-Based Functional Electrical Stimulation on Paretic Arm Motor Recovery in Patient with Stroke: A Randomized Controlled Trial.

    Science.gov (United States)

    Kim, TaeHoon; Kim, SeongSik; Lee, ByoungHee

    2016-03-01

    The purpose of this study was to investigate whether action observational training (AOT) plus brain-computer interface-based functional electrical stimulation (BCI-FES) has a positive influence on motor recovery of paretic upper extremity in patients with stroke. This was a hospital-based, randomized controlled trial with a blinded assessor. Thirty patients with a first-time stroke were randomly allocated to one of two groups: the BCI-FES group (n = 15) and the control group (n = 15). The BCI-FES group administered to AOT plus BCI-FES on the paretic upper extremity five times per week during 4 weeks while both groups received conventional therapy. The primary outcomes were the Fugl-Meyer Assessment of the Upper Extremity, Motor Activity Log (MAL), Modified Barthel Index and range of motion of paretic arm. A blinded assessor evaluated the outcomes at baseline and 4 weeks. All baseline outcomes did not differ significantly between the two groups. After 4 weeks, the Fugl-Meyer Assessment of the Upper Extremity sub-items (total, shoulder and wrist), MAL (MAL-Activity of Use and Quality of Movement), Modified Barthel Index and wrist flexion range of motion were significantly higher in the BCI-FES group (p BCI-based FES is effective in paretic arm rehabilitation by improving the upper extremity performance. The motor improvements suggest that AOT plus BCI-based FES can be used as a therapeutic tool for stroke rehabilitation. The limitations of the study are that subjects had a certain limited level of upper arm function, and the sample size was comparatively small; hence, it is recommended that future large-scale trials should consider stratified and lager populations according to upper arm function. PMID:26301519

  15. Performance evaluation of a motor-imagery-based EEG-Brain computer interface using a combined cue with heterogeneous training data in BCI-Naive subjects

    Directory of Open Access Journals (Sweden)

    Lee Youngbum

    2011-10-01

    Full Text Available Abstract Background The subjects in EEG-Brain computer interface (BCI system experience difficulties when attempting to obtain the consistent performance of the actual movement by motor imagery alone. It is necessary to find the optimal conditions and stimuli combinations that affect the performance factors of the EEG-BCI system to guarantee equipment safety and trust through the performance evaluation of using motor imagery characteristics that can be utilized in the EEG-BCI testing environment. Methods The experiment was carried out with 10 experienced subjects and 32 naive subjects on an EEG-BCI system. There were 3 experiments: The experienced homogeneous experiment, the naive homogeneous experiment and the naive heterogeneous experiment. Each experiment was compared in terms of the six audio-visual cue combinations and consisted of 50 trials. The EEG data was classified using the least square linear classifier in case of the naive subjects through the common spatial pattern filter. The accuracy was calculated using the training and test data set. The p-value of the accuracy was obtained through the statistical significance test. Results In the case in which a naive subject was trained by a heterogeneous combined cue and tested by a visual cue, the result was not only the highest accuracy (p Conclusions We propose the use of this measuring methodology of a heterogeneous combined cue for training data and a visual cue for test data by the typical EEG-BCI algorithm on the EEG-BCI system to achieve effectiveness in terms of consistence, stability, cost, time, and resources management without the need for a trial and error process.

  16. Effectiveness of the P3-speller in brain-computer interfaces for amyotrophic lateral sclerosis patients: a systematic review and meta-analysis

    Directory of Open Access Journals (Sweden)

    Konstantinos ePriftis

    2014-05-01

    Full Text Available A quarter of century ago, Farwell and Donchin have described their mental prosthesis for talking off the top of your head. This innovative communication system, later named P3-speller, has been the most investigated and tested brain-computer interface (BCI system, to date. A main goal of the research on P3-spellers was the development of an effective assistive device for patients with severe motor diseases. Among these patients are those affected by amyotrophic lateral sclerosis (ALS. ALS patients have become a target population in P3-speller (and more generally in BCI research. The P3-speller relies on the visual sensory modality, and it can be controlled by requiring users to actively move their eyes. Unfortunately, eye-movement control is usually not spared in the last stages of ALS, and, then, it is definitively lost in the case of complete paralysis. We reviewed the literature on ALS patients tested by means of P3-speller systems. Our aim was to investigate the evidence available to date of the P3-spellers effectiveness in ALS patients. To address this goal, a meta-analytic approach was adopted. The pooled classification accuracy performance, among retrieved studies, was about 74%. This estimation, however, was affected by significant heterogeneity and inconsistency among studies. This fact makes this percentage estimation (i.e., 74% unreliable. Nowadays, the conclusion is that the initial hopes posed on P3-speller for ALS patients have not been met yet. In addition, no trials in which the P3-speller has been compared to current assistive technologies for communication (e.g., eye trackers are available. In conclusion, further studies are required to obtain a reliable index of P3-speller effectiveness in ALS. Furthermore, comparisons of P3-speller systems with the available assistive technologies are needed to assess the P3 speller usefulness with non-completely paralyzed ALS-patients.

  17. A Comparison of Two Spelling Brain-Computer Interfaces Based on Visual P3 and SSVEP in Locked-In Syndrome

    Science.gov (United States)

    Robben, Arne; Vanhoof, Gertie; Goeleven, Ann; Thijs, Vincent; Van Hulle, Marc M.; Laureys, Steven

    2013-01-01

    Objectives We study the applicability of a visual P3-based and a Steady State Visually Evoked Potentials (SSVEP)-based Brain-Computer Interfaces (BCIs) for mental text spelling on a cohort of patients with incomplete Locked-In Syndrome (LIS). Methods Seven patients performed repeated sessions with each BCI. We assessed BCI performance, mental workload and overall satisfaction for both systems. We also investigated the effect of the quality of life and level of motor impairment on the performance. Results All seven patients were able to achieve an accuracy of 70% or more with the SSVEP-based BCI, compared to 3 patients with the P3-based BCI, showing a better performance with the SSVEP BCI than with the P3 BCI in the studied cohort. Moreover, the better performance of the SSVEP-based BCI was accompanied by a lower mental workload and a higher overall satisfaction. No relationship was found between BCI performance and level of motor impairment or quality of life. Conclusion Our results show a better usability of the SSVEP-based BCI than the P3-based one for the sessions performed by the tested population of locked-in patients with respect to all the criteria considered. The study shows the advantage of developing alternative BCIs with respect to the traditional matrix-based P3 speller using different designs and signal modalities such as SSVEPs to build a faster, more accurate, less mentally demanding and more satisfying BCI by testing both types of BCIs on a convenience sample of LIS patients. PMID:24086289

  18. A comparison of two spelling Brain-Computer Interfaces based on visual P3 and SSVEP in Locked-In Syndrome.

    Directory of Open Access Journals (Sweden)

    Adrien Combaz

    Full Text Available OBJECTIVES: We study the applicability of a visual P3-based and a Steady State Visually Evoked Potentials (SSVEP-based Brain-Computer Interfaces (BCIs for mental text spelling on a cohort of patients with incomplete Locked-In Syndrome (LIS. METHODS: Seven patients performed repeated sessions with each BCI. We assessed BCI performance, mental workload and overall satisfaction for both systems. We also investigated the effect of the quality of life and level of motor impairment on the performance. RESULTS: All seven patients were able to achieve an accuracy of 70% or more with the SSVEP-based BCI, compared to 3 patients with the P3-based BCI, showing a better performance with the SSVEP BCI than with the P3 BCI in the studied cohort. Moreover, the better performance of the SSVEP-based BCI was accompanied by a lower mental workload and a higher overall satisfaction. No relationship was found between BCI performance and level of motor impairment or quality of life. CONCLUSION: Our results show a better usability of the SSVEP-based BCI than the P3-based one for the sessions performed by the tested population of locked-in patients with respect to all the criteria considered. The study shows the advantage of developing alternative BCIs with respect to the traditional matrix-based P3 speller using different designs and signal modalities such as SSVEPs to build a faster, more accurate, less mentally demanding and more satisfying BCI by testing both types of BCIs on a convenience sample of LIS patients.

  19. Toward a semi-self-paced EEG brain computer interface: decoding initiation state from non-initiation state in dedicated time slots.

    Directory of Open Access Journals (Sweden)

    Lingling Yang

    Full Text Available Brain computer interfaces (BCIs offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are "synchronous" systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in "asynchronous" BCIs subjects pace the interaction and the system must determine when the subject's control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject's intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs.

  20. Diseño y aplicación de técnicas de análisis de electroencefalogramas para sistemas Brain Computer Interface basados en potenciales evocados

    OpenAIRE

    Núñez Novo, Pablo

    2014-01-01

    Los sistemas Brain Computer Interface (BCI) facilitan canales para enviar mensajes y órdenes al mundo externo sin realizar ninguna actividad muscular. Estos programas pueden habilitar a personas con discapacidades neuromusculares severas como la esclerosis lateral amiotrófica, ataque cerebral y lesiones de la médula espinal para comunicarse y operar programas como procesadores de texto. Para conseguirlo, se usan una serie de señales electrofisiológicas registradas en el cuero cabelludo ...

  1. Extracción y clasificación de características aplicados a señales electroencefalográficas en sistemas Brain Computer Interface basados en potenciales evocados P300

    OpenAIRE

    Núñez Novo, Pablo

    2015-01-01

    Los sistemas Brain Computer Interface (BCI) permiten enviar mensajes y órdenes al mundo externo sin necesidad de realizar ninguna actividad muscular. Estos programas pueden habilitar a personas con discapacidades neuromusculares severas como la esclerosis lateral amiotrófica, ataque cerebral y lesiones de la médula espinal para comunicarse y utilizar programas y equipos de diversos tipos que mejoran su calidad de vida. Para conseguir este objetivo, se usan una serie de señales electroencefa...

  2. Building an organic computing device with multiple interconnected brains

    OpenAIRE

    Miguel Pais-Vieira; Gabriela Chiuffa; Mikhail Lebedev; Amol Yadav; 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 ...

  3. Intention concepts and brain-machine interfacing

    Directory of Open Access Journals (Sweden)

    OlgaIljina

    2012-11-01

    Full Text Available Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs that are currently being developed to restore speech and motor control in paralyzed subjects. Such BMI devices record the brain activity of the agent, interpret (‘decode’ the agent’s intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent’s intentions from neural signals in practical BMI applications.

  4. Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain-computer interfaces in hemiplegic stroke survivors

    Science.gov (United States)

    Bundy, David T.; Wronkiewicz, Mark; Sharma, Mohit; Moran, Daniel W.; Corbetta, Maurizio; Leuthardt, Eric C.

    2012-06-01

    Brain-computer interface (BCI) systems have emerged as a method to restore function and enhance communication in motor impaired patients. To date, this has been applied primarily to patients who have a compromised motor outflow due to spinal cord dysfunction, but an intact and functioning cerebral cortex. The cortical physiology associated with movement of the contralateral limb has typically been the signal substrate that has been used as a control signal. While this is an ideal control platform in patients with an intact motor cortex, these signals are lost after a hemispheric stroke. Thus, a different control signal is needed that could provide control capability for a patient with a hemiparetic limb. Previous studies have shown that there is a distinct cortical physiology associated with ipsilateral, or same-sided, limb movements. Thus far, it was unknown whether stroke survivors could intentionally and effectively modulate this ipsilateral motor activity from their unaffected hemisphere. Therefore, this study seeks to evaluate whether stroke survivors could effectively utilize ipsilateral motor activity from their unaffected hemisphere to achieve this BCI control. To investigate this possibility, electroencephalographic (EEG) signals were recorded from four chronic hemispheric stroke patients as they performed (or attempted to perform) real and imagined hand tasks using either their affected or unaffected hand. Following performance of the screening task, the ability of patients to utilize a BCI system was investigated during on-line control of a one-dimensional control task. Significant ipsilateral motor signals (associated with movement intentions of the affected hand) in the unaffected hemisphere, which were found to be distinct from rest and contralateral signals, were identified and subsequently used for a simple online BCI control task. We demonstrate here for the first time that EEG signals from the unaffected hemisphere, associated with overt and

  5. Pericytes: brain-immune interface modulators

    Directory of Open Access Journals (Sweden)

    Gabriela eHurtado-Alvarado

    2014-01-01

    Full Text Available The premise that the central nervous system is immune-privileged arose from the fact that direct contact between immune and nervous cells is hindered by the blood-brain barrier. However, the blood-brain barrier also comprises the interface between the immune and nervous systems by secreting chemo-attractant molecules and by modulating immune cell entry into the brain. The majority of published studies on the blood-brain barrier focus on endothelial cells, which are a critical component, but not the only one; other cellular components include astroglia, microglia, and pericytes. Pericytes are poorly studied in comparison with astrocytes or endothelial cells; they are mesenchymal cells that can modify their ultrastructure and gene expression in response to changes in the central nervous system microenvironment. Pericytes have a unique synergistic relationship with brain endothelial cells in the regulation of capillary permeability through secretion of cytokines, chemokines, nitric oxide, matrix metallo-proteinases, and by means of capillary contraction. Those pericyte manifestations are related to changes in blood-brain barrier permeability by an increase in endocytosis-mediated transport and by tight junction disruption. In addition, recent reports demonstrate that pericytes control the migration of leukocytes in response to inflammatory mediators by up-regulating the expression of adhesion molecules and releasing chemo-attractants; however, under physiological conditions they appear to be immune-suppressors. Better understanding of the immune properties of pericytes and their participation in the effects of brain infections, neurodegenerative diseases, and sleep loss will be achieved by analyzing pericyte ultrastructure, capillary coverage, and protein expression. That knowledge may provide a mechanism by which pericytes participate in the maintenance of the proper function of the brain-immune interface.

  6. An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter.

    Science.gov (United States)

    Zhang, Dan; Huang, Bisheng; Wu, Wei; Li, Siliang

    2015-11-01

    Although accurate recognition of the idle state is essential for the application of brain-computer interfaces (BCIs) in real-world situations, it remains a challenging task due to the variability of the idle state. In this study, a novel algorithm was proposed for the idle state detection in a steady-state visual evoked potential (SSVEP)-based BCI. The proposed algorithm aims to solve the idle state detection problem by constructing a better model of the control states. For feature extraction, a maximum evoked response (MER) spatial filter was developed to extract neurophysiologically plausible SSVEP responses, by finding the combination of multi-channel electroencephalogram (EEG) signals that maximized the evoked responses while suppressing the unrelated background EEGs. The extracted SSVEP responses at the frequencies of both the attended and the unattended stimuli were then used to form feature vectors and a series of binary classifiers for recognition of each control state and the idle state were constructed. EEG data from nine subjects in a three-target SSVEP BCI experiment with a variety of idle state conditions were used to evaluate the proposed algorithm. Compared to the most popular canonical correlation analysis-based algorithm and the conventional power spectrum-based algorithm, the proposed algorithm outperformed them by achieving an offline control state classification accuracy of 88.0 ± 11.1% and idle state false positive rates (FPRs) ranging from 7.4 ± 5.6% to 14.2 ± 10.1%, depending on the specific idle state conditions. Moreover, the online simulation reported BCI performance close to practical use: 22.0 ± 2.9 out of the 24 control commands were correctly recognized and the FPRs achieved as low as approximately 0.5 event/min in the idle state conditions with eye open and 0.05 event/min in the idle state condition with eye closed. These results demonstrate the potential of the proposed algorithm for implementing practical SSVEP BCI systems. PMID

  7. A brain-computer interface based cognitive training system for healthy elderly: a randomized control pilot study for usability and preliminary efficacy.

    Directory of Open Access Journals (Sweden)

    Tih-Shih Lee

    Full Text Available Cognitive decline in aging is a pressing issue associated with significant healthcare costs and deterioration in quality of life. Previously, we reported the successful use of a novel brain-computer interface (BCI training system in improving symptoms of attention deficit hyperactivity disorder. Here, we examine the feasibility of the BCI system with a new game that incorporates memory training in improving memory and attention in a pilot sample of healthy elderly. This study investigates the safety, usability and acceptability of our BCI system to elderly, and obtains an efficacy estimate to warrant a phase III trial. Thirty-one healthy elderly were randomized into intervention (n = 15 and waitlist control arms (n = 16. Intervention consisted of an 8-week training comprising 24 half-hour sessions. A usability and acceptability questionnaire was administered at the end of training. Safety was investigated by querying users about adverse events after every session. Efficacy of the system was measured by the change of total score from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS before and after training. Feedback on the usability and acceptability questionnaire was positive. No adverse events were reported for all participants across all sessions. Though the median difference in the RBANS change scores between arms was not statistically significant, an effect size of 0.6SD was obtained, which reflects potential clinical utility according to Simon's randomized phase II trial design. Pooled data from both arms also showed that the median change in total scores pre and post-training was statistically significant (Mdn = 4.0; p<0.001. Specifically, there were significant improvements in immediate memory (p = 0.038, visuospatial/constructional (p = 0.014, attention (p = 0.039, and delayed memory (p<0.001 scores. Our BCI-based system shows promise in improving memory and attention in healthy

  8. Brain-Computer Interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke

    Directory of Open Access Journals (Sweden)

    Kai Keng eAng

    2014-07-01

    Full Text Available The objective of this study was to investigate the efficacy of an Electroencephalography (EEG-based Motor Imagery (MI Brain-Computer Interface (BCI coupled with a Haptic Knob (HK robot for arm rehabilitation in stroke patients. In this three-arm, single-blind, randomized controlled trial; 21 chronic hemiplegic stroke patients (Fugl-Meyer Motor Assessment (FMMA score 10-50, recruited after pre-screening for MI BCI ability, were randomly allocated to BCI-HK, HK or Standard Arm Therapy (SAT groups. All groups received 18 sessions of intervention over 6 weeks, 3 sessions per week, 90 minutes per session. The BCI-HK group received 1 hour of BCI coupled with HK intervention, and the HK group received 1 hour of HK intervention per session. Both BCI-HK and HK groups received 120 trials of robot-assisted hand grasping and knob manipulation followed by 30 minutes of therapist-assisted arm mobilization. The SAT group received 1.5 hours of therapist-assisted arm mobilization and forearm pronation-supination movements incorporating wrist control and grasp-release functions. In all, 14 males, 7 females, mean age 54.2 years, mean stroke duration 385.1 days, with baseline FMMA score 27.0 were recruited. The primary outcome measure was upper-extremity FMMA scores measured mid-intervention at week 3, end-intervention at week 6, and follow-up at weeks 12 and 24. Seven, 8 and 7 subjects underwent BCI-HK, HK and SAT interventions respectively. FMMA score improved in all groups, but no intergroup differences were found at any time points. Significantly larger motor gains were observed in the BCI-HK group compared to the SAT group at weeks 3, 12 and 24, but motor gains in the HK group did not differ from the SAT group at any time point. In conclusion, BCI-HK is effective, safe, and may have the potential for enhancing motor recovery in chronic stroke when combined with therapist-assisted arm mobilization.

  9. 一种基于两种不同范式的混合型脑-机接口系统%A Hybrid Brain-Computer Interface System Based on Two Different Paradigms

    Institute of Scientific and Technical Information of China (English)

    李翔; 高小榕; 高上凯

    2012-01-01

    混合模式脑机接口是脑-机接口研究的一个新方向,它为进一步提高脑-机接口系统性能提供了可能.现有的混合模式脑-机接口所采用的范式通常需要借助较强的视觉刺激,容易引起受试者疲劳等问题.本研究提出将运动想象和运动起始时刻视觉诱发电位两种无需强烈视觉刺激的范式以串行的方式相结合,通过运动起始时刻视觉诱发电位控制字符的输入,通过运动想象控制界面的开关和允许输入下一字符,实现了一种可用于字符输入的混合模式脑-机接口系统.为了验证系统的可行性,共完成了5例实验.实验中受试者首先进行两种范式的训练,然后进行开关系统界面和输入字符的测试.实验结果显示,经过一定训练的受试可以较好地完成系统的操作,运动想象单步操作平均时间最短为3.9s,字符输入的正确率最高可达93.3%.除了不容易令受试者产生疲劳外,本系统相比单一感觉模式的脑-机接口也具有可完成任务种类多、控制方式灵活等优势.%The hybrid brain-computer interface is a new research direction in brain-computer interface which provides a possibility to further enhance the performance of brain-computer interface system. Many of existing hybrid brain-computer interfac uses paradigms with strong visual stimulation,which is likely lead to subjects' fatigue. In this study,a hybrid brain-computer interface system for character input was proposed. The system combined motor imagery with motion-onset visual evoked potential paradigms sequentially. In this system,motion-onset visual evoked potential was used for character input,while motor imagery was used to switch the interface and allow to input the next character. Both these two paradigms do not depend on strong visual stimulations. In order to verify the feasibility of the system,a total of S cases of experiments have been completed. In the experiments,subjects first performed

  10. A STUDY ON BRAIN – MACHINE INTERFACE (BMI)

    OpenAIRE

    Abdul Muqeeth*,

    2015-01-01

    A brain – machine interface (BMI), sometimes called a mind - machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain – machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or se nsory - motor functions. The field of BCI research and developmen...

  11. Neuro-robotics from brain machine interfaces to rehabilitation robotics

    CERN Document Server

    Artemiadis

    2014-01-01

    Neuro-robotics is one of the most multidisciplinary fields of the last decades, fusing information and knowledge from neuroscience, engineering and computer science. This book focuses on the results from the strategic alliance between Neuroscience and Robotics that help the scientific community to better understand the brain as well as design robotic devices and algorithms for interfacing humans and robots. The first part of the book introduces the idea of neuro-robotics, by presenting state-of-the-art bio-inspired devices. The second part of the book focuses on human-machine interfaces for pe

  12. Application of digital signal processor in brain-computer interface based on motor imagery potential%DSP在基于想象动作电位的脑-机接口中的应用

    Institute of Scientific and Technical Information of China (English)

    赵丽; 高新

    2012-01-01

    应用数字信号处理器设计了一个基于想象动作电位的脑-机接口系统,通过模拟滤波与数字信号器处理相结合的方法,实现了对想象动作电位信息的有效采集和处理.系统在硬件上设计了脑电信号放大器,DSP开发板;对脑电信号放大、AD转换后,通过分类特征提取处理后,完成动作识别与控制命令的输出.将数字信号处理器应用在脑-机接口中,利用数字信号处理器优异的处理能力和丰富的外设资源,实现了一个嵌入式、微型化的脑-机接口构建,实现了脑-机接口实时处理与微型化.%This paper presents motor-imaginary-potential-based brain-computer interface (BCI) interface by using digital signal processor (DSP). Combining analog filtering and digital processing; realize the motor imaginary potential information acquisition effectively and processing. A EEG amplifier and DSP development board were designed in system for hardware. Motor imaginary potential information through EEG-amplifier and AD converted, after classification and feature extraction,gesture recognition will be completed and control command will be output to control peripherals. In this paper, digital signal processor (DSP) is used in brain-computer interface (BCI), using the DSP achieved a brain-computer interface real-time processing and miniaturization with its excellent processing power and rich resources of the peripheral. A on embedded technology Based, Miniaturization of brain-computer interface was built.

  13. What limits the performance of current invasive Brain Machine Interfaces?

    Directory of Open Access Journals (Sweden)

    Gytis eBaranauskas

    2014-04-01

    Full Text Available The concept of a brain-machine interface (BMI or a computer-brain interface is simple: BMI creates a communication pathway for a direct control by brain of an external device. In reality BMIs are very complex devices and only recently the increase in computing power of microprocessors enabled a boom in BMI research that continues almost unabated to this date, the high point being the insertion of electrode arrays into the brains of 5 human patients in a clinical trial run by Cyberkinetics with few other clinical tests still in progress. Meanwhile several EEG-based BMI devices (non-invasive BMIs were launched commercially. Modern electronics and dry electrode technology made possible to drive the cost of some of these devices below few hundred dollars. However, the initial excitement of the direct control by brain waves of a computer or other equipment is dampened by large efforts required for learning, high error rates and slow response speed. All these problems are directly related to low information transfer rates typical for such EEG-based BMIs. In invasive BMIs employing multiple electrodes inserted into the brain one may expect much higher information transfer rates than in EEG-based BMIs because, in theory, each electrode provides an independent information channel. However, although invasive BMIs require more expensive equipment and have ethical problems related to the need to insert electrodes in the live brain, such financial and ethical costs are often not offset by a dramatic improvement in the information transfer rate. Thus the main topic of this review is why in invasive BMIs an apparently much larger information content obtained with multiple extracellular electrodes does not translate into much higher rates of information transfer? This paper explores possible answers to this question by concluding that more research on what movement parameters are encoded by neurons in motor cortex is needed before we can enjoy the next

  14. Application of Functional Electrical Stimulation and Brain Computer Interface in Medicine%功能性电刺激与脑机接口在医学中的应用

    Institute of Scientific and Technical Information of China (English)

    隋宝石; 万柏坤

    2011-01-01

    To review the application of functional electrical stimulation (FES) on treatment of motor dysfunction and dysphagia induced by brain or spinal cord injury, and introduce the application of brain computer interface in modern medicine and the progress of BCI-based FES. The results show that BCI, FES and BCI-based FES are good prospect of new technology in modern rehabilitation engineering area.%本文回顾了功能性电刺激(Functional Electrical Stimulation,FES)在治疗脑损伤和脊髓损伤所造成的运动功能和吞咽障碍方面的应用;总结了脑机接口(Brain Computer Interface,BCI)技术在现代医学中的应用以及基于BCI的FES的研究现状.结果 显示,BCI、FES及二者相结合技术在现代康复工程领域中是极具应用前景的新技术.

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

    Science.gov (United States)

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

    2015-09-01

    , 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. PMID:26477360

  16. Interfaz cerebro computador basada en P300 para la comunicación alternativa: estudio de caso en dos adolescentes en situación de discapacidad motora [P300 based Brain Computer Interface for alternative communication: a case study with two teenagers with motor disabilities

    NARCIS (Netherlands)

    Garcia Cossio, E.; Fernandez, C.; Gaviria, M.E.; Palacio, C.; Alvaran, L.; Torres Villa, R.A.

    2011-01-01

    Brain computer interface systems use brain signals to enable the control of external devices, such as: wheelchairs, communicators, neuro-prosthesis, among others; in people with severe motor disabilities. In this study two young men with motor disabilities were trained to learn how to control a brai

  17. Review of brain-computer interface technology based on virtual reality environment%基于虚拟现实环境的脑机接口技术研究进展

    Institute of Scientific and Technical Information of China (English)

    孔丽文; 薛召军; 陈龙; 何峰; 綦宏志; 万柏坤; 明东

    2015-01-01

    Virtual reality based brain-computer interface ( BCI-VR ) , which combines brain-computer interface ( BCI) and virtual reality ( VR) technology, is a new BCI application that appears in the field of multimedia and entertainment.The advantages of both BCI and VR are embodied and promoted in BCI-VR, which provides wide prospect for the BCI-VR application.This paper reviews BCI-VR researches in recently years from many aspects, such as the basic system structure, the influence between BCI and VR and so on.It also summarizes the current difficulty and future trend of BCI-VR study, hoping to exchange ideas with readers to promote the fast development of BCI-VR technology.%将脑-机接口(brain-computer interface,BCI)技术与虚拟现实(virtual reality,VR)相结合构成基于虚拟现实的脑-机接口( BCI-VR)新技术是最近在多媒体和娱乐领域出现的一种BCI应用新模式。 BCI-VR兼取两者优势互补,同时又相互促进创新,显示出广阔应用前景。本文从BCI-VR系统基本构成、BCI对VR控制和VR对BCI影响等方面,较详细介绍了近年来BCI-VR的主要研究方法、研究进展和成就,并根据作者体会小结了目前存在的难点与未来的可能发展动向,以与读者交流、共同促进BCI-VR新技术的快速发展。

  18. What will this do to me and my brain? Ethical issues in brain-to-brain interfacing.

    Science.gov (United States)

    Hildt, Elisabeth

    2015-01-01

    Recent brain-to-brain interfacing studies provide proof of principle for the feasibility of various forms of direct information transfer between two brains, and may lead to the development of new approaches involving memory, emotions, or senses. What makes brain-to-brain interfaces unique is the transfer of information representing specific messages directly from one brain to another, without involving any activity of the peripheral nervous system or senses. The article discusses ethical issues that arise in neural interfacing. The focus is on the implications that brain-to-brain interfaces may have on the individual at the recipient side. PMID:25762903

  19. A chronic generalized bi-directional brain-machine interface

    Science.gov (United States)

    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.

  20. A chronic generalized bi-directional brain-machine interface.

    Science.gov (United States)

    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. PMID:21543839

  1. Computer screens and brain cancer

    International Nuclear Information System (INIS)

    Australia, both in the media and at the federal government level, over possible links between screen-based computer use and cancer, brain tumour in particular. The screen emissions assumed to be the sources of the putative hazard are the magnetic fields responsible for horizontal and vertical scanning of the display. Time-varying fluctuations in these magnetic fields induce electrical current flows in exposed tissues. This paper estimates that the induced current densities in the brain of the computer user are up to 1 mA/m2 (due to the vertical flyback). Corresponding values for other electrical appliances or installations are in general much less than this. The epidemiological literature shows no obvious signs of a sudden increase in brain tumour incidence, but the widespread use of computers is a relatively recent phenomenon. The occupational use of other equipment based on cathode ray tubes (such as TV repair) has a much longer history and has been statistically linked to brain tumour in some studies. A number of factors make this an unreliable indicator of the risk from computer screens, however. 42 refs., 3 tabs., 2 figs

  2. BRAIN MACHINE INTERFACING WITH IoT FUNTIONALITY

    OpenAIRE

    Himanshu Lunia, Meera Bagdai

    2016-01-01

    Brain dead people and people having any disability related to brain cannot normally communicate with others and for their betterment some electronic need to be developed and Brain Machine Interfacing (BMI) is one such solution. BMI includes extracting brain signals directly from the skull of the subject and interfacing it with a machine to determine the state of thinking and act accordingly. The brainwaves are collected by non-invasive electrodes and the output is fed to an amplifier and filt...

  3. TMS communications hardware. Volume 1: Computer interfaces

    Science.gov (United States)

    Brown, J. S.; Weinrich, S. S.

    1979-01-01

    A prototpye coaxial cable bus communications system was designed to be used in the Trend Monitoring System (TMS) to connect intelligent graphics terminals (based around a Data General NOVA/3 computer) to a MODCOMP IV host minicomputer. The direct memory access (DMA) interfaces which were utilized for each of these computers are identified. It is shown that for the MODCOMP, an off-the-shell board was suitable, while for the NOVAs, custon interface circuitry was designed and implemented.

  4. Human-computer interface and human reliability

    International Nuclear Information System (INIS)

    Issues associated with the impact of human-computer interfaces on human performance and reliability are discussed. Potential benefits of computer-based interfaces are identified as are concerns which could mitigate these benefits. In this paper it is suggested that the primary issues affecting human reliability in advanced systems involve allocation of function strategies which support operator situation awareness, specification of cognitive processing resources so minimal competition for shared resources occurs, and design of the user interface and information displays which supports task accomplishment and rapid assimilation of information by the operator. Examples of problems associated with each of these issues are briefly discussed

  5. Neuroplasticity subserving the operation of brain-machine interfaces.

    Science.gov (United States)

    Oweiss, Karim G; Badreldin, Islam S

    2015-11-01

    Neuroplasticity is key to the operation of brain machine interfaces (BMIs)-a direct communication pathway between the brain and a man-made computing device. Whereas exogenous BMIs that associate volitional control of brain activity with neurofeedback have been shown to induce long lasting plasticity, endogenous BMIs that use prolonged activity-dependent stimulation--and thus may curtail the time scale that governs natural sensorimotor integration loops--have been shown to induce short lasting plasticity. Here we summarize recent findings from studies using both categories of BMIs, and discuss the fundamental principles that may underlie their operation and the longevity of the plasticity they induce. We draw comparison to plasticity mechanisms known to mediate natural sensorimotor skill learning and discuss principles of homeostatic regulation that may constrain endogenous BMI effects in the adult mammalian brain. We propose that BMIs could be designed to facilitate structural and functional plasticity for the purpose of re-organization of target brain regions and directed augmentation of sensorimotor maps, and suggest possible avenues for future work to maximize their efficacy and viability in clinical applications. PMID:25968934

  6. Usability and performance-informed selection of personalized mental tasks for an online near-infrared spectroscopy brain-computer interface

    Science.gov (United States)

    Weyand, Sabine; Schudlo, Larissa; Takehara-Nishiuchi, Kaori; Chau, Tom

    2015-01-01

    Abstract. Brain–computer interfaces (BCIs) allow individuals to use only cognitive activities to interact with their environment. The widespread use of BCIs is limited, due in part to their lack of user-friendliness. The main goal of this work was to develop a more user-centered BCI and determine if: (1) individuals can acquire control of an online near-infrared spectroscopy BCI via usability and performance-informed selection of mental tasks without compromising classification accuracy and (2) the combination of usability and performance-informed selection of mental tasks yields subjective ease-of-use ratings that exceed those attainable with prescribed mental tasks. Twenty able-bodied participants were recruited. Half of the participants served as a control group, using the state-of-the-art prescribed mental strategies. The other half of the participants comprised the study group, choosing their own personalized mental strategies out of eleven possible tasks. It was concluded that users were, in fact, able to acquire control of the more user-centered BCI without a significant change in accuracy compared to the prescribed task BCI. Furthermore, the personalized BCI yielded higher subjective ease-of-use ratings than the prescribed BCI. Average online accuracies of 77±12.9% and 73±12.9% were achieved by the personalized and prescribed mental task groups, respectively. PMID:26158005

  7. Prospects of brain-machine interfaces for space system control

    Science.gov (United States)

    Menon, Carlo; de Negueruela, Cristina; Millán, José del R.; Tonet, Oliver; Carpi, Federico; Broschart, Michael; Ferrez, Pierre; Buttfield, Anna; Tecchio, Franca; Sepulveda, Francisco; Citi, Luca; Laschi, Cecilia; Tombini, Mario; Dario, Paolo; Maria Rossini, Paolo; De Rossi, Danilo

    2009-02-01

    The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical and electronic systems. This paper describes the state of the art of non-invasive brain-machine interfaces (BMIs) and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation.

  8. What will this do to me and my brain? Ethical issues in brain-to-brain interfacing

    OpenAIRE

    Hildt, Elisabeth

    2015-01-01

    Recent brain-to-brain interfacing studies provide proof of principle for the feasibility of various forms of direct information transfer between two brains, and may lead to the development of new approaches involving memory, emotions, or senses. What makes brain-to-brain interfaces unique is the transfer of information representing specific messages directly from one brain to another, without involving any activity of the peripheral nervous system or senses. The article discusses ethical issu...

  9. Research on control system of brain-computer interface based on the alpha waves in EEG%基于脑电Alpha波的脑-机接口控制系统研究

    Institute of Scientific and Technical Information of China (English)

    张谦; 王振; 秦琦; 万柏坤

    2006-01-01

    设计了1种基于脑电α波的脑-机接口(Brain-Computer Interface,BCI)系统,利用睁闭眼对于α波幅值的影响,实现了对计算机屏幕4个方向目标的选择控制,并通过5名受试者实验考察了系统的速度、准确率,说明系统具有无须繁复学习与生物反馈训练的易操作性,为进一步研究开发能实时操作使用的BCI控制面板、实现系统集成和便携化提供了技术基础,具有潜在应用价值,值得进一步研究.

  10. 脑机接口中基于MRP的半监督判决空间模式法%Semi-Supervised Discriminative Spatial Patterns Based on MRP for Brain-Computer Interfaces

    Institute of Scientific and Technical Information of China (English)

    吕俊

    2011-01-01

    In the study of brain-computer interface,if the number of training samples is small,the features of movement related potentials can not be well extracted by discriminative spatial pattern algorithm.Thus in this paper,semi-supervised self-training scheme i%在脑-机接口研究中,如果训练样本少,判决空间模式法不能很好地提取运动相关电位特征。为此,文中在半监督框架下,采用自训练方法,引入分类置信度高的无标记样本,迭代学习MRP的空间判决模式。实验结果验证了所提算法的有效性。

  11. 基于功能近红外光谱技术的脑机接口研究%Brain-computer Interface's Research based on Near-infrared Spectroscopy

    Institute of Scientific and Technical Information of China (English)

    胡汉彬; 祝晔; 蒋田仔

    2010-01-01

    我们将功能近红外光谱技术运用于脑机接口(brain-computer interface, BCI)的研究中.通过动手指、想象动手指、听觉三个任务记录大脑的响应活动,计算Hurst指数,输入到BP神经网络中,建立了任务和脑响应的相关模型.实验结果表明,响应与任务的准确识别率达到了70%,说明了近红外光谱技术应用于脑机接口研究中的可能性.

  12. Accumulate information based on DGMM for brain-computer interface%基于判别混合高斯模型的信息积累方法及在脑机接口中的应用

    Institute of Scientific and Technical Information of China (English)

    朱晓源; 吴健康; 程义民

    2007-01-01

    设计有效的学习算法快速准确地对脑电信号(eelectroencephalogram,EEG)进行连续预测是脑机接口(brain-computer interface,BCI)研究的关键之一.本文提出了一种新颖的基于判别混合高斯模型(discriminative gaussian mixture model,DGMM)的信息积累方法.该方法通过区分度权值对分类器在各时段的输出进行积累,从而达到提高脑电信号分类精度的作用.在两个运动想象数据集上的实验结果表明该方法能够提高BCI系统的性能,具有较好的实用性.

  13. Natural Language Interfaces in Computer Games

    OpenAIRE

    Hobro, Mark; Heine, Marcus

    2015-01-01

    Natural language processing is a complex area of computer science whichhas been under discussion for more than forty years. During recent yearsnatural language interfaces have been established in conjunction withspeech recognition. This report will cover the theory behind naturallanguage processing and evaluate the weaknesses and strengths of implementingand using a natural language interface in a text-based gameenvironment using the Natural Language Toolkit for Python. The resultsshow that t...

  14. TMS communications software. Volume 1: Computer interfaces

    Science.gov (United States)

    Brown, J. S.; Lenker, M. D.

    1979-01-01

    A prototype bus communications system, which is being used to support the Trend Monitoring System (TMS) as well as for evaluation of the bus concept is considered. Hardware and software interfaces to the MODCOMP and NOVA minicomputers are included. The system software required to drive the interfaces in each TMS computer is described. Documentation of other software for bus statistics monitoring and for transferring files across the bus is also included.

  15. 基于稳态视觉诱发电位的脑-机接口研究%An Research on Brain-computer Interfaces Based on the Steady State Visual Evoked Potentials

    Institute of Scientific and Technical Information of China (English)

    郑军

    2011-01-01

    A Steady-State Visual Evoked Potentials ( SSVEP) based Brain-Computer Interfaces system whose stimuli frequency produced by a Liquid Crystal Displays (LED) is achieved. In order to extract the Steady-State Visual Evoked Potentials(SSVEP) , the Fast Fourier Transform ( FFT) and the method based on Mallat wavelet and AR model to offline analysis of the electroencephalogram are used. Analysis results show that these two methods both can extract the SSVEP signal with a high accuracy , and the FFT is more suitable for the brain-computer interface system, so it achieves a online test of the SSVEPBCIs based on FFT.%实现了一个以液晶显示器(LED)产生刺激频率的稳态视觉诱发电位(SSVEP)脑-机接口系统(BCIs).为了从脑电中提取出稳态视觉诱发电位(SSVEP)信号,运用基于快速傅里叶变换(FFT)的方法和基于Mallat小波及AR模型分析法这两种处理方法对脑电信号进行离线分析.实验结果表明,用这两种方法提取SSVEP信号都可以达到很高的准确率;而基于FFT的方法更适用于脑-机接口系统.因此用基于FFT的方法完成了这个SSVEPBCIs的在线实验.

  16. A P300-based brain-computer interface with stimuli on moving objects: four-session single-trial and triple-trial tests with a game-like task design.

    Directory of Open Access Journals (Sweden)

    Ilya P Ganin

    Full Text Available Brain-computer interfaces (BCIs are tools for controlling computers and other devices without using muscular activity, employing user-controlled variations in signals recorded from the user's brain. One of the most efficient noninvasive BCIs is based on the P300 wave of the brain's response to stimuli and is therefore referred to as the P300 BCI. Many modifications of this BCI have been proposed to further improve the BCI's characteristics or to better adapt the BCI to various applications. However, in the original P300 BCI and in all of its modifications, the spatial positions of stimuli were fixed relative to each other, which can impose constraints on designing applications controlled by this BCI. We designed and tested a P300 BCI with stimuli presented on objects that were freely moving on a screen at a speed of 5.4°/s. Healthy participants practiced a game-like task with this BCI in either single-trial or triple-trial mode within four sessions. At each step, the participants were required to select one of nine moving objects. The mean online accuracy of BCI-based selection was 81% in the triple-trial mode and 65% in the single-trial mode. A relatively high P300 amplitude was observed in response to targets in most participants. Self-rated interest in the task was high and stable over the four sessions (the medians in the 1st/4th sessions were 79/84% and 76/71% in the groups practicing in the single-trial and triple-trial modes, respectively. We conclude that the movement of stimulus positions relative to each other may not prevent the efficient use of the P300 BCI by people controlling their gaze, e.g., in robotic devices and in video games.

  17. Time problem of computer interface for ADC

    International Nuclear Information System (INIS)

    The author introduced the data acquisition and computer interface in signal processing of nuclear physics and the time problem in transmitting data to microcomputer, analyzed the rate of data acquisition in interrupt mode and the synchronization between data acquisition and data processing and raised methods for increasing counting rate

  18. Electrostatics with Computer-Interfaced Charge Sensors

    Science.gov (United States)

    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,…

  19. A brain-machine interface instructed by direct intracortical microstimulation

    Directory of Open Access Journals (Sweden)

    Joseph E O'Doherty

    2009-09-01

    Full Text Available Brain-machine interfaces (BMIs establish direct communications 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 two 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.

  20. Spectrometer user interface to computer systems

    International Nuclear Information System (INIS)

    A computer system for use in radiation spectrometry should be designed around the needs and comprehension of the user and his operating environment. To this end, the functions of the system should be built in a modular and independent fashion such that they can be joined to the back end of an appropriate user interface. The point that this interface should be designed rather than just allowed to evolve is illustrated by reference to four related computer systems of differing complexity and function. The physical user interfaces in all cases are keyboard terminals, and the virtues and otherwise of these devices are discussed and compared with others. The language interface needs to satisfy a number of requirements, often conflicting. Among these, simplicity and speed of operation compete with flexibility and scope. Both experienced and novice users need to be considered, and any individual's needs may vary from naive to complex. To be efficient and resilient, the implementation must use an operating system, but the user needs to be protected from its complex and unfamiliar syntax. At the same time the interface must allow the user access to all services appropriate to his needs. The user must also receive an image of privacy in a multi-user system. The interface itself must be stable and exhibit continuity between implementations. Some of these conflicting needs have been overcome by the SABRE interface with languages operating at several levels. The foundation is a simple semimnemonic command language that activates indididual and independent functions. The commands can be used with positional parameters or in an interactive dialogue the precise nature of which depends upon the operating environment and the user's experience. A command procedure or macrolanguage allows combinations of commands with conditional branching and arithmetic features. Thus complex but repetitive operations are easily performed