Last posts on brain-computer interface

XWave: Control your iPhone with your brain

2010-09-20T16:05:00+02:00

The XWave is a new technology that uses a single electrode placed on the wearer’s forehead to measure electroencephalography (EEG) data, and converts these analog signals into digital so they can be used to control an external device. The XWave comes bundled with a software that includes a number of brain-training exercises. These include levitating a ball on the iDevice’s screen, changing a color based on the relaxation level of your brain and training your brain to maximize its attention span.

In the company’s own words:

XWave, powered by NeuroSky eSense patented technologies, senses the faintest electrical impulses transmitted through your skull to the surface of your forehead and converts these analogue signals into digital. With XWave, you will be able to detect attention and meditation levels, as well as train your mind to control things. Objects in a game can be controlled, lights in your living room can change colour depending on your mood; the possibilities are limited to only the power of your imagination.

The interesting feature is that the company is even serving up their APIs so developers can design and develop apps using the XWave device. The company reports that some apps already in development include games in which objects are controlled by the wearer’s mind and another that allows the wearer to control the lights in their home or select music based on their mood. You can order an XWave for $US100; it ships on November 1.

Head Chaise: Couching One's Thoughts into a Brain Wave Sofa

2009-12-20T20:44:32+01:00

From Scientific American

Two European designers, Dries Verbruggen and Lucas Maassen used their alpha waves as a source of inspiration for their design work, which resulted in a piece of furniture, the Brain Wave Couch.

“The process is a wink to a rather futuristic design process,” the couch creators wrote in a press release, “for which a designer merely has to close his or her eyes, or merely rest, to have the brain do all the work, and create the data needed to have the CNC machine cut the shape of the sofa.”

The x-axis of the couch represents Maassen’s brain waves in hertz, while the y-axis shows the amount of alpha activity as a percentage, and the z-axis is the time in milliseconds. Once the foam core of the sofa was completed, the designers covered it by hand in soft gray felt and decorated the valleys of the brain waves with buttons.

The Brainwave Sofa was presented at the Bits ‘n Pieces Exhibition in New York.

Post-doc position Brain-Computer Interface

2009-11-17T19:06:00+01:00

A post-doc position (funded for 3+ years) is available immediately in the field of Brain-Computer Interface/Neural Engineering research.

The successful candidate will be part of the Brain-Computer Interface (BCI) Research and Development Program at the Wadsworth Center in Albany, New York. The research will primarily involve the use of signals recorded from the surface of the brain (electrocorticography (ECoG)) in humans to decode specific aspects of human cognition or behavior from ECoG signals, and to use these signals for communication or control. The goal of this project is to build a system that extracts and uses these decoded signals in real time.

This real-time implementation will be based on our BCI2000 system (http://www.bci2000.org), which has become the standard in the field of BCI research and has already been provided to about 500 laboratories around the world.

Required expertise is a solid background in signal processing, in particular time series/spectral analyses, classification, and machine learning, and excellent programming expertise in Matlab. Please do not apply if you do not have this expertise. Additional desired expertise is a solid understanding of neuroscience, in particular in ECoG signals related to attentional/intentional/motor systems, and programming experience in C/C++.

Applicants should send a CV, a brief statement of background and goals, and two reference letters to Dr. Gerwin Schalk (http://www.wadsworth.org/resnres/bios/schalk.htm) at schalk@wadsworth.org. Review of applications will start immediately and continue until the position is filled.

The BCI program at the Wadsworth Center is recognized world-wide for its EEG-and ECoG-based BCI research. The Wadsworth Center has been named one of the "Best Places to Work for Postdocs" and one of the "Best Places to Work in Academia" by The Scientist magazine.

Contacts:

Gerwin Schalk, Ph.D.
Research Scientist V
Wadsworth Center, NYS Dept. of Health
Dept. of Neurology, Albany Medical College
Dept. of Neurosurgery, Washington Univ. in St. Louis
Dept. of Biomed. Eng., Rensselaer Polytechnic Institute
Dept. of Biomed. Sci., State Univ. of New York at Albany
C650 Empire State Plaza
Albany, New York 12201
phone (518) 486-2559
fax (518) 486-4910
e-mail schalk@wadsworth.org

Natural weelchair control

2009-10-02T13:22:00+02:00

Have a look at this demo of an electric wheelchair under control of an Emotiv EEG/EMG headset. The control system developed by Cuitech, detects when the user winks or smiles, and translates these signals into commands to control the wheelchair.

Thought-controlled wheelchairs

2009-07-06T12:35:16+02:00

Via Sentient Development

The BSI-Toyota Collaboration Center (BTCC) is developing a wheelchair that can be navigated in real-time with brain waves. The brain-controlled device can adjust itself to the characteristics of each individual user, thereby improving the efficiency with which it senses the driver's commands. That way, the driver is able to get the system to learn his/her commands (forward/right/left) quickly and efficiently; the system boasts an accuracy rate of 95%.

Neurofeedback-based motor imagery training for brain-computer interface

2009-06-24T18:48:12+02:00

Neurofeedback-based motor imagery training for brain-computer interface (BCI).

J Neurosci Methods. 2009 Apr 30;179(1):150-6

Authors: Hwang HJ, Kwon K, Im CH

In the present study, we propose a neurofeedback-based motor imagery training system for EEG-based brain-computer interface (BCI). The proposed system can help individuals get the feel of motor imagery by presenting them with real-time brain activation maps on their cortex. Ten healthy participants took part in our experiment, half of whom were trained by the suggested training system and the others did not use any training. All participants in the trained group succeeded in performing motor imagery after a series of trials to activate their motor cortex without any physical movements of their limbs. To confirm the effect of the suggested system, we recorded EEG signals for the trained group around sensorimotor cortex while they were imagining either left or right hand movements according to our experimental design, before and after the motor imagery training. For the control group, we also recorded EEG signals twice without any training sessions. The participants' intentions were then classified using a time-frequency analysis technique, and the results of the trained group showed significant differences in the sensorimotor rhythms between the signals recorded before and after training. Classification accuracy was also enhanced considerably in all participants after motor imagery training, compared to the accuracy before training. On the other hand, the analysis results for the control EEG data set did not show consistent increment in both the number of meaningful time-frequency combinations and the classification accuracy, demonstrating that the suggested system can be used as a tool for training motor imagery tasks in BCI applications. Further, we expect that the motor imagery training system will be useful not only for BCI applications, but for functional brain mapping studies that utilize motor imagery tasks as well.

Neurofeedback-based motor imagery training for brain-computer interface

2009-06-09T19:59:25+02:00

Neurofeedback-based motor imagery training for brain-computer interface (BCI).

J Neurosci Methods. 2009 Apr 30;179(1):150-156

Authors: Hwang HJ, Kwon K, Im CH

In the present study, we propose a neurofeedback-based motor imagery training system for EEG-based brain-computer interface (BCI). The proposed system can help individuals get the feel of motor imagery by presenting them with real-time brain activation maps on their cortex. Ten healthy participants took part in our experiment, half of whom were trained by the suggested training system and the others did not use any training. All participants in the trained group succeeded in performing motor imagery after a series of trials to activate their motor cortex without any physical movements of their limbs. To confirm the effect of the suggested system, we recorded EEG signals for the trained group around sensorimotor cortex while they were imagining either left or right hand movements according to our experimental design, before and after the motor imagery training. For the control group, we also recorded EEG signals twice without any training sessions. The participants' intentions were then classified using a time-frequency analysis technique, and the results of the trained group showed significant differences in the sensorimotor rhythms between the signals recorded before and after training. Classification accuracy was also enhanced considerably in all participants after motor imagery training, compared to the accuracy before training. On the other hand, the analysis results for the control EEG data set did not show consistent increment in both the number of meaningful time-frequency combinations and the classification accuracy, demonstrating that the suggested system can be used as a tool for training motor imagery tasks in BCI applications. Further, we expect that the motor imagery training system will be useful not only for BCI applications, but for functional brain mapping studies that utilize motor imagery tasks as well.

4th XVR Workshop & Joint PRESENCCIA and SKILLS PhD Symposium

2009-05-25T13:32:00+02:00

PRESENCCIA and SKILLS are two integrated projects that both aim to advance Virtual Reality technology. These projects are highly interdisciplinary encompassing, among others, computer science, robotics, engineering, interaction design, cognitive science, neuroscience, psychology and philosophy. All these fields, however diverse their interests, come together in the goal of integrating human interaction in mixed and virtual reality environments in order to enhance the user’s experience and enabling him to act and interact in a natural and familiar way by means of enactive paradigms.

The most interesting, challenging and useful digital environments are social, focussing on supporting (group) interaction between real people and other remote people or real people and virtual people. The aim is to understand, track and give appropriate feedback in verbal, non-verbal and implicit interactions while also making digital content more believable and intelligent.

Likewise, a number of methods need to be developed allowing users of virtual environments to not only perform actions effectively in a variety of different scenarios but also be able to choose from a repertoire of suitable actions. This requires adequate digital representations of human skills and also techniques to capture, interpret and deliver them by means of multimodal interfaces, robotics and virtual environments within enactive interaction paradigms.

At the low-level end of the spectrum we also aim to understand the neural basis of presence and its response. Its enhancement and its application is the fundamental object of study from many different points of view, and including visual, haptic and auditory modalities.

To participate to the Workshop, please register on line:
http://www.percro.org/registrationXVR2009/

Keynote Speakers

Salvatore Maria Aglioti, Psychology Department , Università di Roma "La Sapienza” and IRCCS Fondazione Santa Lucia, Roma (http://w3.uniroma1.it/aglioti/) ù

Flesh made Soul: Bodies in the Brain.

Talking about the body implies talking about the very “special object” that allows a deep interconnection between the ability to have self-consciousness and the ability to experience a world of objects. My talk will be based on the studies in healthy and brain damaged subjects we performed in the past fifteen years on the neural representation of the body. I will put forward the idea that, although trivially made of flesh, blood and bones, the body can be considered the “psychic object” par excellence, which mediates and implements a variety of complex functions, ranging from the notion of self to social interactions and negotiations.

Jan Peters, Dept. Empirical Inference, Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany (http://www-clmc.usc.edu/~jrpeters/)

Towards Motor Skill Learning in Robotics.

Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence,
and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this talk, we investigate a general framework suitable for learning motor skills in robotics which is based on the principles behind many analytical robotics approaches. We propose new, task-appropriate architectures, such as the Natural Actor-Critic and the PoWER algorithm.
and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this talk, we investigate a general framework suitable for learning motor skills in robotics which is based on the principles behind many analytical robotics approaches. We propose new, task-appropriate architectures, such as the Natural Actor-Critic and the PoWER algorithm.

Improving the performance of brain-computer interface through meditation

2009-02-16T22:57:28+01:00

Improving the performance of brain-computer interface through meditation practicing.

Conf Proc IEEE Eng Med Biol Soc. 2008;1:662-5

Authors: Eskandari P, Erfanian A

Cognitive tasks using motor imagery have been used for generating and controlling EEG activity in most brain-computer interface (BCI). Nevertheless, during the performance of a particular mental task, different factors such as concentration, attention, level of consciousness and the difficulty of the task, may be affecting the changes in the EEG activity. Accordingly, training the subject to consistently and reliably produce and control the changes in the EEG signals is a critical issue in developing a BCI system. In this work, we used meditation practice to enhance the mind controllability during the performance of a mental task in a BCI system. The mental states to be discriminated are the imaginative hand movement and the idle state. The experiments were conducted on two groups of subject, meditation group and control group. The time-frequency analysis of EEG signals for meditation practitioners showed an event-related desynchronization (ERD) of beta rhythm before imagination during resting state. In addition, a strong event-related synchronization (ERS) of beta rhythm was induced in frequency around 25 Hz during hand motor imagery. The results demonstrated that the meditation practice can improve the classification accuracy of EEG patterns. The average classification accuracy was 88.73% in the meditation group, while it was 70.28% in the control group. An accuracy as high as 98.0% was achieved in the meditation group.

Functional network reorganization during learning in a brain-computer interface paradigm

2009-01-20T06:00:25+01:00

Functional network reorganization during learning in a brain-computer interface paradigm.

Proc Natl Acad Sci U S A. 2008 Dec 1;

Authors: Jarosiewicz B, Chase SM, Fraser GW, Velliste M, Kass RE, Schwartz AB

Efforts to study the neural correlates of learning are hampered by the size of the network in which learning occurs. To understand the importance of learning-related changes in a network of neurons, it is necessary to understand how the network acts as a whole to generate behavior. Here we introduce a paradigm in which the output of a cortical network can be perturbed directly and the neural basis of the compensatory changes studied in detail. Using a brain-computer interface, dozens of simultaneously recorded neurons in the motor cortex of awake, behaving monkeys are used to control the movement of a cursor in a three-dimensional virtual-reality environment. This device creates a precise, well-defined mapping between the firing of the recorded neurons and an expressed behavior (cursor movement). In a series of experiments, we force the animal to relearn the association between neural firing and cursor movement in a subset of neurons and assess how the network changes to compensate. We find that changes in neural activity reflect not only an alteration of behavioral strategy but also the relative contributions of individual neurons to the population error signal.

Brain Controlled Cell Phones

2008-11-04T22:09:00+01:00

Via Textually.org

NeuroSky Inc, a venture company based in San Jose, Calif, prototyped a system that reads brain waves with a sensor and uses them for mobile phone applications.

Software algorithms try to deduce from your brainwaves what you are thinking and pass on the appropriate commands to the cell phone.

New BCI system for gaming applications

2008-07-08T18:41:42+02:00

Emotiv Systems has developed a new brain computer interface headset for video games and other uses. Emotiv’s president Tan Le claims that the headset will be on sale around the end of this year ($299).

Using brain-computer communication to navigate virtual environments

2008-01-23T18:55:43+01:00

Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment.

IEEE Trans Neural Syst Rehabil Eng. 2007 Dec;15(4):473-82

Authors: Leeb R, Lee F, Keinrath C, Scherer R, Bischof H, Pfurtscheller G

The step away from a synchronized or cue-based brain-computer interface (BCI) and from laboratory conditions towards real world applications is very important and crucial in BCI research. This work shows that ten naive subjects can be trained in a synchronous paradigm within three sessions to navigate freely through a virtual apartment, whereby at every junction the subjects could decide by their own, how they wanted to explore the virtual environment (VE). This virtual apartment was designed similar to a real world application, with a goal-oriented task, a high mental workload, and a variable decision period for the subject. All subjects were able to perform long and stable motor imagery over a minimum time of 2 s. Using only three electroencephalogram (EEG) channels to analyze these imaginations, we were able to convert them into navigation commands. Additionally, it could be demonstrated that motivation is a very crucial factor in BCI research; motivated subjects perform much better than unmotivated ones.

Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment

2008-01-09T22:45:00+01:00

Neural Systems and Rehabilitation Engineering, IEEE Transactions on [see also IEEE Trans. on Rehabilitation Engineering]

Leeb, R. Lee, F. Keinrath, C. Scherer, R. Bischof, H. Pfurtscheller, G.

Publication Date: Dec. 2007
Volume: 15, Issue: 4
On page(s): 473-482
ISSN: 1534-4320

The step away from a synchronized or cue-based brain–computer interface (BCI) and from laboratory conditions towards real world applications is very important and crucial in BCI research. This work shows that ten naive subjects can be trained in a synchronous paradigm within three sessions to navigate freely through a virtual apartment, whereby at every junction the subjects could decide by their own, how they wanted to explore the virtual environment (VE). This virtual apartment was designed similar to a real world application, with a goal-oriented task, a high mental workload, and a variable decision period for the subject. All subjects were able to perform long and stable motor imagery over a minimum time of 2 s. Using only three electroencephalogram (EEG) channels to analyze these imaginations, we were able to convert them into navigation commands. Additionally, it could be demonstrated that motivation is a very crucial factor in BCI research; motivated subjects perform much better than unmotivated ones.

Towards an independent brain-computer interface using steady state visual evoked potentials

2007-12-22T18:36:35+01:00

Towards an independent brain-computer interface using steady state visual evoked potentials.

Clin Neurophysiol. 2007 Dec 10;

Authors: Allison BZ, McFarland DJ, Schalk G, Zheng SD, Jackson MM, Wolpaw JR

OBJECTIVE: Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze. METHODS: Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs. RESULTS: Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control. CONCLUSIONS: The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user. SIGNIFICANCE: SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

Towards an independent brain-computer interface using steady state visual evoked potentials

2007-12-16T14:30:18+01:00

Towards an independent brain-computer interface using steady state visual evoked potentials.

Clin Neurophysiol. 2007 Dec 10;

Authors: Allison BZ, McFarland DJ, Schalk G, Zheng SD, Jackson MM, Wolpaw JR

Self-initiation of EEG-based brain-computer communication using the heart rate response

2007-12-08T19:10:07+01:00

Self-initiation of EEG-based brain-computer communication using the heart rate response.

J Neural Eng. 2007 Dec;4(4):L23-9

Authors: Scherer R, Müller-Putz GR, Pfurtscheller G

Self-initiation, that is the ability of a brain-computer interface (BCI) user to autonomously switch on and off the system, is a very important issue. In this work we analyze whether the respiratory heart rate response, induced by brisk inspiration, can be used as an additional communication channel. After only 20 min of feedback training, ten healthy subjects were able to self-initiate and operate a 4-class steady-state visual evoked potential-based (SSVEP) BCI by using one bipolar ECG and one bipolar EEG channel only. Threshold detection was used to measure a beat-to-beat heart rate increase. Despite this simple method, during a 30 min evaluation period on average only 2.9 non-intentional switches (heart rate changes) were detected.

Brain2Robot

2007-11-25T23:50:00+01:00

Researchers at the Fraunhofer Institute for Computer Architecture and Software Technology FIRST and the Charite hospital in Berlin have developed a new EEG-controlled robot arm, which might one day bring help to people with paralysis.

Electrodes attached to the patient's scalp measure the brain's electrical signals, which are amplified and transmitted to a computer. Highly efficient algorithms analyze these signals using a self-learning technique. The software is capable of detecting changes in brain activity that take place even before a movement is carried out. It can recognize and distinguish between the patterns of signals that correspond to an intention to raise the left or right hand, and extract them from the pulses being fired by millions of other neurons in the brain. These neural signal patterns are then converted into control instructions for the computer. "The aim of the project is to help people with severe motor disabilities to carry out everyday tasks. The advantage of our technology is that it is capable of translating an intended action directly into instructions for the computer," says team leader Florin Popescu. The Brain2Robot project has been granted around 1.3 million euros in research funding under the EU's sixth Framework Programme (FP6). Its focus lies on developing medical applications, in particular control systems for prosthetics, personal robots and wheelchairs. The researchers have also developed a "thought-controlled typewriter", a communication device that enables severely paralyzed patients to pick out letters of the alphabet and write texts. The robot arm could be ready for commercialization in just a few years' time.

Press release:Brain2Robot

Project page:Brain2Robot

Virtual reality hardware and graphic display options for brain-machine interfaces

2007-11-18T22:59:33+01:00

Virtual reality hardware and graphic display options for brain-machine interfaces.

J Neurosci Methods. 2007 Sep 29;

Authors: Marathe AR, Carey HL, Taylor DM

Virtual reality hardware and graphic displays are reviewed here as a development environment for brain-machine interfaces (BMIs). Two desktop stereoscopic monitors and one 2D monitor were compared in a visual depth discrimination task and in a 3D target-matching task where able-bodied individuals used actual hand movements to match a virtual hand to different target hands. Three graphic representations of the hand were compared: a plain sphere, a sphere attached to the fingertip of a realistic hand and arm, and a stylized pacman-like hand. Several subjects had great difficulty using either stereo monitor for depth perception when perspective size cues were removed. A mismatch in stereo and size cues generated inappropriate depth illusions. This phenomenon has implications for choosing target and virtual hand sizes in BMI experiments. Target-matching accuracy was about as good with the 2D monitor as with either 3D monitor. However, users achieved this accuracy by exploring the boundaries of the hand in the target with carefully controlled movements. This method of determining relative depth may not be possible in BMI experiments if movement control is more limited. Intuitive depth cues, such as including a virtual arm, can significantly improve depth perception accuracy with or without stereo viewing.

A Low Cost Human Computer Interface based on Eye Tracking

2007-10-20T17:36:40+02:00

A Low Cost Human Computer Interface based on Eye Tracking.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:3226-3229

Authors: Hiley JB, Redekopp AH, Fazel-Rezai R

This paper describes the implementation of a human computer interface based on eye tracking. Current commercially available systems exist, but have limited use due mainly to their large cost. The system described in this paper was designed to be a low cost and unobtrusive. The technique was video-oculography assisted by corneal reflections. An off-the shelf CCD webcam was used to capture images. The images were analyzed in software to extract key features of the eye. The users gaze point was then calculated based on the relative position of these features. The system is capable of calculating eye-gaze in real-time to provide a responsive interaction. A throughput of eight gaze points per second was achieved. The accuracy of the fixations based on the calculated eye-gazes were within 1 cm of the on-screen gaze location. By developing a low-cost system, this technology is made accessible to a wider range of applications.

A brain-computer interface with vibrotactile biofeedback for haptic information

2007-10-20T16:47:24+02:00

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

J Neuroengineering Rehabil. 2007 Oct 17;4(1):40

Authors: Chatterjee A, Aggarwal V, Ramos A, Acharya S, Thakor NV

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

Microsoft Mind Reader

2007-10-20T16:44:07+02:00

Via NewScientist Tech

Microsoft plans to use EEG signals for task classification and activity recognition of users. The software giant has applied a new patent for a method that will allow to separate useful cognitive information from EEG artifacts and noise.

Read the full Microsoft mind reading patent application

Brain-computer interface for Second Life

2007-10-12T18:04:27+02:00

Great catch by Pink Tentacle: researchers at Keio University Biomedical Engineering Laboratory have developed a brain-computer interface that allows the user controlling his avatar in Second Life by thinking about movements — the avatar walks forward when the user thinks about moving his/her own feet, and it turns right and left when the user imagines moving his/her right and left arms. A future goal is to improve the system and make Second Life avatars perform more complex movements and gestures.

video (14,9 MB)

A low cost human computer interface based on eye tracking

2007-09-20T21:48:22+02:00

A Low Cost Human Computer Interface based on Eye Tracking.

Conf Proc IEEE Eng Med Biol Soc. 2006;1(1):3226-3229

Authors: Hiley JB, Redekopp AH, Fazel-Rezai R

Motorized wheelchair guided by thoughts

2007-09-07T17:55:00+02:00

Via NewScientist.com

US company Ambient has unveiled a motorized wheelchair that moves when the operator thinks of particular words. The wheelchair works by intercepting signals sent from their brain to their voice box, even when no sound is actually produced.

The wheelchair was developed in collaboration with the Rehabilitation Institute of Chicago. It could help people with spinal injuries, or neurological problems like cerebral palsy or motor neuron disease, operate computers and other equipment despite serious problems with muscle control.

Read Original Article

Brain-computer interface: a reciprocal self-regulated neuromodulation

2007-09-05T18:11:43+02:00

Brain-computer interface: a reciprocal self-regulated neuromodulation.

Acta Neurochir Suppl. 2007;97(Pt 2):555-9

Authors: Angelakis E, Hatzis A, Panourias IG, Sakas DE

Brain-computer interface (BCI) is a system that records brain activity and process it through a computer, allowing the individual whose activity is recorded to monitor this activity at the same time. Applications of BCIs include assistive modules for severely paralyzed patients to help them control external devices or to communicate, as well as brain biofeedback to self regulate brain activity for treating epilepsy, attention-deficit hyperactivity disorder (ADHD), anxiety, and other psychiatric conditions, or to enhance cognitive performance in healthy individuals. The vast majority of BCIs utilizes non-invasive scalp recorded electroencephalographic (EEG) signals, but other techniques like invasive intracortical EEG, or near-infrared spectroscopy measuring brain blood oxygenation are tried experimentally.

An MEG-based brain-computer interface (BCI)

2007-08-07T18:50:00+02:00

An MEG-based brain-computer interface (BCI).

Neuroimage. 2007 Jul 1;36(3):581-93

Authors: Mellinger J, Schalk G, Braun C, Preissl H, Rosenstiel W, Birbaumer N, Kübler A

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

EEG-based assessment of driver cognitive responses in a dynamic virtual-reality driving environment

2007-07-06T16:05:45+02:00

EEG-based assessment of driver cognitive responses in a dynamic virtual-reality driving environment.

IEEE Trans Biomed Eng. 2007 Jul;54(7):1349-52

Authors: Lin CT, Chung IF, Ko LW, Chen YC, Liang SF, Duann JR

Accidents caused by errors and failures in human performance among traffic fatalities have a high death rate and become an important issue in public security. They are mainly caused by the failures of the drivers to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for assessing driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. The VR technique allows subjects to interact directly with the moving virtual environment instead of monotonic auditory and visual stimuli, thereby provides interactive and realistic tasks without the risk of operating on an actual machine. Independent component analysis (ICA) is used to separate and extract noise-free ERP signals from the multi-channel EEG signals. A temporal filter is used to solve the time-alignment problem of ERP features and principle component analysis (PCA) is used to reduce feature dimensions. The dimension-reduced features are then input to a self-constructing neural fuzzy inference network (SONFIN) to recognize different brain potentials stimulated by red/green/yellow traffic events, the accuracy can be reached 87% in average eight subjects in this visual-stimuli ERP experiment. It demonstrates the feasibility of detecting and analyzing multiple streams of ERP signals that represent operators' cognitive states and responses to task events.

Brain-computer interface systems: progress and prospects

2007-07-06T15:20:42+02:00

Brain-computer interface systems: progress and prospects.

Expert Rev Med Devices. 2007 Jul;4(4):463-474

Authors: Allison BZ, Wolpaw EW, Wolpaw JR

Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.

Pentagon to Merge Next-Gen Binoculars With Soldiers' Brains

2007-07-03T22:09:25+02:00

Via Networked Performance

From Pentagon to Merge Next-Gen Binoculars With Soldiers' Brains by Sharon Weinberger, Wired:

"U.S. Special Forces may soon have a strange and powerful new weapon in their arsenal: a pair of high-tech binoculars 10 times more powerful than anything available today, augmented by an alerting system that literally taps the wearer's prefrontal cortex to warn of furtive threats detected by the soldier's subconscious.

In a new effort dubbed "Luke's Binoculars" -- after the high-tech binoculars Luke Skywalker uses in Star Wars -- the Defense Advanced Research Projects Agency is setting out to create its own version of this science-fiction hardware. And while the Pentagon's R&D arm often focuses on technologies 20 years out, this new effort is dramatically different -- Darpa says it expects to have prototypes in the hands of soldiers in three years...

The most far-reaching component of the binocs has nothing to do with the optics: it's Darpa's aspirations to integrate EEG electrodes that monitor the wearer's neural signals, cueing soldiers to recognize targets faster than the unaided brain could on its own. The idea is that EEG can spot "neural signatures" for target detection before the conscious mind becomes aware of a potential threat or target."

A MEG-based brain-computer interface

2007-05-07T21:50:20+02:00

A MEG-based brain-computer interface (BCI).

Neuroimage. 2007 Mar 27;

Authors: Mellinger J, Schalk G, Braun C, Preissl H, Rosenstiel W, Birbaumer N, Kübler A

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

2007-04-22T22:31:23+02:00

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

J Neurosci Methods. 2007 Mar 13;

Authors: Hoffmann U, Vesin JM, Ebrahimi T, Diserens K

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 disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed.

Cortical current density estimation for the classification of motor imagery

2007-04-11T23:41:46+02:00

Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy.

J Neural Eng. 2007 Jun;4(2):17-25

Authors: Kamousi B, Amini AN, He B

The goal of the present study is to employ the source imaging methods such as cortical current density estimation for the classification of left- and right-hand motor imagery tasks, which may be used for brain-computer interface (BCI) applications. The scalp recorded EEG was first preprocessed by surface Laplacian filtering, time-frequency filtering, noise normalization and independent component analysis. Then the cortical imaging technique was used to solve the EEG inverse problem. Cortical current density distributions of left and right trials were classified from each other by exploiting the concept of Von Neumann entropy. The proposed method was tested on three human subjects (180 trials each) and a maximum accuracy of 91.5% and an average accuracy of 88% were obtained. The present results confirm the hypothesis that source analysis methods may improve accuracy for classification of motor imagery tasks. The present promising results using source analysis for classification of motor imagery enhances our ability of performing source analysis from single trial EEG data recorded on the scalp, and may have applications to improved BCI systems.

Brain-controlled devices and games

2007-04-01T20:53:27+02:00

Via pasta & vinegar

An article in The Economist about brain-controlled devices and games.

From the article:

At the moment, EEG's uses are mostly medical. Though the output of the electrodes is a set of crude brain waves, enough is now known about the healthy patterns of these waves for changes in them to be used to diagnose unhealthy abnormalities. Yet, because parts of a person's grey matter exhibit increased electric activity when they respond to stimuli or prepare for movements, there has always been the lingering hope that EEG might also manifest someone's thoughts in a machine-readable form that could be used for everyday purposes.

To realise that hope means solving two problems—one of hardware and one of software. The hardware problem is that existing EEG requires a helmet with as many as 120 electrodes in it, and that these electrodes have to be affixed to the scalp with a gel. The software problem is that many different types of brain waves have to be interpreted simultaneously and instantly. That is no mean computing task.

Electrocorticographically controlled brain-computer interfaces

2007-04-01T20:34:08+02:00

Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases.

J Neurosurg. 2007 Mar;106(3):495-500

Authors: Felton EA, Wilson JA, Williams JC, Garell PC

Brain-computer interface (BCI) technology can offer individuals with severe motor disabilities greater independence and a higher quality of life. The BCI systems take recorded brain signals and translate them into real-time actions, for improved communication, movement, or perception. Four patient participants with a clinical need for intracranial electrocorticography (ECoG) participated in this study. The participants were trained over multiple sessions to use motor and/or auditory imagery to modulate their brain signals in order to control the movement of a computer cursor. Participants with electrodes over motor and/or sensory areas were able to achieve cursor control over 2 to 7 days of training. These findings indicate that sensory and other brain areas not previously considered ideal for ECoG-based control can provide additional channels of control that may be useful for a motor BCI.

Pigeonbots

2007-03-17T14:51:12+01:00

From Practical Neurotechnology

Scientists at the Robot Engineering Technology Research Center of east China's Shandong University of Science and Technology claim to have implanted micro electrodes in the brain of a pigeon so they can command it to fly right or left or up or down.

The implants stimulated different areas of the pigeon's brain according to signals sent by the scientists via computer, and forced the bird to comply with their commands.

http://blog.wired.com/defense/2007/02/cyborg_flying_r.html
http://tenementpalm.blogspot.com/2007/02/psb-buys-tiny-german-spy-copters.html
http://english.people.com.cn/200702/27/eng20070227_352761.html

BCI for communication and motor control

2007-03-10T20:22:16+01:00

Breaking the silence: brain-computer interfaces (BCI) for communication and motor control.

Psychophysiology. 2006 Nov;43(6):517-32

Authors: Birbaumer N

Brain-computer interfaces (BCI) allow control of computers or external devices with regulation of brain activity alone. Invasive BCIs, almost exclusively investigated in animal models using implanted electrodes in brain tissue, and noninvasive BCIs using electrophysiological recordings in humans are described. Clinical applications were reserved with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in syndrome with slow cortical potentials, sensorimotor rhythm and P300, and restoration of movement and cortical reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs allow brain-derived communication in paralyzed and locked-in patients but not in completely locked-in patients. At present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made. Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force variations based on spike patterns and extracellular field potentials. The newly developed fMRI-BCIs and NIRS-BCIs, like EEG BCIs, offer promise for the learned regulation of emotional disorders and also disorders of young children.

A Wheelchair That Reads Your Mind

2007-01-29T16:46:03+01:00

From Wired

Spanish scientists are building a robotic wheelchair controlled by thought, so even completely immobile patients can have some freedom of movement...

read the full story on Wired

Volitional control of neural activity: implications for BCIs

2007-01-22T21:45:00+01:00

Volitional control of neural activity: implications for brain-computer interfaces.

J Physiol. 2007 Jan 18;

Authors: Fetz EE

Successful operation of brain-computer interfaces [BCI] and brain-machine interfaces [BMI] depends significantly on the degree to which neural activity can be volitionally controlled. This paper reviews evidence for such volitional control in a variety of neural signals, with particular emphasis on the activity of cortical neurons. Some evidence comes from conventional experiments that reveal volitional modulation in neural activity related to behaviors, including real and imagined movements, cognitive imagery and shifts of attention. More direct evidence comes from studies on operant conditioning of neural activity using biofeedback, and from BCI/BMI studies in which neural activity controls cursors or peripheral devices. Limits in the degree of accuracy of control in the latter studies can be attributed to several possible factors. Some of these factors, particularly limited practice time, can be addressed with long-term implanted BCIs. Preliminary observations with implanted circuits implementing recurrent BCIs are summarized.

BCI: Communication and Restoration of Movement in Paralysis

2007-01-22T00:07:34+01:00

Brain-Computer-Interfaces(BCI): Communication and Restoration of Movement in Paralysis.

J Physiol. 2007 Jan 18;

Authors: Birbaumer N, Cohen LG

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

The University of Washington Neural Systems Lab have created a humanoid robot you can control with your thoughts.

2007-01-15T20:52:18+01:00

Via Mind Hacks

Researchers at the University of Washington Neural Systems Lab have created a humanoid robot you can control with your thoughts via a EEG-based non-invasive brain-computer interface.

Link to Neural Systems Lab robot info page

EMG and EOG artifacts in brain computer interface systems

2006-12-18T18:08:35+01:00

EMG and EOG artifacts in brain computer interface systems: A survey.

Clin Neurophysiol. 2006 Dec 12;

Authors: Fatourechi M, Bashashati A, Ward RK, Birch GE

It is widely accepted in the brain computer interface (BCI) research community that neurological phenomena are the only source of control in any BCI system. Artifacts are undesirable signals that can interfere with neurological phenomena. They may change the characteristics of neurological phenomena or even be mistakenly used as the source of control in BCI systems. Electrooculography (EOG) and electromyography (EMG) artifacts are considered among the most important sources of physiological artifacts in BCI systems. Currently, however, there is no comprehensive review of EMG and EOG artifacts in BCI literature. This paper reviews EOG and EMG artifacts associated with BCI systems and the current methods for dealing with them. More than 250 refereed journal and conference papers are reviewed and categorized based on the type of neurological phenomenon used and the methods employed for handling EOG and EMG artifacts. This study reveals weaknesses in BCI studies related to reporting the methods of handling EMG and EOG artifacts. Most BCI papers do not report whether or not they have considered the presence of EMG and EOG artifacts in the brain signals. Only a small percentage of BCI papers report automated methods for rejection or removal of artifacts in their systems. As the lack of dealing with artifacts may result in the deterioration of the performance of a particular BCI system during practical applications, it is necessary to develop automatic methods to handle artifacts or to design BCI systems whose performance is robust to the presence of artifacts.

Researchers demonstrate EEG control of humanoid robot

2006-12-18T17:32:00+01:00

Via ScienceDaily

University of Washington researchers have developed a brain-computer interface that allows humans to control the actions of robots through commands generated by analysis of EEG signals

Link

Online control of a brain-computer interface using phase synchronization

2006-12-15T00:19:43+01:00

Online control of a brain-computer interface using phase synchronization.

IEEE Trans Biomed Eng. 2006 Dec;53(12 Pt 1):2501-6

Authors: Brunner C, Scherer R, Graimann B, Supp G, Pfurtscheller G

Currently, almost all brain-computer interfaces (BCIs) ignore the relationship between phases of electroencephalographic signals detected from different recording sites (i.e., electrodes). The vast majority of BCI systems rely on feature vectors derived from e.g., bandpower or univariate adaptive autoregressive (AAR) parameters. However, ample evidence suggests that additional information is obtained by quantifying the relationship between signals of single electrodes, which might provide innovative features for future BCI systems. This paper investigates one method to extract the degree of phase synchronization between two electroencephalogram (EEG) signals by calculating the so-called phase locking value (PLV). In our offline study, several PLV-based features were acquired and the optimal feature set was selected for each subject individually by a feature selection algorithm. The online sessions with three trained subjects revealed that all subjects were able to control three mental states (motor imagery of left hand, right hand, and foot, respectively) with single-trial accuracies between 60% and 66.7% (33% would be expected by chance) throughout the whole session.

Brain-computer interface technology as a tool to augment plasticity

2006-11-29T22:35:28+01:00

Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation.

J Physiol. 2006 Nov 16;

Authors: Dobkin BH

Brain-computer interfaces are a rehabilitation tool for tetraplegic patients that aim to improve quality of life by augmenting communication, control of the environment, and self-care. The neurobiology of both rehabilitation and BCI control depends upon learning to modify the efficacy of spared neural ensembles that represent movement, sensation, and cognition through progressive practice with feedback and reward. To serve patients, BCI systems must become safe, reliable, cosmetically acceptable, quickly mastered with minimal ongoing technical support, and highly accurate even in the face of mental distractions and the uncontrolled environment beyond a laboratory. BCI technologies may raise ethical concerns if their availability affects the decisions of patients who become locked-in with brain stem stroke or amyotrophic lateral sclerosis to be sustained with ventilator support. If BCI technology becomes flexible and affordable, volitional control of cortical signals could be employed for the rehabilitation of motor and cognitive impairments in hemiplegic or paraplegic patients by offering on-line feedback about cortical activity associated with mental practice, motor intention, and other neural recruitment strategies during progressive task-oriented practice. Clinical trials with measures of quality of life will be necessary to demonstrate the value of near-term and future BCI applications.

Model train controlled via brain-machine interface

2006-11-29T22:24:07+01:00

Re-blogged from Pink Tentacle

Hitachi has successfully tested a brain-machine interface that allows users to turn power switches on and off with their mind. Relying on optical topography, a neuroimaging technique that uses near-infrared light to map blood concentration in the brain, the system can recognize the changes in brain blood flow associated with mental activity and translate those changes into voltage signals for controlling external devices. In the experiments, test subjects were able to activate the power switch of a model train by performing mental arithmetic and reciting items from memory.

Link to Pink Tentacle post

Brain computer interface covered by Nature

2006-11-21T17:00:59+01:00

Via Neurobot

Brain-machine interfaces promise to aid paralyzed patients by re-routing movement-related signals around damaged parts of the nervous system.

a web focus by Nature

BCI as a tool to induce neuroplasticity

2006-11-11T16:03:48+01:00

Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation.

J Physiol. 2006 Nov 9

Authors: Dobkin BH

Brain-computer interfaces are a rehabilitation tool for tetraplegic patients that aim to improve quality of life by augmenting communication, control of the environment, and self-care. The neurobiology of both rehabilitation and BCI control depends upon learning to modify the efficacy of spared neural ensembles that represent movement, sensation, and cognition through progressive practice with feedback and reward. To serve patients, BCI systems must become safe, reliable, cosmetically acceptable, quickly mastered with minimal ongoing technical support, and highly accurate even in the face of mental distractions and the uncontrolled environment beyond a laboratory. BCI technologies may raise ethical concerns if their availability affects the decisions of patients who become locked-in with brain stem stroke or amyotrophic lateral sclerosis to be sustained with ventilator support. If BCI technology becomes flexible and affordable, volitional control of cortical signals could be employed for the rehabilitation of motor and cognitive impairments in hemiplegic or paraplegic patients by offering on-line feedback about cortical activity associated with mental practice, motor intention, and other neural recruitment strategies during progressive task-oriented practice. Clinical trials with measures of quality of life will be necessary to demonstrate the value of near-term and future BCI applications.

An extended EM algorithm for joint feature extraction and classification in BCI

2006-11-05T21:54:57+01:00

An extended EM algorithm for joint feature extraction and classification in brain-computer interfaces.

Neural Comput. 2006 Nov;18(11):2730-61

Authors: Li Y, Guan C

For many electroencephalogram (EEG)-based brain-computer interfaces (BCIs), a tedious and time-consuming training process is needed to set parameters. In BCI Competition 2005, reducing the training process was explicitly proposed as a task. Furthermore, an effective BCI system needs to be adaptive to dynamic variations of brain signals; that is, its parameters need to be adjusted online. In this article, we introduce an extended expectation maximization (EM) algorithm, where the extraction and classification of common spatial pattern (CSP) features are performed jointly and iteratively. In each iteration, the training data set is updated using all or part of the test data and the labels predicted in the previous iteration. Based on the updated training data set, the CSP features are reextracted and classified using a standard EM algorithm. Since the training data set is updated frequently, the initial training data set can be small (semi-supervised case) or null (unsupervised case). During the above iterations, the parameters of the Bayes classifier and the CSP transformation matrix are also updated concurrently. In online situations, we can still run the training process to adjust the system parameters using unlabeled data while a subject is using the BCI system. The effectiveness of the algorithm depends on the robustness of CSP feature to noise and iteration convergence, which are discussed in this article. Our proposed approach has been applied to data set IVa of BCI Competition 2005. The data analysis results show that we can obtain satisfying prediction accuracy using our algorithm in the semisupervised and unsupervised cases. The convergence of the algorithm and robustness of CSP feature are also demonstrated in our data analysis.

Neural internet: web surfing with brain potentials

2006-11-05T21:53:28+01:00

Neural internet: web surfing with brain potentials for the completely paralyzed.

Neurorehabil Neural Repair. 2006 Dec;20(4):508-15

Authors: Karim AA, Hinterberger T, Richter J, Mellinger J, Neumann N, Flor H, Kübler A, Birbaumer N

Neural Internet is a new technological advancement in brain-computer interface research, which enables locked-in patients to operate a Web browser directly with their brain potentials. Neural Internet was successfully tested with a locked-in patient diagnosed with amyotrophic lateral sclerosis rendering him the first paralyzed person to surf the Internet solely by regulating his electrical brain activity. The functioning of Neural Internet and its clinical implications for motor-impaired patients are highlighted.