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May 02, 2006

Cardiac responses induced during thought-based control of a virtual environment

Cardiac responses induced during thought-based control of a virtual environment.

Int J Psychophysiol. 2006 Apr 25;

Authors: Pfurtscheller G, Leeb R, Slater M

Cardiac responses induced by motor imagery were investigated in 3 subjects in a series of experiments with a synchronous (cue-based) Brain-Computer Interface (BCI). The cue specified right hand vs. leg/foot motor imagery. After a number of BCI training sessions reaching a classification accuracy of at least 80%, the BCI experiments were carried out in an immersive virtual environment (VE), commonly referred as a "CAVE". In this VE, the subjects were able to move along a virtual street by motor imagery alone. The thought-based control of VE resulted in an acceleration of the heart rate in 2 subjects and a heart rate deceleration in the other subject. In control experiments in front of a PC, all 3 subjects displayed a significant heart rate deceleration of the order of about 3-5%. This heart rate decrease during motor imagery in a normal environment is similar to that observed during preparation for a voluntary movement. The heart rate acceleration in the VE is interpreted as effect of an increased mental effort to walk as far as possible in VE.

Steady-state somatosensory evoked potentials

Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces?

IEEE Trans Neural Syst Rehabil Eng. 2006 Mar;14(1):30-7

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

One of the main issues in designing a brain-computer interface (BCI) is to find brain patterns, which could easily be detected. One of these pattern is the steady-state evoked potential (SSEP). SSEPs induced through the visual sense have already been used for brain-computer communication. In this work, a BCI system is introduced based on steady-state somatosensory evoked potentials (SSSEPs). Transducers have been used for the stimulation of both index fingers using tactile stimulation in the "resonance"-like frequency range of the somatosensory system. Four subjects participated in the experiments and were trained to modulate induced SSSEPs. Two of them learned to modify the patterns in order to set up a BCI with an accuracy of between 70% and 80%. Results presented in this work give evidence that it is possible to set up a BCI which is based on SSSEPs.

Playing pong with thought

(Via VRoot)


This video shows people playing pong with thought, just by thinking of moving square.

Computer game technology will plug directly into our brains

Via the Presence Mailing List

(from Australia's The Age)

The new PlayStation or X-box may not need a joystick, with the Federal Government approving the development of Australian computer game technology that plugs directly into our brains.

With technology that is reminiscent of science fiction classics such as Ghost in the Shell, Lawnmower Man and Firefox, Sydney-based company Emotiv Systems will develop, with the assistance of $1.5 million in Federal Government funding, a wireless headset that will "read" brain waves for a gaming experience that will blur the line between fantasy and reality.

The funding was announced by federal Industry Minister Ian Macfarlane yesterday, and forms part of the Government's Commercial Ready program, the aim of which is to help develop new Australian ideas into commercially viable products.


Read the full story

Apr 27, 2006

Your Thoughts Are Your Password

From Wired
Scientists hope that mind-reading computers will one day replace typed passwords, making fingerprint readers and retina scans obsolete. Skeptics say don't count on it.
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Apr 26, 2006

Neural interfaces for gaming

Via VRoot

Mercurynews reports that at least two start-ups have developed technology that monitors a player's brain waves and uses the signals to control the action in games. They hope it will enable game creators to immerse players in imaginary worlds that they can control with their thoughts instead of their hands.

San Jose's NeuroSky has been testing prototypes of its system that uses a sensor-laden headband to monitor brain waves, and then uses the signals to control the interaction in video games. They hope that such games are just the beginning of a mind-machine interface with many different applications.

Read the full story

Apr 12, 2006

Neural Interfaces: Chip ramps up neuron-to-computer communication

Via VRoot (From New Scientist):

A specialised microchip that could communicate with thousands of individual brain cells has been developed by European scientists.

The device will help researchers examine the workings of interconnected brain cells, and might one day enable them to develop computers that use live neurons for memory.

The computer chip is capable of receiving signals from more than 16,000 mammalian brain cells, and sending messages back to several hundred cells. Previous neuron-computer interfaces have either connected to far fewer individual neurons, or to groups of neurons clumped together.

New BCI shown off at CEBIT

Via New Scientist

Researchers at the Fraunhofer Institute in Berlin and Charité, the medical school of Berlin Humboldt University in Germany, have developed and succesfully tested a brain-computer interface, which could provide a way for paralysed patients to operate computers, or for amputees to operate electronically controlled artificial limbs. 

The device allows to type messages onto a computer screen by mentally controlling the movement of a cursor. A user must wear a cap containing EEG electrodes, and imagine moving their left or right arm in order to manoeuvre the cursor around.

The device was recently presented at the CeBit electronics fair in Hanover, Germany.

Read full article 

Apr 11, 2006

A comparison of common spatial patterns in a four-class BCI experiment

A comparison of common spatial patterns with complex band power features in a four-class BCI experiment.

IEEE Trans Biomed Eng. 2006 Apr;53(4):642-51

Authors: Townsend G, Graimann B, Pfurtscheller G

We report on the offline analysis of four-class brain-computer interface (BCI) data recordings. Although the analysis is done within defined time windows (cue-based BCI), our goal is to work toward an approach which classifies on-going electroencephalogram (EEG) signals without the use of such windows (un-cued BCI). To that end, we provide some elements of that analysis related to timing issues that will become important as we pursue this goal in the future. A new set of features called complex band power (CBP) features which make explicit use of phase are introduced and are shown to produce good results. As reference methods we used traditional band power features and the method of common spatial patterns. We consider also for the first time in the context of a four-class problem the issue of variability of the features over time and how much data is required to give good classification results. We do this in a practical way where training data precedes testing data in time.

Apr 07, 2006

EEG classification of movement intention

Classification of movement intention by spatially filtered electromagnetic inverse solutions.

Phys Med Biol. 2006 Apr 21;51(8):1971-89

Authors: Congedo M, Lotte F, Lécuyer A

We couple standardized low-resolution electromagnetic tomography, an inverse solution for electroencephalography (EEG) and the common spatial pattern, which is here conceived as a data-driven beamformer, to classify the benchmark BCI (brain-computer interface) competition 2003, data set IV. The data set is from an experiment where a subject performed a self-paced left and right finger tapping task. Available for analysis are 314 training trials whereas 100 unlabelled test trials have to be classified. The EEG data from 28 electrodes comprise the recording of the 500 ms before the actual finger movements, hence represent uniquely the left and right finger movement intention. Despite our use of an untrained classifier, and our extraction of only one attribute per class, our method yields accuracy similar to the winners of the competition for this data set. The distinct advantages of the approach presented here are the use of an untrained classifier and the processing speed, which make the method suitable for actual BCI applications. The proposed method is favourable over existing classification methods based on an EEG inverse solution, which rely either on iterative algorithms for single-trial independent component analysis or on trained classifiers.

Mar 28, 2006

Robust classification of EEG signal for brain-computer interface

Robust classification of EEG signal for brain-computer interface.

IEEE Trans Neural Syst Rehabil Eng. 2006 Mar;14(1):24-9

Authors: Thulasidas M, Guan C, Wu J

We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the training time can be further reduced by a factor of two from its current value of about 20 min. High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller.

gizmag: New Computer Interface Provides a Mental Typewriter and Brain Pong

Mindware Forum[Mindware Forum]

The initial project demonstrates how a paralyzed patient could communicate by using a mental typewriter alone - without touching the keyboard. In the case of serious accident or illness, a patient’s limbs can be paralyzed, severely restricting communication with the outside world.

Feb 10, 2006


BCI-info is an open international platform for Brain-Computer Interface research. Hosted by BCI Lab, TU Graz, Austria, the platform aims to provide information for scientists, patients, students, the media, and people from the general public interested in BCI technology.




Registration and submissions are free., but contributions are reviewed by the BCI-info board.

Feb 02, 2006

Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks

Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks

Neuroimage. 2006 Jan 26;

Authors: Pfurtscheller G, Brunner C, Schlögl A, Lopes da Silva FH

We studied the reactivity of EEG rhythms (mu rhythms) in association with the imagination of right hand, left hand, foot, and tongue movement with 60 EEG electrodes in nine able-bodied subjects. During hand motor imagery, the hand mu rhythm blocked or desynchronized in all subjects, whereas an enhancement of the hand area mu rhythm was observed during foot or tongue motor imagery in the majority of the subjects. The frequency of the most reactive components was 11.7 Hz +/- 0.4 (mean +/- SD). While the desynchronized components were broad banded and centered at 10.9 Hz +/- 0.9, the synchronized components were narrow banded and displayed higher frequencies at 12.0 Hz +/- 1.0. The discrimination between the four motor imagery tasks based on classification of single EEG trials improved when, in addition to event-related desynchronization (ERD), event-related synchronization (ERS) patterns were induced in at least one or two tasks. This implies that such EEG phenomena may be utilized in a multi-class brain-computer interface (BCI) operated simply by motor imagery.

Jan 25, 2006

A time-series prediction approach for feature extraction in a brain-computer interface

A time-series prediction approach for feature extraction in a brain-computer interface.

IEEE Trans Neural Syst Rehabil Eng. 2005 Dec;13(4):461-7

Authors: Coyle D, Prasad G, McGinnity TM

This paper presents a feature extraction procedure (FEP) for a brain-computer interface (BCI) application where features are extracted from the electroencephalogram (EEG) recorded from subjects performing right and left motor imagery. Two neural networks (NNs) are trained to perform one-step-ahead predictions for the EEG time-series data, where one NN is trained on right motor imagery and the other on left motor imagery. Features are derived from the power (mean squared) of the prediction error or the power of the predicted signals. All features are calculated from a window through which all predicted signals pass. Separability of features is achieved due to the morphological differences of the EEG signals and each NNs specialization to the type of data on which it is trained. Linear discriminant analysis (LDA) is used for classification. This FEP is tested on three subjects off-line and classification accuracy (CA) rates range between 88% and 98%. The approach compares favorably to a well-known adaptive autoregressive (AAR) FEP and also a linear AAR model based prediction approach.

Jan 16, 2006

Walking from thought

Walking from thought

Brain Res. 2006 Jan 5;

Authors: Pfurtscheller G, Leeb R, Keinrath C, Friedman D, Neuper C, Guger C, Slater M

Online analysis and classification of single electroencephalogram (EEG) trials during motor imagery were used for navigation in the virtual environment (VE). The EEG was recorded bipolarly with electrode placement over the hand and foot representation areas. The aim of the study was to demonstrate for the first time that it is possible to move through a virtual street without muscular activity when the participant only imagines feet movements. This is achieved by exploiting a brain-computer interface (BCI) which transforms thought-modulated EEG signals into an output signal that controls events within the VE. The experiments were carried out in an immersive projection environment, commonly referred to as a "Cave" (Cruz-Neira, C., Sandin, D.J., DeFanti, T.A., Surround-screen projection-based virtual reality: the design and implementation of the CAVE. Proceedings of the 20th annual conference on Computer graphics and interactive techniques, ACM Press, 1993, pp. 135-142) where participants were able to move through a virtual street by foot imagery only. Prior to the final experiments in the Cave, the participants underwent an extensive BCI training.

Jan 03, 2006

Brainport: an alternative input to the brain

J Integr Neurosci. 2005 Dec;4(4):537-50

Authors: Danilov Y, Tyler M

Brain Computer Interface (BCI) technology is one of the most rapidly developing areas of modern science; it has created numerous significant crossroads between Neuroscience and Computer Science. The goal of BCI technology is to provide a direct link between the human brain and a computerized environment. The objective of recent BCI approaches and applications have been designed to provide the information flow from the brain to the computerized periphery. The opposite or alternative direction of the flow of information (computer to brain interface, or CBI) remains almost undeveloped. The BrainPort is a CBI that offers a complementary technology designed to support a direct link from a computerized environment to the human brain - and to do so non-invasively. Currently, BrainPort research is pursuing two primary goals. One is the delivery of missing sensory information critical for normal human behavior through an additional artificial sensory channel around the damaged or malfunctioning natural sensory system. The other is to decrease the risk of sensory overload in human-machine interactions by providing a parallel and supplemental channel for information flow to the brain. In contrast, conventional CBI strategies (e.g., Virtual Reality), are usually designed to provide additional or substitution information through pre-existing sensory channels, and unintentionally aggravate the brain overload problem.

Dec 01, 2005

A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications

J Neural Eng. 2005 Dec;2(4):65-72

Authors: Qin L, He B

Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain-computer interfaces (BCIs). The translation of these EEG signals to control signals of a device is based on a good classification of various kinds of imagination. We have developed a wavelet-based time-frequency analysis approach for classifying motor imagery tasks. Time-frequency distributions (TFDs) were constructed based on wavelet decomposition and event-related (de)synchronization patterns were extracted from symmetric electrode pairs. The weighted energy difference of the electrode pairs was then compared to classify the imaginary movement. The present method has been tested in nine human subjects and reached an averaged classification rate of 78%. The simplicity of the present technique suggests that it may provide an alternative method for EEG-based BCI applications.

Characterization of four-class motor imagery EEG data for the BCI-competition 2005

J Neural Eng. 2005 Dec;2(4):L14-22

Authors: Schlögl A, Lee F, Bischof H, Pfurtscheller G

To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)-for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst.

Nov 03, 2005

New asynchronous brain computer interface

Via Smart Mobs

An asynchronous brain computer interface is under development at Oxford University, with the collaboration of Southampton and Essex universities. The system should allow a more effective way of controlling robotic arms and wheelchairs, as opposed to the less natural on/off mode of existing synchronous BCI technology. But the real novelty of this BCI apparatus is that it will use only one electrode.

The two-year project has been funded £180,000 by the EPSRC. According to project's leader Prof. Stephen Roberts (Oxford University), the new BCI system could improve the quality of life of severely disabled, but potential applications of this technology range from the gaming and entertainment industries to the automotive sector.

Read full article on the Engineer Online

More to explore

This page offers some introductory links to sources of information on the Web about the BCI

A list of BCI research lab