Towards self-paced ( asynchronous ) Brain-Computer Communication : Navigation through virtual worlds

The self-paced (or asynchronous) control paradigm enables users to operate Brain-Computer Interfaces (BCI) i n a more natural way: no longer the machine is in control of timing and speed of communication, but the user. This is important t o enhance the usability, flexibility and response time of a BCI . In this work, we show how subjects, after performing a cuebased feedback training (smiley paradigm), learned to navi gate self-paced through the “freeSpace” Virtual Environment (VE). Similar to computer games, subjects had the task of picking u p items within a limited time period using the following navigation commands: rotate left, rotate right and move forward (3-classes). Since the self-paced control paradigm allows subjects to ma ke voluntary decisions on time, type and duration of mental act ivity, no cues or routing directives were presented. The BCI was based on three bipolar electroencephalogram (EEG) channel s and operated by motor imagery. Eye movements (electrooculogra m, EOG) and electromyographic (EMG) artifacts were reduced and detected on-line. Results of three able-bodied subject s are reported and problems emerging from asynchronous control a re discussed.

[1]  G Pfurtscheller,et al.  Frequency component selection for an EEG-based brain to computer interface. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[2]  Bernhard Graimann,et al.  Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis , 2004, IEEE Transactions on Biomedical Engineering.

[3]  G. Pfurtscheller,et al.  EEG-based neuroprosthesis control: A step towards clinical practice , 2005, Neuroscience Letters.

[4]  Reinhold Scherer,et al.  A fully on-line adaptive BCI , 2006, IEEE Transactions on Biomedical Engineering.

[5]  Clemens Brunner,et al.  Online Control of a Brain-Computer Interface Using Phase Synchronization , 2006, IEEE Transactions on Biomedical Engineering.

[6]  Niels Birbaumer,et al.  Brain–computer-interface research: Coming of age , 2006, Clinical Neurophysiology.

[7]  Gary E. Birch,et al.  Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch , 2004, IEEE Transactions on Biomedical Engineering.

[8]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[9]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[10]  Gary E. Birch,et al.  A brain-controlled switch for asynchronous control applications , 2000, IEEE Trans. Biomed. Eng..

[11]  G. Pfurtscheller,et al.  Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. , 2005, Brain research. Cognitive brain research.

[12]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[13]  J. Mourino,et al.  Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  M. Tarr,et al.  Virtual reality in behavioral neuroscience and beyond , 2002, Nature Neuroscience.

[15]  Christa Neuper,et al.  An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate , 2004, IEEE Transactions on Biomedical Engineering.

[16]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface , 2008, WCCI.

[17]  Reinhold Scherer,et al.  Study of On-Line Adaptive Discriminant Analysis for EEG-Based Brain Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.

[18]  Dennis J. McFarland,et al.  Brain-computer interface (BCI) operation: signal and noise during early training sessions , 2005, Clinical Neurophysiology.

[19]  G. Pfurtscheller,et al.  A fully automated correction method of EOG artifacts in EEG recordings , 2007, Clinical Neurophysiology.

[20]  Christa Neuper,et al.  Human Brain – Computer Interface , 2005 .

[21]  G. Pfurtscheller,et al.  Critical Decision-Speed and Information Transfer in the “Graz Brain–Computer Interface” , 2003, Applied psychophysiology and biofeedback.

[22]  Mehrdad Fatourechi,et al.  Evaluating the Performance of Self-Paced Brain-Computer Interface Technology , 2006 .

[23]  F H Lopes da Silva,et al.  Automatic detection and localization of epileptic foci. , 1977, Electroencephalography and clinical neurophysiology.