Audio-cued motor imagery-based brain-computer interface: Navigation through virtual and real environments

The aim of this work is to provide a navigation paradigm that could be used to control a wheelchair through a brain-computer interface (BCI). In such a case, it is desirable to control the system without a graphical interface so that it will be useful for people without gaze control. Thus, an audio-cued paradigm with several navigation commands is proposed. In order to reduce the probability of misclassification, the BCI operates with only two mental tasks: relaxed state versus imagination of right hand movements; the use of motor imagery for navigation control is not yet extended among the auditory BCIs. Two experiments are described: in the first one, users practice the switch from a graphical to an audio-cued interface with a virtual wheelchair; in the second one, they change from virtual to real environments. The obtained results support the use of the proposed interface to control a real wheelchair without the need of a screen to provide visual stimuli or feedback.

[1]  H. Flor,et al.  A multimodal brain-based feedback and communication system , 2004, Experimental Brain Research.

[2]  F. L. D. Silva,et al.  Event-Related Desynchronization , 1999 .

[3]  S. Voloshynovskiy,et al.  EEG-Based Synchronized Brain-Computer Interfaces: A Model for Optimizing the Number of Mental Tasks , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.

[5]  John Q. Gan,et al.  Asynchronous BCI Control of a Robot Simulator with Supervised Online Training , 2007, IDEAL.

[6]  Gert Pfurtscheller,et al.  Self-paced exploration of the Austrian National Library through thought , 2007 .

[7]  José del R. Millán,et al.  Evaluation Criteria for BCI Research , 2007 .

[8]  R. F. Thompson,et al.  Habituation: a model phenomenon for the study of neuronal substrates of behavior. , 1966, Psychological review.

[9]  E. Donchin,et al.  A P300-based brain–computer interface: Initial tests by ALS patients , 2006, Clinical Neurophysiology.

[10]  Brice Rebsamen,et al.  A brain controlled wheelchair to navigate in familiar environments. , 2010, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[11]  F. Boiten,et al.  Event-related desynchronization: the effects of energetic and computational demands. , 1992, Electroencephalography and clinical neurophysiology.

[12]  Bernhard Schölkopf,et al.  An Auditory Paradigm for Brain-Computer Interfaces , 2004, NIPS.

[13]  Thilo Hinterberger,et al.  An Auditory Brain-Computer Interface Based on the Self-Regulation of Slow Cortical Potentials , 2005, Neurorehabilitation and neural repair.

[14]  John R. Anderson Acquisition of cognitive skill. , 1982 .

[15]  Ricardo Ron-Angevin,et al.  Brain–computer interface: Changes in performance using virtual reality techniques , 2009, Neuroscience Letters.

[16]  D. McFarland,et al.  An auditory brain–computer interface (BCI) , 2008, Journal of Neuroscience Methods.

[17]  Francisco Velasco-Álvarez,et al.  A two-class brain computer interface to freely navigate through virtual worlds / Ein Zwei-Klassen-Brain-Computer-Interface zur freien Navigation durch virtuelle Welten , 2009, Biomedizinische Technik. Biomedical engineering.

[18]  Marco A. Meggiolaro,et al.  Activation of a mobile robot through a brain computer interface , 2010, 2010 IEEE International Conference on Robotics and Automation.

[19]  Stephen J. Roberts,et al.  A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training , 2009, Medical & Biological Engineering & Computing.

[20]  Francisco Velasco-Álvarez,et al.  Free Virtual Navigation Using Motor Imagery Through an Asynchronous BrainComputer Interface , 2010, PRESENCE: Teleoperators and Virtual Environments.

[21]  Christa Neuper,et al.  Motor imagery and ERD , 1999 .

[22]  Shin'ichiro Kanoh,et al.  A Brain-Computer Interface (BCI) System Based on Auditory Stream Segregation , 2010 .

[23]  N. Birbaumer,et al.  An auditory oddball brain–computer interface for binary choices , 2010, Clinical Neurophysiology.

[24]  Horst Bischof,et al.  Toward Self-Paced Brain–Computer Communication: Navigation Through Virtual Worlds , 2008, IEEE Transactions on Biomedical Engineering.

[25]  G. Pfurtscheller,et al.  Information transfer rate in a five-classes brain-computer interface , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[26]  K. Müller,et al.  Psychological predictors of SMR-BCI performance , 2012, Biological Psychology.

[27]  C. Neuper,et al.  Toward a high-throughput auditory P300-based brain–computer interface , 2009, Clinical Neurophysiology.

[28]  Iñaki Iturrate,et al.  A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation , 2009, IEEE Transactions on Robotics.

[29]  G. Pfurtscheller,et al.  Rapid prototyping of an EEG-based brain-computer interface (BCI) , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[30]  N. Birbaumer,et al.  An auditory oddball (P300) spelling system for brain-computer interfaces. , 2009, Psychophysiology.

[31]  Gernot R. Müller-Putz,et al.  Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic , 2007, Comput. Intell. Neurosci..

[32]  Ricardo Ron-Angevin,et al.  A two-class self-paced BCI to control a robot in four directions , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[33]  N. Birbaumer,et al.  Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. , 2001, Archives of physical medicine and rehabilitation.