Offline Study of Brain Computer Interfacing for Hand Movement Using OpenVIBE

Brain Computer Interfacing is a newly developing field of neuroscience and computer science. Brain Computer Interface (BCI) is a System that provides a non-muscular communication between men and machines. Communication with BCI is done without the use of language or without controlling the computer manually with body parts. We are describing the OpenViBE platform that serves all the necessary steps required in designing typical BCI system. OpenViBE works on EEG signals to measure brain activity. EEG signals on the basis of frequencies and amplitudes are classified into different bands corresponding to different states like sleep, awake, mediation and alert. In this paper we describe the preprocessing, feature extraction, classification and conversion of signals into commands by taking prerecorded EEG signals of real hand movement and motor imaginary movement and convert it into suitable commands that are used to control virtual reality computer application.

[1]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[2]  Massimo Poesio,et al.  Detecting Semantic Category in Simultaneous EEG/MEG Recordings , 2010, HLT-NAACL 2010.

[3]  Bin He,et al.  Relationship between speed and EEG activity during imagined and executed hand movements , 2010, Journal of neural engineering.

[4]  G Calhoun,et al.  Brain-computer interfaces based on the steady-state visual-evoked response. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[5]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[6]  R. Nagarajan,et al.  EEG Motor Imagery Classification of Hand Movements for a Brain Machine Interface( Biosensors: Data Acquisition, Processing and Control) , 2009 .

[7]  Gerwin Schalk,et al.  A brain–computer interface using electrocorticographic signals in humans , 2004, Journal of neural engineering.

[8]  José del R. Millán,et al.  Brain-actuated interaction , 2004, Artif. Intell..

[9]  T. J. Sullivan,et al.  A user-friendly SSVEP-based brain–computer interface using a time-domain classifier , 2010, Journal of neural engineering.

[10]  Ling Huang,et al.  Feature Extraction of EEG Signals Using Power Spectral Entropy , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[11]  Bernhard Schölkopf,et al.  Methods Towards Invasive Human Brain Computer Interfaces , 2004, NIPS.

[12]  Russell M. Taylor,et al.  VRPN: a device-independent, network-transparent VR peripheral system , 2001, VRST '01.

[13]  Francesco Piccione,et al.  Integration of a P300 Brain Computer Interface into Virtual Environment , 2007, 2007 Virtual Rehabilitation.

[14]  D. Gutierrez,et al.  Using EEG/MEG Data of Cognitive Processes in Brain-Computer Interfaces , 2008 .

[15]  Cuntai Guan,et al.  An Efficient P300-based Brain-Computer Interface with Minimal Calibration Time , 2009, NIPS 2009.

[16]  Christa Neuper,et al.  Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments. , 2006, Progress in brain research.