Study of electroencephalogram feature extraction and classification of three tasks of motor imagery

Brain-computer interface (BCI) instead of depending on the brain's normal output pathways, can use electroencephalogram (EEG) from the scalp as the representation of brain activity to control external devices. EEG during motor imagery (MI) provides a non-muscular communication way to control external devices and has advantage of non-invasiveness and high time resolution. However the application is still limited by time-consuming training and poor classification rate with multiple categories etc. We recorded 64-channel scalp EEG from eight healthy subjects during imagery tasks of left, right hand movements and stop. EEG was analyzed in time-frequency distribution and spatial topographies were explored too. A one versus one common spatial pattern was applied to construct feature vector and then linear discriminant analysis was used for the classification. For the purpose of real time control in the future, small training size was used and we got discrimination among three types of motor imagery at the accuracy rate about 90%.

[1]  Michael Unser,et al.  A review of wavelets in biomedical applications , 1996, Proc. IEEE.

[2]  J. Wolpaw,et al.  Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements , 2004, Brain Topography.

[3]  N. Thakor,et al.  Multiresolution wavelet analysis of evoked potentials , 1993, IEEE Transactions on Biomedical Engineering.

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

[5]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  D.J. McFarland,et al.  The Wadsworth Center brain-computer interface (BCI) research and development program , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  G Pfurtscheller,et al.  Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data , 2002, Clinical Neurophysiology.

[8]  Faisal Karmali,et al.  Environmental control by a brain-computer interface , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[9]  R Biscay,et al.  Multiresolution decomposition of non-stationary EEG signals: a preliminary study. , 1995, Computers in biology and medicine.

[10]  J Pardey,et al.  A review of parametric modelling techniques for EEG analysis. , 1996, Medical engineering & physics.

[11]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[12]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[13]  Michiteru Kitazaki,et al.  Event-related de-synchronization and synchronization (ERD/ERS) of EEG for controlling a brain-computer-interface driving simulator , 2009, VRST '09.

[14]  Shang-Lin Wu,et al.  Common spatial pattern and linear discriminant analysis for motor imagery classification , 2013, 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB).

[15]  Shangkai Gao,et al.  A practical VEP-based brain-computer interface. , 2006, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[17]  S. Tseng,et al.  Evaluation of parametric methods in EEG signal analysis. , 1995, Medical engineering & physics.

[18]  Yijun Wang,et al.  Brain-Computer Interfaces Based on Visual Evoked Potentials , 2008, IEEE Engineering in Medicine and Biology Magazine.

[19]  Dong Ming,et al.  Time-locked and phase-locked features of P300 event-related potentials (ERPs) for brain-computer interface speller , 2010, Biomed. Signal Process. Control..

[20]  Walter H. Chang,et al.  The new design of an infrared-controlled human-computer interface for the disabled. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[21]  Gerwin Schalk,et al.  Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000. , 2009, Journal of visualized experiments : JoVE.

[22]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.