Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller

This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels.

[1]  D. Craig,et al.  Adaptive EEG Thought Pattern Classifier for Advanced Wheelchair Control , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  J. Wolpaw,et al.  Towards an independent brain–computer interface using steady state visual evoked potentials , 2008, Clinical Neurophysiology.

[3]  Gerwin Schalk,et al.  Brain–computer symbiosis , 2008, Journal of neural engineering.

[4]  M. Stokes,et al.  Cognitive tasks for driving a brain-computer interfacing system: a pilot study , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[6]  Hung T. Nguyen,et al.  Intelligent technologies for real-time biomedical engineering applications , 2008, Int. J. Autom. Control..

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

[8]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

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

[10]  R. Palaniappan,et al.  Brain Computer Interface Design Using Band Powers Extracted During Mental Tasks , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

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

[12]  M. Nuttin,et al.  Asynchronous non-invasive brain-actuated control of an intelligent wheelchair , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.