Mental tasks selection method for a SVM-based BCI system

In this work, a study that analyzes the best combinations of mental tasks in a Brain-Computer Interface (BCI) using a classifier based on Support Vector Machine (SVM) is presented. To that end, twelve mental tasks of different nature are analyzed and the results of the classification for the combinations of two, three and four tasks are obtained. Four volunteers performed registers of the twelve tasks. The main goal is to find the combination of more than three mental tasks that obtains the higher reliability to apply it in future complex applications that require the use of more than three mental control commands. After a selection procedure, the results obtained show higher success percentages and important differences according to the nature of the mental tasks, which suggest that it is possible to differentiate with enough reliability between more than three mental tasks using the methodology proposed.

[1]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[2]  Xiaorong Gao,et al.  A BCI-based environmental controller for the motion-disabled , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  C. Perez,et al.  Brain-machine interface based on EEG mapping to control an assistive robotic arm , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[4]  Rabab K Ward,et al.  A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.

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

[6]  Yoko Akiyama,et al.  The Development of BCI Using Alpha Waves for Controlling the Robot Arm , 2008, IEICE Trans. Commun..

[7]  Febo Cincotti,et al.  Relevant EEG features for the classification of spontaneous motor-related tasks , 2002, Biological Cybernetics.

[8]  Miguel A. L. Nicolelis,et al.  Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex , 1999, Nature Neuroscience.

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

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

[11]  Miguel A. L. Nicolelis,et al.  Actions from thoughts , 2001, Nature.

[12]  S. Senthilkumar,et al.  Non-Invasive Brain-Machine Interaction , 2014 .

[13]  S. Meagher.  Instant neural control of a movement signal , 2002 .

[14]  J.D. Bayliss,et al.  Use of the evoked potential P3 component for control in a virtual apartment , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Andrés Úbeda,et al.  Development of a Low-cost SVM-based Spontaneous Brain-computer Interface , 2011, IJCCI.

[16]  Ricardo Chavarriaga,et al.  Non-Invasive Brain-Machine Interaction , 2008, Int. J. Pattern Recognit. Artif. Intell..

[17]  Andrés Úbeda,et al.  Mental tasks-based brain-robot interface , 2010, Robotics Auton. Syst..

[18]  José del R. Millán,et al.  Towards Brain-Computer Interfacing , 2007 .

[19]  Andrés Úbeda,et al.  Visual evoked potential-based brain-machine interface applications to assist disabled people , 2012, Expert Syst. Appl..

[20]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.