EEG-based motor imagery classification using subject-specific spatio-spectral features

Brain-Computer Interface (BCI) technology provides a new mode of direct communication between man and machine, replacing human's normal output control pathways of nerves and muscles. It embeds huge potential to be explored in medicine, rehabilitation and entertainment that can be used for the healthy as well as the disabled. In order to thoroughly exploit BCI's capabilities, efficient man-machine interaction has to be established by accurately identifying human intentions from brain waves. Electroencephalogram (EEG) is a strong tool in BCI research as it is a cheap and easy non-invasive recording methodology of brain activity encoded with human thoughts. This paper investigates an EEG dataset recorded from 85 healthy subjects performing imagination of motor movements, and highlights the necessity of selecting the subject-specific spatial and spectral features to optimize motor imagery recognition performance. Subject-specific spectral and spatial features associated with right and left hand motor imagery are identified on account of the discriminative weights of EEG signals recorded from motor cortex region. Discriminative capability has been estimated using the Fisher ratio values of each frequency component for each channel. Using the proposed hybrid subject-specific selection of channels and bands, the proposed BCI system is capable of offering comparable classification accuracy with the state of the art methodology which employs more number of channels and frequency bands.

[1]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[2]  Klaus-Robert Müller,et al.  Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing , 2006, IEEE Transactions on Biomedical Engineering.

[3]  Chiew Tong Lau,et al.  A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces , 2009, IEEE Transactions on Biomedical Engineering.

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

[5]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[7]  J. Baron,et al.  Motor Imagery: A Backdoor to the Motor System After Stroke? , 2006, Stroke.

[8]  Chongxun Zheng,et al.  Study on the Effect of Different Frequency Bands of EEG Signals on Mental Tasks Classification , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[9]  David Feess,et al.  Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface , 2013, PloS one.

[10]  G. Pfurtscheller,et al.  Motor imagery activates primary sensorimotor area in humans , 1997, Neuroscience Letters.

[11]  Gernot R. Müller-Putz,et al.  Evaluation of Different EEG Acquisition Systems Concerning Their Suitability for Building a Brain–Computer Interface: Case Studies , 2016, Front. Neurosci..

[12]  A. Prasad Vinod,et al.  An iterative optimization technique for robust channel selection in motor imagery based Brain Computer Interface , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[13]  Chiew Tong Lau,et al.  Adaptive tracking of discriminative frequency components in electroencephalograms for a robust brain–computer interface , 2011, Journal of neural engineering.

[14]  G Pfurtscheller,et al.  Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[15]  Cuntai Guan,et al.  EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  Vinod Achutavarrier Prasad,et al.  Optimized Bi-Objective EEG Channel Selection and Cross-Subject Generalization With Brain–Computer Interfaces , 2016, IEEE Transactions on Human-Machine Systems.

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

[18]  Gabriel Curio,et al.  MACHINE LEARNING TECHNIQUES FOR BRAIN-COMPUTER INTERFACES , 2004 .

[19]  V V Skvortsov,et al.  Transcranial Electrostimulation in Treatment of Chronic Diffuse Liver Diseases , 2015, Meditsinskaia tekhnika.