Motor imagery based brain computer interface using transform domain features

Brain Computer Interface (BCI) is a channel of communication between the human brain and an external device through brain electrical activity. In this paper, we extracted different features to boost the classification accuracy as well as the mutual information of BCI systems. The extracted features include the magnitude of the discrete Fourier transform and the wavelet coefficients for the EEG signals in addition to distance series values and invariant moments calculated for the reconstructed phase space of the EEG measurements. Different preprocessing, feature selection, and classification schemes were utilized to evaluate the performance of the proposed system for dataset III from BCI competition II. The maximum accuracy achieved was 90.7% while the maximum mutual information was 0.76 bit obtained using the distance series features.

[1]  Andrzej Cichocki,et al.  Nonnegative Tensor Factorization for Continuous EEG Classification , 2007, Int. J. Neural Syst..

[2]  Francisco Sepulveda,et al.  Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface , 2008, Inf. Sci..

[3]  Yasar Ayaz,et al.  MOTOR IMAGERY BASED EEG SIGNAL CLASSIFICATION USING SELF ORGANIZING MAPS , 2015 .

[4]  Min-You Chen,et al.  Extracting features from phase space of EEG signals in brain-computer interfaces , 2015, Neurocomputing.

[5]  Yasser M. Kadah,et al.  Arrhythmia classification based on novel distance series transform of phase space trajectories , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[6]  Qi Xu,et al.  Fuzzy support vector machine for classification of EEG signals using wavelet-based features. , 2009, Medical engineering & physics.

[7]  Abbas Erfanian,et al.  An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network. , 2010, Medical engineering & physics.

[8]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[9]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[10]  A. Al-Ani,et al.  Brain-Computer Interface Analysis using Continuous Wavelet Transform and Adaptive Neuro-Fuzzy Classifier , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Naveeta Kant,et al.  Classification of EEG Signal for Imagined Left and Right Hand Movement for Brain Computer Interface Applications , 2014 .

[12]  A. Schlogl,et al.  Information transfer of an EEG-based brain computer interface , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

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

[14]  Steven Lemm,et al.  BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements , 2004, IEEE Transactions on Biomedical Engineering.

[15]  Alexander G. Mamistvalov.  n-Dimensional Moment Invariants and Conceptual Mathematical Theory of Recognition n-Dimensional Solids , 1998, IEEE Trans. Pattern Anal. Mach. Intell..