High Performance Multi-class Motor Imagery EEG Classification

Use of Motor Imagery (MI) in Electroencephalography (EEG) for real-life Brain Computer Interface applications require high performance algorithms that are both accurate as well as less computationally intensive. Common Spatial Pattern (CSP) and Filter Bank Common Spatial Pattern (FBSP) based methods of feature extraction for MI-classification has been shown very promising. In this paper we have advanced this frontier to present a new efficient approach whose variants out compete in accuracy (in terms of kappa values) with the existing approaches with the same or smaller feature set. We have demonstrated that use of one mu band and three beta sub-bands is very ideal both from the point-of-view of accuracy as well as computational complexity. We have been able to achieve the best reported kappa value of 0.67 for Dataset 2a of BCI Competition IV using our approach with a feature vector of length 64 directly composed out of FBCSP transformed data samples without the need of further feature selection. The feature vector of size 32 directly composed from FBCSP data is enough to outcompete existing approaches with regard to kappa value achievement. In this paper we also have systematically reported experiments with different classifiers including kNN, SVM, LDA, Ensemble, ANN and ANFIS and different lengths of feature vectors. SVM has been reported as the best classifier followed by the LDA.