Data-driven motor imagery EEG classifier using difference subspace method

Motor imagery (MI) electroencephalography (EEG) based brain-computer interface (BCI) systems are in great demand for many real-time applications both in the medical and consumer sectors. However, the processing of MI EEG data is challenging, and many of the approaches are complex and suffer from poor classification accuracy due to the unstability of EEG signals. To enhance the classification accuracy, a novel computationally efficient difference subspace method (DSM) of classification is proposed in this study. The proposed method is driven directly by the data without the necessity of extracting any handcrafted features. Subspaces are learned using principal component analysis (PCA). A search strategy based on overlapping criteria is used for selecting the subspace dimensions. The similarity between the two subspaces, measured based on the canonical angles, is used as the criteria to classify MI activity. The proposed method is implemented on BCI competition III and IV datasets, and the performance is evaluated on the basis of classification accuracy (%CA), Cohen's kappa coefficient, F1-score etc., By retention of only the difference components between the two groups of data after removing the common components, the difference subspace method could do dominatingly well in classifying the left and right hand MI classification compared to the subspace method (SM) and many of state-of-the-art techniques. The results show that the DSM achieves an average classification accuracy and Cohen's kappa coefficient for BCI competition III and IV datasets as 83.97% & 0.68 and 86.22% & 0.72, respectively.