Improving Mean Covariance Matrix Estimation by Minimizing Within-class Dissimilarities Using Asymmetry of Kullback-Leibler Divergence in MI-Based BCI

Brain-computer interface (BCI) systems create a direct communication channel from the brain to an output device by translating brain activity into a sequence of control commands. Feature extraction algorithms play a key role in the performance of the BCI systems. Some feature extraction algorithms use the mean (class) covariance matrices containing information about the dispersion and variability of the data and are estimated from empirical data. Common spatial pattern is one of the most widespread feature extraction algorithms in this field that needs a robust and well-estimated mean covariance matrix for each class. To this end, we have proposed a novel method to estimate a mean covariance matrix for each class of data which has the minimum within-class dissimilarity based on the asymmetry of Kullback–Leibler divergence using a linear combination of its two asymmetric directions. We applied the proposed algorithm to three public electroencephalographic datasets (BCI competition IV dataset I, BCI competition IV dataset IIa, and BCI competition III dataset IVa) to validate its effectiveness, compared with several methods in the same category of covariance matrix improvement as the benchmark. The proposed method showed at least 8% superiority in grand mean classification accuracy of the three datasets over the benchmark methods, while the accuracies of most individuals are improved. The experimental results reveal that the proposed algorithm provides statistically significant improvements in classification results by reducing within-class variations. In addition, it led to more compact and separable features and may cause more robust BCI applications.