Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and Riemannian tangent space for MI-BCIs.

Motor imagery-based brain computer interfaces (MI-BCIs) have been widely researched because they do not demand external stimuli and have a high degree of maneuverability. In most scenarios, superabundant selected channels, fixed time windows, and frequency bands would certainly affect the performance of MI-BCIs due to the neurophysiological diversities among different individuals. In this study, we attempt to effectively use the Riemannian geometry of spatial covariance matrix to extract more robust features and thus enhance the decoding efficiency.First, we utilize a Riemannian distance-based electroencephalography (EEG) channel selection method, which preliminarily reduces the information redundancy in the first stage. Second, we extract discriminative Riemannian tangent space features of EEG signals of selected channels from the most discriminant time-frequency bands to further enhance decoding accuracy for MI-BCIs. Finally, we train a support vector machine model with a linear kernel to classify our extracted discriminative Riemannian features, and evaluate our proposed method using publicly available BCI Competition IV dataset Ⅰ (DS1) and Competition Ⅲ dataset Ⅲa (DS2).The experimental results show that the average classification accuracy with the selected 16-channel EEG signals of our method is 90.0% and 89.4% in DS1 and DS2 respectively. The average improvements are 20.0% and 21.2% on DS1, 9.4% and 7.2% on DS2 for 8 and 16 selected channels, respectively.These results show that our proposed method is a promising candidate for the performance improvement of MI-BCIs.