Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interface

BACKGROUND Recently, hybrid brain-computer interfaces (BCIs) combining more than one modality have been investigated with the aim of boosting the performance of the existing single-modal BCIs in terms of accuracy and information transfer rate (ITR). Previously, we introduced a novel hybrid BCI in which EEG and fTCD modalities are used simultaneously to measure electrical brain activity and cerebral blood velocity during motor imagery (MI) tasks. NEW METHOD In this paper, we used multi-scale analysis and common spatial pattern algorithm to extract EEG and fTCD features. Moreover, we proposed probabilistic fusion of EEG and fTCD evidences instead of concatenating EEG and fTCD feature vectors corresponding to each trial. A Bayesian approach was proposed to fuse EEG and fTCD evidences under 3 different assumptions. RESULTS Experimental results showed that 93.85%, 93.71%, and 100% average accuracies and 19.89, 26.55, and 40.83 bits/min average ITRs were achieved for right MI vs baseline, left MI versus baseline, and right MI versus left MI respectively. COMPARISON WITH EXISTING METHODS These performance measures outperformed the results we obtained before in our preliminary study in which average accuracies of 88.33%, 89.48%, and 82.38% and average ITRs of 4.17, 5.45, and 10.57 bits/min were achieved for right MI versus baseline, left MI versus baseline, and right MI versus left MI respectively. Moreover, in terms of both accuracy and speed, the EEG- fTCD BCI with the proposed analysis techniques outperformed all EEG- fNIRS studies in comparison. CONCLUSIONS The proposed system is a more accurate and faster alternative to EEG-fNIRS systems.