Motor imagery-based neuro-feedback system using neuronal excitation of the active synapses

摘要

Neuronal excitation enables identifying the features of an electroencephalogram (EEG) signal for motor imagery detection. We propose a novel feature extraction algorithm supported by short-term cepstrum-based inverse filtration of neuronal excitation of the active synapse. The maximum power of the estimated neuronal excitation is subjected to a two-class Bayesian probabilistic classifier. The feature extraction algorithm with the Bayesian probabilistic classifier significantly improves the brain-computer interface performance compared with that of other conventional methods of EEG signal processing such as wavelet with a Bayesian classifier, autocorrelation and CSP filter with a naive Bayes classifier over the BCI competition II and IV datasets. Consequently, this neuronal excitation feature allows the authors to develop a motor imagery neuro-feedback system; the performance of which achieves 87.2% average classification accuracy, which is 14% greater than that of the wavelet-based algorithm and 6.2% greater than that of the TRSP-based algorithm, with 53 ms of processing time allotted for each instruction in a real-time experiment. However, brain signal variation across different subjects and sessions significantly impairs decision accuracy. Our neuronal excitation base feature extraction algorithm minimizes these variations in classification accuracy.