Motor imagery EEG classification via Bayesian extreme learning machine
Yu Zhang, Jing Jin, Xingyu Wang, Yu Wang · 2016
Motor imagery is usually hard to be classified with a high accuracy, since the task-related electroencephalogram (EEG) responses are likely to be contaminated by some ongoing noises. Design of an efficient classifier is considerably important for the realization of a brain-computer interface (BCI) system based on motor imagery. This study introduces a Bayesian extreme learning machine (BELM) based method for accurate classification of motor imagery. By combing ELM and Bayesian inference, BELM achieves the smallest norm of output weights with automatically estimated regularization for alleviating the possible overfitting during calibration procedure. Effectiveness of the BELM-based method is validated on a public BCI dataset, in comparison with other two competing methods.