Abstract: Motor imagery electroencephalography (EEG) is a multi-channel and high-dimensional signal. Feature selection can reduce the feature dimension and select more discriminative features, thereby effectively improving the performance of EEG decoding. The existing feature selection methods mainly include filter, wrapper and embedded methods, these three methods have their own advantages and disadvantages. In order to comprehensively utilize the advantages of various methods, two hybrid feature selection methods are proposed in this paper. For the first method, the least absolute shrinkage and selection operator (LASSO) is used for feature selection. After the weight of LASSO model is obtained, a series of weight thresholds are set for secondary feature selection. For the second method, the Fisher score is used to score the features, then a series of weight thresholds are set for secondary feature selection. The Fisher linear discriminant analysis (FLDA) is used to classify the feature subsets selected by the two methods. Experiments were conducted on two sets of brain-computer interface (BCI) competition data sets and a set of self-collected laboratory data sets, and the average classification accuracy rates were 77.47%, 76.11%, and 71.30%, respectively. The experimental results show that the classification performance of the proposed method is better than the existing feature selection methods, and the feature selection time also has a greater advantage.

Key words: motor imagery, EEG, feature selection