An EEG Signal Analysis Method based on Elastic Network Combined with Filter Bank

Brain Computer Interface (BCI) is a new way of interaction between the human brain and the outside world. The analysis of EEG(Electroencephalogram) signals is crucial in the field of brain-computer interface. In this paper, a feature selection and classification method based on encapsulated elastic network is proposed in combination with filter banks. The effectiveness of the method is demonstrated by the case of motion-imagining EEG signals in international competition data. At the same time, the method is compared with the conventional band selection method and the filtered elastic network feature selection method to prove the superiority of the method in the classification performance of the BCI system.