GBCI: Adaptive Frequency Band Learning for Gender Recognition in Brain-Computer Interfaces

Pengpai Wang, Yueying Zhou, Zhongnian Li, Daoqiang Zhang

Abstract: In recent years, with the rapid development of brain-computer interface (BCI) technology, various applications based on BCI have generated significant interest. A motivating application of BCI is predicting human gender by electroencephalogram (EEG) analysis. However, most of the recent researches only identify the gender of EEG in the resting state. In real-life practice, it is difficult for the subjects to be at a full resting state when it mixes with motor imagery (MI). Therefore, we acquired two kinds of EEG activities data including 9 subjects (5 males and 4 females) with two state (i.e., resting state and MI) based on Chinese sign language. In this paper, to recognize gender form two state, an improved adaptive variational mode decomposition with long short term memory (AVMD-LSTM) network is developed to construct a hybrid state learning framework. Besides, the sample entropy (SE) is employed to select the channel of EEG data as the input of AVMD-LSTM. The recognition accuracy of gender classification was 89.21\(\%\). Comparing with the task of complete resting state, the model is validated with more robustness. Furthermore, the proposed algorithm has many applications, including biometrics, healthcare, and online entertainment advertising.