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

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Artificial Intelligence (CICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13069))

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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.

Supported by the National Natural Science Foundation of China under Grant 61876082, 61861130366, 61732006 and National Key R&D Program of China under Grant 2018YFC2001600, 2018YFC2001602. Thanks to Chinese sign language teacher Yinling Du for providing guidance.

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Authors and Affiliations

  1. College of Computer Science and Technology, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Pengpai Wang, Yueying Zhou, Zhongnian Li & Daoqiang Zhang

Authors
  1. Pengpai Wang
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  2. Yueying Zhou
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  3. Zhongnian Li
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  4. Daoqiang Zhang
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Corresponding author

Correspondence to Daoqiang Zhang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Duke University, Durham, NC, USA

    Yiran Chen

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. University of British Columbia, Vancouver, BC, Canada

    Jane Wang

  5. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

  6. Xidian University, Xi’an, China

    Weisheng Dong

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Wang, P., Zhou, Y., Li, Z., Zhang, D. (2021). GBCI: Adaptive Frequency Band Learning for Gender Recognition in Brain-Computer Interfaces. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_19

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  • DOI: https://doi.org/10.1007/978-3-030-93046-2_19

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-93046-2

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