Attention-based spatio-temporal-spectral feature learning for subject-specific EEG classification

Dong Hee Ko, Dong Hee Shin, Tae Eui Kam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Brain-computer interface (BCI) is a system that recognizes the human intentions from the brain signals for communication with external devices. The electroencephalography (EEG) signals are commonly used for motor imagery based braincomputer interface (MI-BCI) due to non-invasive, cost-effective, and portable manner. For the analysis of the EEG signals, there are several machine learning and deep learning methods. However, the majority of those methods have limitations of not considering the distinct frequency bands for subject-specific manner. Therefore, we propose the method that pays attention to the significant frequency bands for each subject and also extracts the spatio-temporal-spectral features simultaneously. We utilize filter bank, sliding window segmentation, and the convolutional neural network (CNN) to extract the spatio-temporal features with consideration of multiple frequency bands. Then, we employ the sub-band attention to determine the significant information of each frequency band. Finally, the attention-based Bi-directional Long-Short Term Memory (Bi-LSTM) is implemented to extract the temporal dynamic features. Our proposed method is evaluated on the BCI Competition IV-2a dataset by using two classes in the subject-specific manner. The experimental results demonstrate that our proposed method is effective to focus on the significant frequency band for each subject.

Original languageEnglish
Title of host publication9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728184852
DOIs
Publication statusPublished - 2021 Feb 22
Event9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021 - Gangwon, Korea, Republic of
Duration: 2021 Feb 222021 Feb 24

Publication series

Name9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021

Conference

Conference9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
Country/TerritoryKorea, Republic of
CityGangwon
Period21/2/2221/2/24

Keywords

  • Brain-computer interface
  • Deep learning
  • Electroencephalography
  • Feature learning
  • Filter bank
  • Motor imagery
  • Self-attention
  • Subject-specific manner

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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