A Convolutional Recurrent Attention Model for Subject-Independent EEG Signal Analysis

Dalin Zhang*, Lina Yao, Kaixuan Chen, Jessica Monaghan

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

124 Citations (Scopus)

Abstract

The electroencephalogram (EEG) signal is a medium to realize a brain-computer interface (BCI) system due to its zero clinical risk and portable acquisition devices. Current EEG-based BCI research usually requires a subject-specific adaptation step before a BCI can be employed by a new user. In contrast, the subject-independent scenario, where a well trained model can be directly applied to new users without precalibration, is particularly desired. Considering this critical gap, the focus in this letter is developing an effective EEG signal analysis adaptively applied to subject-independent settings. We present a convolutional recurrent attention model (CRAM) that utilizes a convolutional neural network to encode the high-level representation of EEG signals and a recurrent attention mechanism to explore the temporal dynamics of the EEG signals as well as to focus on the most discriminative temporal periods. Extensive experiments on a benchmark multiclass EEG dataset containing four movement intentions indicate that the proposed model is capable of exploiting the underlying invariant EEG patterns across different subjects and generalizing the patterns to new subjects with better performance than a series of state-of-the-art and baseline approaches by at least eight percentage points. The implementation code is made publicly available.11https://github.com/dalinzhang/CRAM.

Original languageEnglish
Article number8675451
JournalIEEE Signal Processing Letters
Volume26
Issue number5
Pages (from-to)715-719
Number of pages5
ISSN1070-9908
DOIs
Publication statusPublished - May 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1994-2012 IEEE.

Keywords

  • attention model
  • deep learning
  • EEG
  • movement intention
  • subject-independent

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