A Dual-Branch Spatio-Temporal-Spectral Transformer Feature Fusion Network for EEG-Based Visual Recognition

Luo, Jie; Cui, Weigang; Xu, Sheng; Wang, Lina; Xiao, Li; Liao, Xiaofeng; Li, Yang

doi:10.1109/tii.2023.3280560

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2024

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Cited by 3 publications

References 41 publications

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AdaptiveGait: adaptive feature fusion network for gait recognition

Liang,

Chen,

Liu

et al. 2024

Multimed Tools Appl

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AdaptiveGait: adaptive feature fusion network for gait recognition

Liang,

Chen,

Liu

et al. 2024

Multimed Tools Appl

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Learning Exemplar Representations in Single-Trial EEG Category Decoding

Kilgallen,

Pearlmutter,

Siskind

2024

2024 35th Irish Signals and Systems Conference (ISSC)

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A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks

Luo,

Cui,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is a promising target detection technique by using electroencephalogram (EEG) signals. However, existing deep learning approaches seldom considered dependencies of multi-scale temporal features and discriminative multi-view spectral features simultaneously, which limits the representation learning ability of the model and undermine the EEG classification performance. In addition, recent transfer learning-based methods generally failed to obtain transferable cross-subject invariant representations and commonly ignore the individualspecific information, leading to the poor cross-subject transfer performance. In response to these limitations, we propose a cross-scale Transformer and triple-view attention based domain-rectified transfer learning (CST-TVA-DRTL) for the RSVP classification. Specially, we first develop a cross-scale Transformer (CST) to extract multi-scale temporal features and exploit the dependencies of different scales features. Then, a triple-view attention (TVA) is designed to capture spectral features from triple views of multi-channel timefrequency images. Finally, a domain-rectified transfer learning (DRTL) framework is proposed to simultaneously obtain transferable domain-invariant representations and untransferable domain-specific representations, then utilize domain-specific information to rectify domain-invariant representations to adapt to target data. Experimental results on two public RSVP datasets suggests that our CST-TVA-DRTL outperforms the state-of-the-art methods in the RSVP classification task. The source code of our model is publicly available in https://github.com/ljbuaa/CST_TVA_DRTL.

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A Dual-Branch Spatio-Temporal-Spectral Transformer Feature Fusion Network for EEG-Based Visual Recognition

Cited by 3 publications

References 41 publications

AdaptiveGait: adaptive feature fusion network for gait recognition

AdaptiveGait: adaptive feature fusion network for gait recognition

Learning Exemplar Representations in Single-Trial EEG Category Decoding

A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks

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