research-article

TACNet: Task-aware Electroencephalogram Classification for Brain-Computer Interface through A Novel Temporal Attention Convolutional Network

Authors:

Dezhi ZhengAuthors Info & Claims

UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers

Pages 660 - 665

https://doi.org/10.1145/3460418.3480411

Published: 24 September 2021 Publication History

UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers

TACNet: Task-aware Electroencephalogram Classification for Brain-Computer Interface through A Novel Temporal Attention Convolutional Network

Pages 660 - 665

Abstract
References

Abstract

Electroencephalogram (EEG) based brain-computer interface (BCI) has emerged as a promising tool for communication and control. Temporal non-stationarity of the signal is one of the critical challenges faced by motor imagery (MI) classification for EEG based BCI. To address this challenge, this paper proposes a novel temporal attention convolutional network (TACNet) for MI classification. By combining two types of sub-networks through attention mechanisms, TACNet can selectively focus on valuable time slices of the signal to obtain task-related information. In TACNet architecture, a global sub-network is applied to the entire time horizon and guides the attention mechanism to select a few time slices to apply the local sub-networks. We compare TACNet with other deep learning models on two EEG datasets: BCI competition IV dataset 2a (BCIC IV 2a) and high gamma dataset (HGD). The results show that our approach achieves significantly better classification accuracies than other baseline models.

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Cited By

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https://dl.acm.org/doi/10.1145/3675095.3676607
Zhang YLi PCheng LLi MLi H(2024)Attention-Based Multiscale Spatial-Temporal Convolutional Network for Motor Imagery EEG DecodingIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333042370:1(2423-2434)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3330423
Chen WWang CXu KYuan YBai YZhang D(2024)D-FaST: Cognitive Signal Decoding With Disentangled Frequency–Spatial–Temporal AttentionIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2024.337026116:4(1476-1493)Online publication date: Aug-2024
https://doi.org/10.1109/TCDS.2024.3370261
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cover image ACM Conferences

UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers

September 2021

711 pages

ISBN:9781450384612

DOI:10.1145/3460418

Copyright © 2021 ACM.

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Published: 24 September 2021

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UbiComp '21: The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 21 - 26, 2021

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Cited By

Bian SKang PMoosmann JLiu MBonazzi PRosipal RMagno MKostakos VKay JHoang T(2024)On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer InterfaceProceedings of the 2024 ACM International Symposium on Wearable Computers10.1145/3675095.3676607(9-16)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675095.3676607
Zhang YLi PCheng LLi MLi H(2024)Attention-Based Multiscale Spatial-Temporal Convolutional Network for Motor Imagery EEG DecodingIEEE Transactions on Consumer Electronics10.1109/TCE.2023.333042370:1(2423-2434)Online publication date: Feb-2024
https://doi.org/10.1109/TCE.2023.3330423
Chen WWang CXu KYuan YBai YZhang D(2024)D-FaST: Cognitive Signal Decoding With Disentangled Frequency–Spatial–Temporal AttentionIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2024.337026116:4(1476-1493)Online publication date: Aug-2024
https://doi.org/10.1109/TCDS.2024.3370261
Saibene AGhaemi HDagdevir E(2024)Deep learning in motor imagery EEG signal decoding: A Systematic ReviewNeurocomputing10.1016/j.neucom.2024.128577610(128577)Online publication date: Dec-2024
https://doi.org/10.1016/j.neucom.2024.128577
Shimizu DWatanabe KDengel A(2024)Feature Estimation of Global Language Processing in EEG Using Attention MapsComputer Vision – ACCV 202410.1007/978-981-96-0901-7_6(88-103)Online publication date: 8-Dec-2024
https://doi.org/10.1007/978-981-96-0901-7_6
Siddiqui ASanyal SSelvanambi R(2024)Visualizing Optimal Classifiers in EEG-Based Sleepy Driver PredictionAdvanced Network Technologies and Intelligent Computing10.1007/978-3-031-64070-4_4(59-83)Online publication date: 8-Aug-2024
https://doi.org/10.1007/978-3-031-64070-4_4
Sun YLiu XNa RWang SZheng DFan S(2023)Cross-domain Feature Distillation Framework for Enhancing Classification in Ear-EEG Brain-Computer InterfacesAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3612911(706-711)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3612911
Li JGe CTao JWang JXu XChen XGui WLiang XDing W(2023)SolareSkin: Self-powered Visible Light Sensing Through a Solar Cell E-SkinAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3612904(664-669)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3612904
Dos Santos DDe Souza GBernardino HVieira AMotta L(2023)Residual Attention Module on EEGN et for Brain-Computer Interface2023 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI52147.2023.10371842(58-63)Online publication date: 5-Dec-2023
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Yağan MMusellim SArslan SÇakar TAlp NOzkan H(2023)A new benchmark dataset for P300 ERP-based BCI applicationsDigital Signal Processing10.1016/j.dsp.2023.103950135:COnline publication date: 30-Apr-2023
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