A Spatio-Temporal Interactive Attention Network for Motor Imagery EEG Decoding

Brain-computer interface (BCI) technology can link direct communication paths between human brain and external devices, where tasks of motor imagery (MI) electroencephalogram (EEG) decoding play important roles. Multi-channel electrode montage achieves EEG measurements with high spatial resolution....

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Bibliographic Details
Published in2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) pp. 1 - 6
Main Authors Ma, Yue, Bian, Doudou, Xu, Dongyang, Zou, Wei, Wang, Jiajun, Hu, Nan
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.10.2022
Subjects
attention mechanism
Brain modeling
Convolution
Data models
Decoding
Electrodes
Electroencephalogram (EEG)
Electroencephalography
Feature extraction
functional connectivity
graph convolution
motor imagery (MI)
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Summary:Brain-computer interface (BCI) technology can link direct communication paths between human brain and external devices, where tasks of motor imagery (MI) electroencephalogram (EEG) decoding play important roles. Multi-channel electrode montage achieves EEG measurements with high spatial resolution. In previous studies of MI-EEG decoding, the extracted temporal features of multi-channel EEG measurement data were harnessed to recognize different MI-EEG patterns, while spatial features, especially those manifesting the intrinsic connectivity of EEG channels during different MI tasks, has often been overlooked. In this paper, we propose a spatio-temporal interactive attention network (STIA-Net), which exploits spatial features, temporal features, as well as their interaction, for MI-EEG decoding. Graph convolution is employed for spatial feature manipulation, where functional connectivity with phase locking value (PLV) is involved to establish a graph and hence exhibiting topological structural properties. The temporal features are extracted by dilated temporal convolutions, and spatio-temporal interaction is accomplished via attention mechanism. The STIA-Net utilizes the spatio-temporal feature fusion for ultimate MI-EEG classification. The experimental results demonstrate that the proposed STIA-Net performs well on the PhysioNet MI-EEG dataset, with a subject-independent classification accuracy of 83.9%, higher than state-of-the-art methods.
DOI:10.1109/ICSPCC55723.2022.9984387