Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164529
Title: Tensor-CSPNet: a novel geometric deep learning framework for motor imagery classification
Authors: Ju, Ce
Guan, Cuntai
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Ju, C. & Guan, C. (2022). Tensor-CSPNet: a novel geometric deep learning framework for motor imagery classification. IEEE Transactions On Neural Networks and Learning Systems, 1-15. https://dx.doi.org/10.1109/TNNLS.2022.3172108
Project: A20G8b0102 
Journal: IEEE Transactions on Neural Networks and Learning Systems 
Abstract: Deep learning (DL) has been widely investigated in a vast majority of applications in electroencephalography (EEG)-based brain-computer interfaces (BCIs), especially for motor imagery (MI) classification in the past five years. The mainstream DL methodology for the MI-EEG classification exploits the temporospatial patterns of EEG signals using convolutional neural networks (CNNs), which have been particularly successful in visual images. However, since the statistical characteristics of visual images depart radically from EEG signals, a natural question arises whether an alternative network architecture exists apart from CNNs. To address this question, we propose a novel geometric DL (GDL) framework called Tensor-CSPNet, which characterizes spatial covariance matrices derived from EEG signals on symmetric positive definite (SPD) manifolds and fully captures the temporospatiofrequency patterns using existing deep neural networks on SPD manifolds, integrating with experiences from many successful MI-EEG classifiers to optimize the framework. In the experiments, Tensor-CSPNet attains or slightly outperforms the current state-of-the-art performance on the cross-validation and holdout scenarios in two commonly used MI-EEG datasets. Moreover, the visualization and interpretability analyses also exhibit the validity of Tensor-CSPNet for the MI-EEG classification. To conclude, in this study, we provide a feasible answer to the question by generalizing the DL methodologies on SPD manifolds, which indicates the start of a specific GDL methodology for the MI-EEG classification.
URI: https://hdl.handle.net/10356/164529
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2022.3172108
Schools: School of Computer Science and Engineering 
Research Centres: S-Lab
Rights: © 2022 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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