TY - GEN
T1 - EEG VMamba
T2 - 6th International Conference on Video, Signal and Image Processing, VSIP 2024
AU - Deng, Qi
AU - Wang, Qun
AU - Liu, Weicheng
AU - Xue, Yu
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2025/2/27
Y1 - 2025/2/27
N2 - Epileptic seizure prediction algorithms based on EEG signals can help epilepsy patients take timely measures to avoid risks. However, EEG signals possess high dimensionality, nonlinearity, and strong temporal dependencies, making it difficult for models to integrate global and local features and capture long-term dependencies. To address these issues, we propose EEG VMamba, which use the Visual State Space (VSS) block as the backbone and introduce Convolutional Neural Network (CNN) at the later stage. This approach fully combining the local perception capability of CNN and the global modeling capability of VSS. It was evaluated on the publicly available CHB-MIT dataset to demonstrate its effectiveness in seizure prediction, achieving a sensitivity of 91.1% and an AUC of 0.914. Compared to the seizure prediction method based on Vision Transformer, the EEG VMamba demonstrates superior performance, evidenced by higher sensitivity and AUC score.
AB - Epileptic seizure prediction algorithms based on EEG signals can help epilepsy patients take timely measures to avoid risks. However, EEG signals possess high dimensionality, nonlinearity, and strong temporal dependencies, making it difficult for models to integrate global and local features and capture long-term dependencies. To address these issues, we propose EEG VMamba, which use the Visual State Space (VSS) block as the backbone and introduce Convolutional Neural Network (CNN) at the later stage. This approach fully combining the local perception capability of CNN and the global modeling capability of VSS. It was evaluated on the publicly available CHB-MIT dataset to demonstrate its effectiveness in seizure prediction, achieving a sensitivity of 91.1% and an AUC of 0.914. Compared to the seizure prediction method based on Vision Transformer, the EEG VMamba demonstrates superior performance, evidenced by higher sensitivity and AUC score.
KW - CNN
KW - EEG signals
KW - VSS
KW - seizure prediction
UR - https://www.scopus.com/pages/publications/105001548062
U2 - 10.1145/3708568.3708588
DO - 10.1145/3708568.3708588
M3 - Conference contribution
AN - SCOPUS:105001548062
T3 - Proceedings of the 2024 6th International Conference on Video, Signal and Image Processing, VSIP 2024
SP - 119
EP - 124
BT - Proceedings of the 2024 6th International Conference on Video, Signal and Image Processing, VSIP 2024
PB - Association for Computing Machinery, Inc
Y2 - 22 November 2024 through 24 November 2024
ER -