Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals

Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals

It is important to classify electroencephalography (EEG) signals automatically for the diagnosis and treatment of epilepsy. Currently, the dominant single-modal feature extraction methods cannot cover the information of different modalities, resulting in poor classification performance of existing m...

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Bibliographic Details
Main Authors: Ning Huang, Zhengtao Xi, Yingying Jiao, Yudong Zhang, Zhuqing Jiao, Xiaona Li
Format: Article
Language:English
Published: AIMS Press 2024-08-01
Series:Mathematical Biosciences and Engineering
Subjects:
epilepsy
electroencephalogram
feature fusion
multi-modal feature fusion
multi-head self-attention mechanism
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2024304
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https://www.aimspress.com/article/doi/10.3934/mbe.2024304

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