ArticleFrontiers in neurorobotics2025
AMEEGNet: attention-based multiscale EEGNet for effective motor imagery EEG decoding.
Article in Frontiers in neurorobotics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- EEG Feature Extraction and Classification for Upper Limb Flexion and Extension Motor Imagery Based on Discriminative Filter Bank Common Spatial Pattern.Brain sciences · 2026Article
- The brain networks indices associated with the human perception of comfort in static force exertion tasks.Frontiers in neuroergonomics · 2025Article
- Transfer learning for subject-independent motor imagery EEG classification using convolutional relational networks.Frontiers in neuroscience · 2025Article
- Lobe-wise cognitive load detection using empirical Fourier decomposition and optimized machine learning.Frontiers in physiology · 2025Article
- Deep learning approaches for EEG-based healthcare applications: a comprehensive review.Frontiers in human neuroscience · 2025Review
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6 authors.
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Abstract
Recently, electroencephalogram (EEG) based on motor imagery (MI) have gained significant traction in brain-computer interface (BCI) technology, particularly for the rehabilitation of paralyzed patients. But the low signal-to-noise ratio of MI EEG makes it difficult to decode effectively and hinders the development of BCI. In this paper, a method of attention-based multiscale EEGNet (AMEEGNet) was proposed to improve the decoding performance of MI-EEG. First, three parallel EEGNets with fusion transmission method were employed to extract the high-quality temporal-spatial feature of EEG data from multiple scales. Then, the efficient channel attention (ECA) module enhances the acquisition of more discriminative spatial features through a lightweight approach that weights critical channels. The experimental results demonstrated that the proposed model achieves decoding accuracies of 81.17, 89.83, and 95.49% on BCI-2a, 2b and HGD datasets. The results show that the proposed AMEEGNet effectively decodes temporal-spatial features, providing a novel perspective on MI-EEG decoding and advancing future BCI applications.
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