ArticleFrontiers in neuroscience2023
A learnable EEG channel selection method for MI-BCI using efficient channel attention.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Time-frequency-spatial channel attention network for semantic decoding: an exploratory EEG study.Medical & biological engineering & computing · 2026Article
- Distance-based temporal similarity metrics for adaptive channel selection in multi-modal EEG-fNIRS BCI frameworks.Scientific reports · 2026Article
- Efficient EEG-Based Person Identification: A Unified Framework from Automatic Electrode Selection to Intent Recognition.Sensors (Basel, Switzerland) · 2026Article
- MCFANet: a multi-class fusion attention network for motor imagery EEG classification.Frontiers in human neuroscience · 2026Article
- Recent applications of EEG-based brain-computer-interface in the medical field.Military Medical Research · 2025Review
- Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System.Sensors (Basel, Switzerland) · 2024Article
- Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.Frontiers in neuroscience · 2024Article
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Abstract
Introduction: During electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy. Methods: This paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a. Results and discussion: The proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.
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