Advancing BCI with a transformer-based model for motor imagery classification

Advancing BCI with a transformer-based model for motor imagery classification

Abstract Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such...

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Bibliographic Details
Main Authors: Wangdan Liao, Hongyun Liu, Weidong Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Motor imagery (MI)
Electroencephalography (EEG)
Classification
Transformer
Temporal Convolutional Networks (TCNs)
Online Access:https://doi.org/10.1038/s41598-025-06364-4
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https://doi.org/10.1038/s41598-025-06364-4

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