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Advancing Brain–Computer Interfaces for Rehabilitation and Assistive Technologies

Motor imagery electroencephalography (EEG) signals depict changes in brain activity during imagined limb movements. Conventional methods, however, often fail to capture these spatiotemporal variations. Researchers from Chiba University have developed a novel Embedding-Driven Graph Convolutional Network that can decode the spatiotemporal heterogeneity in EEG signals, advancing brain-computer interface technologies.
Image credit: Professor Akio Namiki from Chiba University, Japan
Image source link: https://www.sciencedirect.com/science/article/pii/S1566253526000497?via%3Dihub

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