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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