Wenhao Rao

MFMSRNet: An Interpretable Multi-frequency and Multi-scale Riemannian Network for Motor Imagery Decoding

Motor imagery (MI) electroencephalography (EEG) decoding has benefited from deep learning, yet methods operate in Euclidean space and behave as opaque black boxes, neglecting the intrinsic geometry of functional brain connectivity. EEG connectivity descriptors, such as phase synchrony and covariance