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Unsupervised adaptive transfer learning for Steady-State Visual Evoked Potential brain-computer interfaces.

, , , , and . SMC, page 4135-4140. IEEE, (2016)

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Clustering Brain-Network Time Series by Riemannian Geometry., , , , , , , and . IEEE Trans. Signal and Information Processing over Networks, 4 (3): 519-533 (2018)Controllability of structural brain networks, , , , , , , , , and 1 other author(s). Nature Communications, (Oct 1, 2015)Unsupervised adaptive transfer learning for Steady-State Visual Evoked Potential brain-computer interfaces., , , , and . SMC, page 4135-4140. IEEE, (2016)Compositional measures of diffusion anisotropy and asymmetry., , , , , , , and . ISBI, page 123-126. IEEE, (2018)Scale-specific dynamics of high-amplitude bursts in EEG capture behaviorally meaningful variability., , , , , and . NeuroImage, (2021)Stimulation-based control of dynamic brain networks., , , , , , and . CoRR, (2016)Local White Matter Architecture Defines Functional Brain Dynamics., , , , and . SMC, page 595-602. IEEE, (2018)A bag-of-words model for task-load prediction from EEG in complex environments., , , , , , , , and . ICASSP, page 1227-1231. IEEE, (2013)Clustering brain-network-connectivity states using kernel partial correlations., , , , , , , and . ACSSC, page 268-272. IEEE, (2016)Classifying High-Noise EEG in Complex Environments for Brain-Computer Interaction Technologies., , , , , , , and . ACII (2), volume 6975 of Lecture Notes in Computer Science, page 467-476. Springer, (2011)