Transfer Learning

Inter-and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-based BCIs

Author(s): Chi Man Wong, Ze Wang, Boyu Wang, Ka Fai Lao, Agostinho Rosa, Peng Xu, Tzyy-Ping Jung, C. L. Philip Chen, Feng Wan
Inter-and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-based BCIs 1000 920 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that can deliver high information transfer rate (ITR) usually require subject’s calibration data to learn the class-and subject-specific model parameters (e.g. the spatial filters and SSVEP templates). Normally, the amount of the calibration data for learning is proportional to the number of classes (or visual stimuli), which could be huge and consequently lead to a time-consuming calibration. read more

Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces

Author(s): Wen Zhang, Dongrui Wu
Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces 1281 545 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Transfer learning makes use of data or knowledge in one problem to help solve a different, yet related, problem. It is particularly useful in brain-computer interfaces (BCIs), for coping with…

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A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

Author(s): Stanislas Chambon, Mathieu N. Galtier, Pierrick J. Arnal, Gilles Wainrib, Alexandre Gramfort
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series 780 364 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

     Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of…

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