Transfer Learning

Improved Transfer Learning for Detecting Upper-Limb Movement Intention Using Mechanical Sensors in an Exoskeletal Rehabilitation System
Improved Transfer Learning for Detecting Upper-Limb Movement Intention Using Mechanical Sensors in an Exoskeletal Rehabilitation System 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
The objective of this study was to propose a novel strategy for detecting upper-limb motion intentions from mechanical sensor signals using deep and heterogeneous transfer learning techniques. Three sensor types,… read more

From Simulation to Reality: Predicting Torque With Fatigue Onset via Transfer Learning
From Simulation to Reality: Predicting Torque With Fatigue Onset via Transfer Learning 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Muscle fatigue impacts upper extremity function but is often overlooked in biomechanical models. The present work leveraged a transfer learning approach to improve torque predictions during fatiguing upper extremity movements.… read more

Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data
Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultural differences affecting… read more

Inter-and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-based BCIs
Author(s)3: 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)3: 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)3: 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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