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Learning with covariate shift-detection and adaptation in non-stationary environments: Application to brain-computer interface.

, , , and . IJCNN, page 1-8. IEEE, (2015)

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Optimising frequency band selection with forward-addition and backward-elimination algorithms in EEG-based brain-computer interfaces., , and . IJCNN, page 1-7. IEEE, (2015)Covariate shift-adaptation using a transductive learning model for handling non-stationarity in EEG based brain-computer interfaces., , , and . BIBM, page 230-236. IEEE Computer Society, (2014)Dataset Shift Detection in Non-stationary Environments Using EWMA Charts., , and . SMC, page 3151-3156. IEEE, (2013)Online Covariate Shift Detection-Based Adaptive Brain-Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation., , , , and . IEEE Trans. Cognitive and Developmental Systems, 10 (4): 1070-1080 (2018)A study on cortico-muscular coupling in finger motions for exoskeleton assisted neuro-rehabilitation., , , , , and . EMBC, page 4610-4614. IEEE, (2015)Adaptive learning with covariate shift-detection for non-stationary environments., , and . UKCI, page 1-8. IEEE, (2014)Adaptive learning with covariate shift-detection for motor imagery-based brain-computer interface., , , and . Soft Comput., 20 (8): 3085-3096 (2016)Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability., , , , , , , , , and . IEEE J. Biomedical and Health Informatics, 22 (6): 1786-1795 (2018)EEG-EMG based Hybrid Brain Computer Interface for Triggering Hand Exoskeleton for Neuro-Rehabilitation., , , and . AIR, page 45:1-45:6. ACM, (2017)Frequency Profile Improvement of a Microgrid through Aggregated Demand Response., , , and . TENCON, page 1584-1588. IEEE, (2018)