K
Kai Keng Ang
Researcher at Nanyang Technological University
Publications - 195
Citations - 8986
Kai Keng Ang is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Motor imagery & Brain–computer interface. The author has an hindex of 38, co-authored 184 publications receiving 7046 citations. Previous affiliations of Kai Keng Ang include Institute for Infocomm Research Singapore & Tan Tock Seng Hospital.
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Proceedings ArticleDOI
Calibrating EEG-based motor imagery brain-computer interface from passive movement
TL;DR: EEG data from performing motor imagery are usually collected to calibrate a subject-specific model for classifying the EEG data during the evaluation phase of motor imagery Brain-Computer Interface (BCI), but it is shown that it is feasible to calibrating EEG-based motor imagery BCI from passive movement.
Journal ArticleDOI
Adaptation of motor imagery EEG classification model based on tensor decomposition.
TL;DR: The objective of this paper is to quantify the mismatch between the training model and test data caused by nonstationarity and to adapt the model towards minimizing the mismatch.
Proceedings ArticleDOI
Maximum dependency and minimum redundancy-based channel selection for motor imagery of walking EEG signal detection
TL;DR: This paper proposes a novel method to detect motor imagery of walking for the rehabilitation of stroke patients using the Laplacian derivatives of power averaged across frequency bands as the feature and achieves significant better accuracy for poor BCI performers compared to existing methods.
Journal ArticleDOI
An iterative cross-subject negative-unlabeled learning algorithm for quantifying passive fatigue.
TL;DR: The proposed iterative NU learning algorithm is capable of labelling and quantifying the most fatigued block in a cross-subject manner despite the lack of ground truth in the fatigue levels of unlabeled driving EEG data, and shows promise in the application of the proposed algorithm to detect fatigue from EEG.
Proceedings ArticleDOI
An analysis on driver drowsiness based on reaction time and EEG band power
TL;DR: Overall, this study shows that reaction time can be used to infer the drowsiness, and subject-specific changes in the EEG band power may beused to infer drowsness.