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.
Papers
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Journal ArticleDOI
Brain-Computer Interface in Stroke Rehabilitation
Kai Keng Ang,Cuntai Guan +1 more
TL;DR: This paper reviews the most recent works of BCI in stroke rehabilitation with a focus on methodology that reports on data collected from stroke patients and clinical studies that reported on the motor improvements of stroke patients.
Journal ArticleDOI
Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs
TL;DR: A novel statistical method to automatically select the optimal subject-specific time segment and temporal frequency band based on the mutual information between the spatial-temporal patterns from the EEG signals and the corresponding neuronal activities and its one-versus-rest multi-class extension was presented.
Proceedings ArticleDOI
On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification
TL;DR: The results demonstrate that the CNNs are capable of learning discriminant, deep structure features for EEG classification without relying on the handcrafted features.
Journal ArticleDOI
Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI.
TL;DR: This framework significantly improves attention detection accuracy with inter-subject classification and is capable of learning from raw data with the least amount of pre-processing, which in turn eliminates the extensive computational load of time-consuming data preparation and feature extraction.
Proceedings ArticleDOI
A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation
Kai Keng Ang,Cuntai Guan,Karen Sui Geok Chua,Beng Ti Ang,Christopher Wee Keong Kuah,Chuanchu Wang,Kok Soon Phua,Zheng Yang Chin,Haihong Zhang +8 more
TL;DR: Evidence is provided that BCI-driven robotic rehabilitation is effective in restoring motor control for stroke by investigating the effects of MI-BCI for upper limb robotic rehabilitation compared to standard robotic rehabilitation.