Kai Keng Ang — accepted AI-conference papers

2025

CAT-Net: A Co-Adaptive Transfer Learning Network for BCI-Assisted Neurorehabilitation

Shuailei Zhang, Yi Ding, Muyun Jiang, Ning Tang, Effie Chew, Kai Keng Ang +1

ICASSP 2025accepted

Brain-computer interfaces (BCIs) hold great potential for motor recovery in post-stroke patients. However, the motor imagery decoding accuracy is limited by the non-stationarity of EEG signals across subjects and sessions. We propose CAT-Net: a Co-Adaptive Transfer learning network to simultaneously…

Cited by 0 Source ↗Scholar ↗

2019

A Subject-to-subject Transfer Learning Framework Based on Jensen-Shannon Divergence for Improving Brain-computer Interface

Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh

ICASSP 2019accepted

One of the major limitations of current electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the long calibration time. Due to a high level of noise and non-stationarity inherent in EEG signals, a calibration model trained using limited number of train data may not yield an accurate…

Cited by 0 Source ↗Scholar ↗