EEG-based emotion recognition systems face significant challenges when deployed across different domains due to limited labeled data and individual signal variability. We introduce …
RML has three papers accepted to IEEE EMBC 2026 in Toronto, covering multilingual emotion recognition, weakly-supervised breast lesion segmentation, and facial expression synthesis …
Niki Sheibani and Dr. Khan present PC-SSL at ICASSP 2026 — a predictive coding self-supervised learning framework for EEG-based emotion recognition, achieving state-of-the-art …
We introduce a predictive coding self-supervised learning (PC-SSL) approach to overcome the limitations of traditional supervised methods hindered by the high cost of labeling EEG …
Vision transformers have shown tremendous success in numerous computer vision applications; however, they have not been exploited for stress assessment using physiological signals …
We propose an enhanced unsupervised domain adaptation framework for EEG-based emotion recognition that achieves robust cross-dataset generalization without requiring target-domain …
RML presents three papers at the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
We propose a multilevel stress assessment framework that fuses ECG signals with contextual data from a virtual reality environment. The system classifies multiple levels of …
Emotion Recognition and Stress Assessment using Multimodal Signals for therapy and training
Dr. Khan receives $400K NSERC Alliance grant with Wellwave Inc. to develop therapeutic VR games with multimodal stress assessment for children.