Emotion Recognition from DEAP: Feature Engineering & Baselines
Published:
Preprint codebase for DEAP EEG emotion recognition with multiple handcrafted features and CNN models. 
Published:
Preprint codebase for DEAP EEG emotion recognition with multiple handcrafted features and CNN models. 
Published:
Prototype smart classroom system combining voice, gesture, and face recognition for automated attendance and control.
Published:
Pilot implementation for real-world EEG emotion recognition using compact deep models on SEED-like setups.
Published:
End-to-end pipeline for time–frequency feature extraction and 3D-CNN based emotion recognition on DEAP and SEED EEG datasets.

Published:
Geometry-preserving layers for learning on the SPD manifold — equivariant mapping, Riemannian pooling, geometric bias, and geodesic attention — released as the open-source package egnlib (pip install egnlib).