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

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Library of geometry-preserving layers and losses for learning on Riemannian manifolds, including SPD-valued features.