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EEG Emotion Recognition with Deep CNNs by Wei Li is a document available to read on EtoBox.

This document summarizes research on using deep learning models for EEG-based emotion recognition. Specifically: - It proposes using a deep convolutional neural network to classify emotions based on temporal, frequential, and combined features extracted from EEG data. - Experimental results on the DEAP dataset show the deep CNN models achieved the best recognition performance on combined temporal and frequency features for both valence and arousal, with an accuracy improvement of 3.58% for valence and 3.

Author

Wei Li

Language

EN