EEG Emotion Recognition Using Multi-Feature DNN with PCA and Multi-Seed Training

Weiguo Zhang

doi:10.47297/taposatWSP2633-456921.20260704

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EEG Emotion Recognition Using Multi-Feature DNN with PCA and Multi-Seed Training

Volume 7, Issue 4

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EEG Emotion Recognition Using Multi-Feature DNN with PCA and Multi-Seed Training
EEG Emotion Recognition Using Multi-Feature DNN with PCA and Multi-Seed Training
School of Artificial Intelligence, Zhejiang Dongfang Polytechnic, Wenzhou, Zhejiang, 325000. China
Vol. 7, Issue 4, Pages: 116-119(2026)
Published：30 April 2026
DOI：10.47297/taposatWSP2633-456921.20260704
Accepted：

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Zhang Weiguo.EEG Emotion Recognition Using Multi-Feature DNN with PCA and Multi-Seed Training[J].Theory and Practice of Science and Technology,2026,07(04):116-119.
DOI：

Zhang Weiguo.EEG Emotion Recognition Using Multi-Feature DNN with PCA and Multi-Seed Training[J].Theory and Practice of Science and Technology,2026,07(04):116-119. DOI： 10.47297/taposatWSP2633-456921.20260704.

摘要

Abstract

EEG-based emotion recognition is crucial for affective computing and human-computer interaction. This study proposes a deep neural network framework using multi-domain statistical features—including mean

variance

standard deviation

kurtosis

skewness

and zero-crossing rate—combined with PCA for dimensionality reduction and multi-seed training for robustness. Evaluated on a 32-channel EEG dataset

the model achieved an average accuracy of 76.31% across three runs

with a peak accuracy of 77.49%. The approach demonstrates reliable and generalizable performance

providing a practical solution for real-world EEG emotion recognition applications.

关键词

Keywords

references

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