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EEG-based Deep Emotional Diagnosis: A Comparative Study

Liu, Geyi; Zhang, Zhaonian; Jiang, Richard; Crookes, Danny; Chazot, Paul

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Authors

Geyi Liu

Zhaonian Zhang

Richard Jiang

Danny Crookes



Contributors

Richard Jiang
Editor

Li Zhang
Editor

Hua-Liang Wei
Editor

Danny Crookes
Editor

Abstract

Emotion is an important part of people's daily life, particularly relevant to the mental health of people. Emotional diagnosis is closely related to the nervous system, which can well reflect people's mental conditions in response to the surrounding environment or the development of various neurodegenerative diseases. Emotion recognition can help the medical diagnosis of mental health. In recent years, emotion recognition based on EEG has attracted the attention of many researchers accompanying with the continuous development of artificial intelligence and brain computer interface technology. In this paper, we carried out a comparison on the performance of three deep learning techniques on EEG classification, including DNN, CNN and CNN-LSTM. DEAP data set was used in our experiments. EEG signals were transformed from time domain to frequency domain first, and then features are extracted to classify emotions. From our research, it shows these deep learning techniques can achieve good accuracy on emotional diagnosis.

Citation

Liu, G., Zhang, Z., Jiang, R., Crookes, D., & Chazot, P. (2022). EEG-based Deep Emotional Diagnosis: A Comparative Study. In R. Jiang, L. Zhang, H. Wei, D. Crookes, & P. Chazot (Eds.), Recent Advances in AI-enabled Automated Medical Diagnosis. Routledge. https://doi.org/10.1201/9781003176121

Online Publication Date Oct 20, 2022
Publication Date 2022
Deposit Date Jul 15, 2022
Publicly Available Date Oct 21, 2023
Publisher Routledge
Edition 1st ed.
Book Title Recent Advances in AI-enabled Automated Medical Diagnosis
ISBN 9781032008431
DOI https://doi.org/10.1201/9781003176121
Public URL https://durham-repository.worktribe.com/output/1621580

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