Emotion classification based on EEG wavelet features and LSTM network

Jinghong Tian; Xu Luo

doi:10.1117/12.3053037

3 January 2025 Emotion classification based on EEG wavelet features and LSTM network

Jinghong Tian, Xu Luo

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 134420B (2025) https://doi.org/10.1117/12.3053037
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

This study explores an advanced method for emotion classification using electroencephalogram (EEG) data, leveraging the DEAP dataset. The proposed approach combines wavelet transform for feature extraction with long short-term memory (LSTM) neural networks for classification. Initially, the EEG signals were decomposed using fast discrete wavelet transform (DWT) to extract wavelet coefficients from both low-frequency and high-frequency sub-bands. Key statistical features, including the maximum, minimum, mean, standard deviation, energy value, and relative energy value of these coefficients, were computed to form comprehensive feature vectors. These feature vectors were then input into a sophisticated 7-layer LSTM neural network for training and testing. The LSTM network's ability to handle long-term dependencies in sequential data proved highly effective in this context. This study conducted experimental comparisons between single-channel and multi-channel classification performance and explored the impact of different feature component combinations on classification outcomes. The experimental results showed that multi-channel combinations significantly improved classification accuracy, with the best accuracy 95.15% observed in the 8-channel combination scheme. Further analysis revealed that when the feature data contains wavelet components with frequencies between 0 and 8Hz, the classification performance of the network can be significantly improved.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinghong Tian and Xu Luo "Emotion classification based on EEG wavelet features and LSTM network", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 134420B (3 January 2025); https://doi.org/10.1117/12.3053037

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Electroencephalography

Emotion

Wavelets

Neural networks

Feature extraction

Education and training

Video

Show All Keywords

Keywords/Phrases

Search In:

Publication Years