Deep learning for electroencephalography emotion recognition

Pourrostami H. AlyanNezhadi M.M. Nazari M. Band S.S. Mosavi A.
2025 American Institute of Mathematical Sciences

AIMS Public Health
2025 #12 Issue 3 812 - 834 pp.

This study presents an Electroencephalography (EEG) emotion recognition using a long short-term memory (LSTM)-based method. Our proposed method selects window sizes and overlaps to divide the EEG data into segments, which optimally captures subtle signal changes. A Bidirectional LSTM (BiLSTM) layer is added to standard LSTM layers to better detect forward and backward patterns in the data. By using this dual-layer setup, we aim to improve both the feature extraction and the classification accuracy. The model was tested on the Database for Emotion Analysis using Physiological signals (DEAP) dataset and showed acceptable accuracy across emotional dimensions: arousal (94.0%), liking (98.9%), dominance (95.3%), and valence (99.6%). Our results suggest that the model better supports emotion recognition and has potential for mental health monitoring and adaptive therapy.

applied AI , artificial intelligence , big data , data science , electroencephalography , emotion recognition , human-computer interaction , machine learning

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Department of Computer Science, University of Science and Technology of Mazandaran, Behshahr, Iran
Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
John Von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
Institute of the Information Society, Ludovika University of Public Service, Budapest, Hungary
Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
Faculty of Economics and Informatics, Univerzita J. Selyeho Komarom, Slovakia

Department of Computer Science
Department of Information Management
John Von Neumann Faculty of Informatics
Institute of the Information Society
Abylkas Saginov Karaganda Technical University
Faculty of Economics and Informatics

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