Five-channel EEG-based Emotion Recognition using CNN and LSTM Networks

Document Type : Research Article

Authors

Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

This study presents an emotion recognition approach using five-channel electroencephalogram (EEG) signals analyzed through convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The goal is to achieve robust binary classification (positive vs. negative emotion) using minimal electrode input to reduce computational cost. We employed five electrodes positioned in the prefrontal and temporal regions of the brain to reduce computational complexity and enhance classification accuracy. Using the SEED dataset (SJTU Emotion EEG Dataset), we applied continuous wavelet transform (CWT) to generate 2-D images for the CNN model and wavelet packet transform (WPT) for gamma band extraction in the LSTM model. Our results show an average classification accuracy of 94.57±0.06% for CNN and 74.6% for LSTM. We emphasize that, despite using basic architectures, effective performance was achieved with minimal electrodes. Training time for the CNN model was 3 minutes and for LSTM model was 31 minutes that indicates the robustness of this methodology. This lightweight system has practical applications in emotion-aware technologies, mental health tools, and human-computer interaction.

Keywords

Future Research on AI and IoT
Volume 2, Issue 2
October 2026
Pages 18-29

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History

  • Receive Date: 16 December 2025
  • Revise Date: 27 February 2026
  • Accept Date: 05 June 2026
  • First Publish Date: 05 June 2026
  • Publish Date: 01 June 2026

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