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<p>Mental health monitoring through emotion recognition plays an important role in early intervention and personalized healthcare systems. Traditional EEG-based emotion recognition approaches have encountered significant limitations, including heavy reliance on manual feature engineering, poor generalization across datasets, and computational complexity that restricts real-world deployment. This study introduces a novel hybrid dual-branch deep learning architecture that integrates temporal and spectral feature extraction for robust EEG-based emotion recognition, while minimizing preprocessing requirements. The proposed framework integrates a Long Short-Term Memory (LSTM) network to capture temporal dependencies directly from raw EEG signals, while concurrently leveraging Convolutional Neural Networks (CNNs) to extract spatial features from Mel-Frequency Cepstral Coefficients (MFCC) representations. This architecture further incorporates innovative cross-modality enhancement mechanisms, such as inverse MFCC computation and LSTM-to-MFCC projection, which facilitate bidirectional feature learning between temporal and spectral domains. Subsequently, feature fusion is achieved through element-wise multiplication and concatenation, and the integrated representations are classified using an Artificial Neural Network (ANN). The evaluation has been conducted on three benchmark datasets: Brainwave EEG, WESAD, and SWELL, achieving remarkable performance with accuracies of 96.49%, 99.99%, and 99.99%, respectively. The model has attained perfect precision, recall, and F1-scores on the WESAD and SWELL datasets, setting new state-of-the-art standards. Comparative analysis has demonstrated the method’s superiority over existing machine learning and deep learning techniques, while preserving computational efficiency that supports real-time applications. These results have confirmed the framework’s strong potential for practical use in mental health monitoring and affective computing.&#xa0;The source code of this work is available at:&#xa0;<a href="https://github.com/DebamSahaCS/Dual-Branch-DL-Framework">https://github.com/DebamSahaCS/Dual-Branch-DL-Framework</a></p>

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A dual-branch deep learning framework for emotion recognition from EEG signals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月11日

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  • Debam Saha,
  • Asfak Ali,
  • Vycheslav Gulvanskii,
  • Dmitrii Kaplun,
  • Ram Sarkar

摘要

Mental health monitoring through emotion recognition plays an important role in early intervention and personalized healthcare systems. Traditional EEG-based emotion recognition approaches have encountered significant limitations, including heavy reliance on manual feature engineering, poor generalization across datasets, and computational complexity that restricts real-world deployment. This study introduces a novel hybrid dual-branch deep learning architecture that integrates temporal and spectral feature extraction for robust EEG-based emotion recognition, while minimizing preprocessing requirements. The proposed framework integrates a Long Short-Term Memory (LSTM) network to capture temporal dependencies directly from raw EEG signals, while concurrently leveraging Convolutional Neural Networks (CNNs) to extract spatial features from Mel-Frequency Cepstral Coefficients (MFCC) representations. This architecture further incorporates innovative cross-modality enhancement mechanisms, such as inverse MFCC computation and LSTM-to-MFCC projection, which facilitate bidirectional feature learning between temporal and spectral domains. Subsequently, feature fusion is achieved through element-wise multiplication and concatenation, and the integrated representations are classified using an Artificial Neural Network (ANN). The evaluation has been conducted on three benchmark datasets: Brainwave EEG, WESAD, and SWELL, achieving remarkable performance with accuracies of 96.49%, 99.99%, and 99.99%, respectively. The model has attained perfect precision, recall, and F1-scores on the WESAD and SWELL datasets, setting new state-of-the-art standards. Comparative analysis has demonstrated the method’s superiority over existing machine learning and deep learning techniques, while preserving computational efficiency that supports real-time applications. These results have confirmed the framework’s strong potential for practical use in mental health monitoring and affective computing. The source code of this work is available at: https://github.com/DebamSahaCS/Dual-Branch-DL-Framework