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July 30, 2025International Journal on Robotics Automation and Sciences

EEG-Based Emotion Recognition Using CNN-LSTM: Dynamic Segmentation and Feature Fusion

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Authors

NTNazia TabraizRiphah International UniversitySJSadia Abdul JabarRiphah International UniversityJIJawaid IqbalRiphah International University

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Implication

Observational analysis enhances emotion recognition accuracy in eeg data, suggesting faster real-time applications.

Key Points

  • Improved emotion recognition accuracy is achieved with real-time techniques and fusion in EEG-based systems, enhancing user interaction.
  • Deep learning models like CNNs and LSTMs have reached accuracy levels of 98%, addressing needs in adaptive interfaces and mental health applications.
  • Dynamic segmentation and feature fusion are key methodologies employed to boost speed and reliability in emotion detection.
  • The growing significance of EEG systems highlights necessary ethical considerations and the importance of dataset diversity for better outcomes.

Cite This Study

Tabraiz et al. (2025) studied this question.

synapsesocial.com/papers/68af2690cf1dd9ea359e1e59https://doi.org/10.33093/ijoras.2025.7.2.8
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Harnessing Deep Learning for EEG Emotion Recognition: A Hybrid Approach with Attention Mechanisms2025
  2. 2EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network2026
  3. 3EEG-Based Affective Computing: A Review of Signal Processing Techniques2025
  4. 4EEG-ERnet: Emotion Recognition based on Rhythmic EEG Convolutional Neural Network Model2025 · 2 citations
  5. 5Design of a Computational Model to Detect Hybrid Emotion Through Facial Expressions in Videos Using CNN LSTM2025 · 1 citations