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September 10, 2025Clinical EEG and Neuroscience

BiLSTM-Based Human Emotion Classification Using EEG Signal

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Authors

AKAkhilesh KumarAKArun Kumar

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Overview

This analysis demonstrates BiLSTM’s effectiveness in classifying emotions from EEG data, suggesting new avenues for affective computing.

Key Points

  • The BiLSTM model achieved classification accuracies of 99.98% for SEED-IV and 99.97% for SEED-V, indicating strong performance.
  • Four datasets were used: SEED, SEED-IV, SEED-V, and DEAP, with results underscoring the model's capability for emotion recognition tasks.
  • By leveraging temporal dependencies in EEG signals, the BiLSTM framework shows superior performance in diverse emotion representations.
  • The findings highlight the importance of optimizing frameworks for real-world applications like wearable EEG devices.

Cite This Study

Kumar et al. (2025) studied this question.

synapsesocial.com/papers/68c23e4fb210217d64792144https://doi.org/10.1177/15500594251364017
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  1. 1Harnessing Deep Learning for EEG Emotion Recognition: A Hybrid Approach with Attention Mechanisms2025
  2. 2Decoding Emotions from EEG Responses Elicited by Videos using Machine Learning Techniques2025
  3. 3EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network2026
  4. 4Emotion‐Based Mental State Classification Using <scp>EEG</scp> for Brain‐Computer Interface Applications2025 · 4 citations
  5. 5Explainable EEG Emotion Recognition Based on 4D Attention2025