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This study looks into how brain signals, recorded using EEG (electroencephalogram) technology, can help identify human emotions through deep learning. It focuses on three emotional states-negative, neutral, and positive-and examines how certain patterns in brain activity can reflect how we feel. To analyze this, the research uses Long Short-Term Memory (LSTM) networks, which are well-suited for understanding time-based data like EEG signals. The model is carefully designed to pick up on meaningful patterns, reduce the risk of overfitting, and improve how accurately it can classify emotional states. Before training the model, the EEG data goes through detailed preparation, including cleaning, labeling, and splitting into training and testing sets. Training is carried out using efficient methods that help save on resources and avoid unnecessary computations. The study points to real-world use cases-such as tools for monitoring mental well-being or creating technology that responds to users’ emotions. Overall, it shows how deep learning and EEG data together can deepen our understanding of emotional states.

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Decoding Emotions: Using LSTM Neural Networks for EEG-Based Emotion Recognition

  • 会议论文

  • 发表日期：

    2026年

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  • Ramesh M. Tirakanagoudar,
  • Lavanya Joshi,
  • Sujay Badiger,
  • Satish Chikkamath,
  • Suneeta V. Budihal,
  • Sujata Kotabagi

摘要

This study looks into how brain signals, recorded using EEG (electroencephalogram) technology, can help identify human emotions through deep learning. It focuses on three emotional states-negative, neutral, and positive-and examines how certain patterns in brain activity can reflect how we feel. To analyze this, the research uses Long Short-Term Memory (LSTM) networks, which are well-suited for understanding time-based data like EEG signals. The model is carefully designed to pick up on meaningful patterns, reduce the risk of overfitting, and improve how accurately it can classify emotional states. Before training the model, the EEG data goes through detailed preparation, including cleaning, labeling, and splitting into training and testing sets. Training is carried out using efficient methods that help save on resources and avoid unnecessary computations. The study points to real-world use cases-such as tools for monitoring mental well-being or creating technology that responds to users’ emotions. Overall, it shows how deep learning and EEG data together can deepen our understanding of emotional states.