Decoding Human Emotions: Analyzing Multi-channel EEG Data Using LSTM Networks
Published in International Conference on Data Science and Applications (ICDSA 2024), 2024
Emotion recognition from electroencephalogram (EEG) signals is a thriving field, particularly in neuroscience and human-computer interaction (HCI). This study aims to understand and improve the predictive accuracy of emotional state classification through metrics such as valence, arousal, dominance, and likeness by applying a long short-term memory (LSTM) network to analyze EEG signals.
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Emotion recognition from electroencephalogram (EEG) signals is a thriving field, particularly in neuroscience and human-computer interaction (HCI). This study aims to understand and improve the predictive accuracy of emotional state classification through metrics such as valence, arousal, dominance, and likeness by applying a long short-term memory (LSTM) network to analyze EEG signals. Using a popular dataset of multi-channel EEG recordings known as DEAP, we look toward leveraging LSTM networks' properties to handle temporal dependencies within EEG signal data. This allows for a more comprehensive understanding and classification of emotional parameter states. We obtain accuracies of 89.89%, 90.33%, 90.70%, and 90.54% for arousal, valence, dominance, and likeness, respectively, demonstrating significant improvements in emotion recognition model capabilities.
Published in: International Conference on Data Science and Applications Pages: 503-515 Publisher: Springer Nature Singapore Date: July 17, 2024 Citations: 1
Citation: Sateesh, S. K., Sparsh, B. K., & Uma, D. (2024). "Decoding Human Emotions: Analyzing Multi-channel EEG Data Using LSTM Networks." International Conference on Data Science and Applications. Springer Nature Singapore, 503-515.
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