Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915442003542016 |
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| author | Sateesh, Shyam K BK, Sparsh D, Uma |
| author_facet | Sateesh, Shyam K BK, Sparsh D, Uma |
| contents | 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 towards 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. This paper elucidates the methodology and architectural specifics of our LSTM model and provides a benchmark analysis with existing papers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10328 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks Sateesh, Shyam K BK, Sparsh D, Uma Machine Learning Artificial Intelligence Human-Computer Interaction 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 towards 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. This paper elucidates the methodology and architectural specifics of our LSTM model and provides a benchmark analysis with existing papers. |
| title | Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks |
| topic | Machine Learning Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2408.10328 |