Multi-Class Anxiety Classification using Error-related EEG Signals and Deep Learning Models
Anxiety disorders impact mental and physical health globally. This study classifies anxiety severity levels using Error-Related Negativity (ERN) signals from EEG data, analyzing 163 participants during a go/no-go task. Employing RNN, LSTM, and GRU models, anxiety was categorized as mild, moderate, o...
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| Format: | Thesis |
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Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/98033 |