Multi-Class Anxiety Classification using Error-related EEG Signals and Deep Learning Models
dc.contributor.author | Chandrasekar, Ramya | |
dc.contributor.supervisor | Tom Gedeon | en_US |
dc.contributor.supervisor | Md Zakir Hossain | |
dc.date.accessioned | 2025-07-04T05:55:49Z | |
dc.date.available | 2025-07-04T05:55:49Z | |
dc.date.issued | 2024 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/98033 | |
dc.description.abstract |
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, or severe. GRU achieved 97.6% accuracy under 10-fold cross-validation. Advanced pre-processing and feature extraction ensured robustness. This method outperforms existing techniques, offering a precise, automated approach to anxiety diagnosis using deep learning and EEG analysis. | en_US |
dc.publisher | Curtin University | en_US |
dc.title | Multi-Class Anxiety Classification using Error-related EEG Signals and Deep Learning Models | en_US |
dc.type | Thesis | en_US |
dcterms.educationLevel | MPhil | en_US |
curtin.department | School of Electrical Engineering, Computing and Mathematical Sciences | en_US |
curtin.accessStatus | Open access | en_US |
curtin.faculty | Science and Engineering | en_US |
curtin.contributor.orcid | Chandrasekar, Ramya [0000-0002-9498-1975] | en_US |