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...
| Main Author: | |
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| Format: | Thesis |
| Published: |
Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/98033 |
| _version_ | 1848766352541614080 |
|---|---|
| author | Chandrasekar, Ramya |
| author_facet | Chandrasekar, Ramya |
| author_sort | Chandrasekar, Ramya |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:49:47Z |
| format | Thesis |
| id | curtin-20.500.11937-98033 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:49:47Z |
| publishDate | 2024 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-980332025-07-04T06:09:25Z Multi-Class Anxiety Classification using Error-related EEG Signals and Deep Learning Models Chandrasekar, Ramya 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. 2024 Thesis http://hdl.handle.net/20.500.11937/98033 Curtin University fulltext |
| spellingShingle | Chandrasekar, Ramya Multi-Class Anxiety Classification using Error-related EEG Signals and Deep Learning Models |
| title | Multi-Class Anxiety Classification using Error-related
EEG Signals and Deep Learning Models |
| title_full | Multi-Class Anxiety Classification using Error-related
EEG Signals and Deep Learning Models |
| title_fullStr | Multi-Class Anxiety Classification using Error-related
EEG Signals and Deep Learning Models |
| title_full_unstemmed | Multi-Class Anxiety Classification using Error-related
EEG Signals and Deep Learning Models |
| title_short | Multi-Class Anxiety Classification using Error-related
EEG Signals and Deep Learning Models |
| title_sort | multi-class anxiety classification using error-related
eeg signals and deep learning models |
| url | http://hdl.handle.net/20.500.11937/98033 |