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dc.contributor.authorChandrasekar, Ramya
dc.contributor.supervisorTom Gedeonen_US
dc.contributor.supervisorMd Zakir Hossain
dc.date.accessioned2025-07-04T05:55:49Z
dc.date.available2025-07-04T05:55:49Z
dc.date.issued2024en_US
dc.identifier.urihttp://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.publisherCurtin Universityen_US
dc.titleMulti-Class Anxiety Classification using Error-related EEG Signals and Deep Learning Modelsen_US
dc.typeThesisen_US
dcterms.educationLevelMPhilen_US
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Sciencesen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidChandrasekar, Ramya [0000-0002-9498-1975]en_US


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