All Outputs (3)

Advanced machine learning techniques for clinical EEG classification (2025)
Thesis

In the rapidly evolving field of medical diagnostics, the accurate classification of electroencephalogram (EEG) signals remains a crucial task for the early detection and treatment of neurological disorders. Traditional EEG analysis relies on manual... Read More about Advanced machine learning techniques for clinical EEG classification.

Adapting deep-learning audio models for abnormal EEG classification (2025)
Presentation / Conference Contribution

EEG signal analysis and audio processing, though distinct in application, share inherent structural similarities in their data patterns. Recognizing this parallel, our study pioneers the application of two renowned audio processing models, PaSST and... Read More about Adapting deep-learning audio models for abnormal EEG classification.

Scope and arbitration in machine learning clinical EEG classification (2023)
Presentation / Conference Contribution

A key task in clinical EEG interpretation is to classify a recording or session as normal or abnormal. In machine learning approaches to this task, recordings are typically divided into shorter windows for practical reasons, and these windows inherit... Read More about Scope and arbitration in machine learning clinical EEG classification.