Multi-Disorder EEG Classification Benchmarks

This paper addresses the critical need for automated EEG analysis across multiple neurological disorders, moving beyond isolated diagnostic problems. It establishes realistic performance baselines and demonstrates the effectiveness of sensitivity-prioritized machine learning for scalable EEG screening and triage. The focus on clinically relevant disorders and the use of a large, heterogeneous dataset are significant strengths.

• •Establishes benchmarks for multi-disorder EEG classification.
• •Demonstrates the effectiveness of sensitivity-prioritized machine learning.
• •Provides evidence for scalable EEG screening and triage.
• •Uses a large, heterogeneous clinical EEG dataset.