Why Participant-Aware Evaluation Matters for EEG Machine Learning Models
Recent analysis reveals how participant overlap between training and testing datasets significantly skews performance metrics in EEG machine learning models. Evaluating across three distinct models, researchers demonstrated that proper participant-aware validation is essential for measuring true generalization capabilities, preventing overly optimistic results, and ensuring reliable real-world brain-computer interface applications.
Source: Towards Data Science