Cross Subject Data Splitting

Cross-subject data splitting in machine learning focuses on improving the generalization and efficiency of models trained on data from multiple sources, particularly in complex domains like brain-computer interfaces. Current research emphasizes developing robust splitting strategies that mitigate data leakage, a critical issue affecting model accuracy and reliability, and explores theoretical frameworks to understand the impact of different splitting methods on model performance. This work is crucial for advancing the reliability and generalizability of machine learning models across diverse datasets, with implications for various fields including neuroscience, personalized medicine, and policy evaluation.