Single-Trial EEG Predicts Memory Retrieval Using Leave-One-Subject-Out Classification

摘要

In this study, we perform single-trial EEG classification in memory retrieval predictions using classifiers trained on a leave-one-subject-out (LOSO) cross-validation basis. We also compare the performance to that of classification using leave-one-trial-out (LOTO) when trained on data for an individual subject. Unlike traditional single-trial EEG analysis performed within an individual subject, we show that it is possible to perform single-trial EEG classification using classifiers trained on different subjects leading the way to more general classifiers for brain-computer interface (BCI) applicable to first-time BCI users.