A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold

• Published in: International journal of neural systems Vol. 30; no. 3; p. 2050009
• Main Authors: Li, Mengfan, Lin, Fang, Xu, Guizhi
• Format: Journal Article
• Language: English
• Published: Singapore 01.03.2020
• Subjects: N200; TrAdaBoost; brain–computer interface; cross-validation; P300; adaptive threshold

Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-validation and an adaptive threshold (CV-T-TAB), to...