標題: Spectral Meta-Learner for Regression (SMLR) Model Aggregation: Towards Calibrationless Brain-Computer Interface (BCI)
作者: Wu, Dongrui
Lawhern, Vernon J.
Gordon, Stephen
Lance, Brent J.
Lin, Chin-Teng
腦科學研究中心
Brain Research Center
關鍵字: Brain-computer interface;calibrationless BCI;regression;EEG;ensemble learning;spectral meta-learner
公開日期: 1-Jan-2016
摘要: To facilitate the transition of brain-computer interface (BCI) systems from laboratory settings to real-world application, it is very important to minimize or even completely eliminate the subject-specific calibration requirement. There has been active research on calibrationless BCI systems for classification applications, e.g., P300 speller. To our knowledge, there is no literature on calibrationless BCI systems for regression applications, e.g., estimating the continuous drowsiness level of a driver from EEG signals. This paper proposes a novel spectral meta-learner for regression (SMLR) approach, which optimally combines base regression models built from labeled data from auxiliary subjects to label offline EEG data from a new subject. Experiments on driver drowsiness estimation from EEG signals demonstrate that SMLR significantly outperforms three state-of-the-art regression model fusion approaches. Although we introduce SMLR as a regression model fusion in the BCI domain, we believe its applicability is far beyond that.
URI: http://hdl.handle.net/11536/146599
ISSN: 1062-922X
期刊: 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
起始頁: 743
結束頁: 749
Appears in Collections:Conferences Paper