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: | 1-一月-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 |
顯示於類別: | 會議論文 |