Spectral Meta-Learner for Regression (SMLR) Model Aggregation: Towards Calibrationless Brain-Computer Interface (BCI)

Dongrui Wu, Lawhern Vernon J., Stephen Gordon, Lance Brent J, Chin-Teng Lin

研究成果: Conference contribution

7 引文 斯高帕斯(Scopus)

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.
原文English
主出版物標題2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
發行者IEEE
頁面743-749
頁數7
ISBN(列印)978-1-5090-1897-0
DOIs
出版狀態Published - 2016

出版系列

名字IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISSN(列印)1062-922X

指紋 深入研究「Spectral Meta-Learner for Regression (SMLR) Model Aggregation: Towards Calibrationless Brain-Computer Interface (BCI)」主題。共同形成了獨特的指紋。

  • 引用此

    Wu, D., Vernon J., L., Gordon, S., Brent J, L., & Lin, C-T. (2016). Spectral Meta-Learner for Regression (SMLR) Model Aggregation: Towards Calibrationless Brain-Computer Interface (BCI). 於 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) (頁 743-749). (IEEE International Conference on Systems Man and Cybernetics Conference Proceedings). IEEE. https://doi.org/10.1109/SMC.2016.7844330