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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publication2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
PublisherIEEE
Pages743-749
Number of pages7
ISBN (Print)978-1-5090-1897-0
DOIs
StatePublished - 2016

Publication series

NameIEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISSN (Print)1062-922X

Keywords

  • brain-computer interface
  • calibrationless BCI
  • spectral meta-learner
  • regression
  • EEG
  • ensemble learning

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    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). In 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) (pp. 743-749). (IEEE International Conference on Systems Man and Cybernetics Conference Proceedings). IEEE. https://doi.org/10.1109/SMC.2016.7844330