Spectral meta-learner for regression (SMLR) model aggregation: Towards calibrationless brain-computer interface (BCI)

Publication Type:
Conference Proceeding
Citation:
2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, 2017, pp. 743 - 749
Issue Date:
2017-02-06
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© 2016 IEEE. 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.
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