Classification of Imperfectly Time-Locked Image RSVP Events with EEG Device
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Author(s)
Meng, Jia
Robbins, Kay
Huang, Yufei
Merino, Lenis Mauricio
Date Issued
September 2013
Journal
Neuroinformatics
Publisher
Springer US
Citation
Meng, Jia, Lenis Mauricio Meriño, Kay Robbins, and Yufei Huang. “Classification of Imperfectly Time-Locked Image RSVP Events with EEG Device.” Neuroinformatics 12, no. 2 (September 15, 2013): 261–275.
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Author's final manuscript
Abstract
Classification based on EEG data in an RSVP experiment is considered. Although the latency in neural response relative to the stimulus onset time may be more realistically considered to vary across trials due to factors such as subject fatigue and environmental distractions, it is nevertheless assumed to be time-locked to the stimulus in most of the existing work as a means to alleviate the computational complexity. We consider here a more practical scenario that allows variation in response latency and develop a rigorous statistical formulation for modeling the uncertainty within the varying latency coupled with a likelihood ratio test (LRT) for classification. The new model not only improves the EEG classification performance, but also may predict the true stimulus onset time when this information is not precisely available. We test the proposed LRT algorithm on an EEG data set from an image RSVP experiment and show that, by admitting the latency variation, the proposed approach consistently outperforms a method that relies on perfect time-locking (AUC: 0.88 vs 0.86), especially when the stimulus onset time is not precisely available (AUC: 0.84 vs 0.71). Furthermore, the predicted stimulus onset times are highly enriched around the true onset time with p-value = 5.2 × 10[superscript −44] .
MIT Department
Broad Institute of MIT and Harvard
Picower Institute for Learning and Memory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s12021-013-9203-4
