Characterization and Robust Classification of EEG Signal from Image RSVP Events with Independent Time-Frequency Features
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Meng-2012-Characterization and.pdf
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Author(s)
Meng, Jia
Merino, Lenis Mauricio
Shamlo, Nima Bigdely
Makeig, Scott
Robbins, Kay
Huang, Yufei
Date Issued
September 2012
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Meng, Jia et al. “Characterization and Robust Classification of EEG Signal from Image RSVP Events with Independent Time-Frequency Features.” Ed. Lawrence M. Ward. PLoS ONE 7.9 (2012): e44464.
Version
Final published version
Abstract
This paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG data. A novel method that aims to identify single-trial event-related potentials (ERPs) in time-frequency is proposed, and a robust classifier with feature clustering is developed to better utilize the correlated ERP features. The method is applied to EEG recordings of a RSVP experiment with multiple sessions and subjects.
The results show that the target image events are mainly characterized by 3 distinct patterns in the time-frequency domain, i.e., a theta band (4.3 Hz) power boosting 300–700 ms after the target image onset, an alpha band (12 Hz) power boosting 500–1000 ms after the stimulus onset, and a delta band (2 Hz) power boosting after 500 ms. The most discriminant time-frequency features are power boosting and are relatively consistent among multiple sessions and subjects.
Since the original discriminant time-frequency features are highly correlated, we constructed the uncorrelated features using hierarchical clustering for better classification of target and non-target images. With feature clustering, performance (area under ROC) improved from 0.85 to 0.89 on within-session tests, and from 0.76 to 0.84 on cross-subject tests. The constructed uncorrelated features were more robust than the original discriminant features and corresponded to a number of local regions on the time-frequency plane.
MIT Department
Picower Institute for Learning and Memory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0044464
