Characterization and Robust Classification of EEG Signal from Image RSVP Events with Independent Time-Frequency Features
Author(s)Meng, Jia; Merino, Lenis Mauricio; Shamlo, Nima Bigdely; Makeig, Scott; Robbins, Kay; Huang, Yufei; ... Show more Show less
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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.
DepartmentPicower Institute for Learning and Memory
Public Library of Science
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.
Final published version