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PloS one DOI:10.1371/journal.pone.0044464

Characterization and Robust Classification of EEG Signal from Image RSVP Events with Independent Time-Frequency Features.

Publication TypeJournal Article
Year of Publication2012
AuthorsMeng, J, Meriño, LM, Shamlo, NB, Makeig, S, Robbins, K, Huang, Y
JournalPloS one
Volume7
Issue9
Pagese44464
Date Published2012/01/01
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.Availability: The data and code are available at: http://compgenomics.cbi.utsa.edu/rsvp/index.html.

URLhttp://dx.plos.org/10.1371/journal.pone.0044464
DOI10.1371/journal.pone.0044464
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/23028544?dopt=Abstract