Scientific Publications

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

Publication TypeJournal Article
AuthorsMeng, J., Meriño LM, Shamlo NB, Makeig S., Robbins K., and Huang Y.
AbstractThis 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.
Year of Publication2012
JournalPloS one
Volume7
Issue9
Pagese44464
Date Published (YYYY/MM/DD)2012/01/01
DOI10.1371/journal.pone.0044464
PubMedhttp://www.ncbi.nlm.nih.gov/pubmed/23028544?dopt=Abstract