title: Characterization and robust classification of EEG signal from image RSVP events with independent time-frequency features.
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.
  
  
  BACKGROUND
  The data and code are available at: http://compgenomics.cbi.utsa.edu/rsvp/index.html.
authors.affiliation: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
authors.forename: Jia
authors.initials: J
authors.lastname: Meng
authors.forename: Lenis Mauricio
authors.initials: LM
authors.lastname: Meriño
authors.forename: Nima
authors.initials: N
authors.lastname: Bigdely Shamlo
authors.forename: Scott
authors.initials: S
authors.lastname: Makeig
authors.forename: Kay
authors.initials: K
authors.lastname: Robbins
authors.forename: Yufei
authors.initials: Y
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D008962:Models, Theoretical