title: A novel approach to identify time-frequency oscillatory features in electrocortical signals.
abstract: BACKGROUND
  Sensory, motor, and cognitive events could not only evoke phase-locked event-related potentials in ongoing electrocortical signals, but also induce non-phase-locked changes of oscillatory activities. These oscillatory activities, whose functional significances differ greatly according to their temporal, spectral, and spatial characteristics, are commonly detected when single-trial signals are transformed into time-frequency distributions (TFDs). Parameters characterizing oscillatory activities are normally measured from multi-channel TFDs within a time-frequency region-of-interest (TF-ROI), pre-defined using a hypothesis-driven or data-driven approach. However, both approaches could ignore the possibility that the pre-defined TF-ROI contains several spatially/functionally distinct oscillatory activities.
  
  
  METHODS
  We proposed a novel approach based on topographic segmentation analysis to optimally and automatically identify detailed time-frequency features. This approach, which could effectively exploit the spatial information of oscillatory activities, has been validated in both simulation and real electrocortical studies.
  
  
  RESULTS
  Simulation study showed that the proposed approach could successfully identify noise-contaminated time-frequency features if their signal-to-noise ratio was relatively high. Real electrocortical study demonstrated that several time-frequency features with distinct scalp distributions and evident neurophysiological functions were identified when the same analysis was applied on stimulus-elicited TFDs.
  
  
  METHODS
  Unlike traditional approaches, the proposed approach could provide an optimal identification of detailed time-frequency features by making use of their distinct spatial distributions.
  
  
  CONCLUSIONS
  Our findings illustrated the validity and usefulness of the presented approach in isolating detailed time-frequency features, thus having wide applications in cognitive neuroscience to provide a precise assessment of the functional significance of oscillatory activities.
authors.affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and Faculty of Psychology, Southwest University, Chongqing 400715, China.
authors.forename: Huibin
authors.initials: H
authors.lastname: Jia
authors.affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and Faculty of Psychology, Southwest University, Chongqing 400715, China. Electronic address: ww.peng0923@gmail.com.
authors.forename: Weiwei
authors.initials: W
authors.lastname: Peng
authors.affiliation: Key Laboratory of Cognition and Personality (Ministry of Education) and Faculty of Psychology, Southwest University, Chongqing 400715, China. Electronic address: huli@swu.edu.cn.
authors.forename: Li
authors.initials: L
authors.lastname: Hu
mesh_terms: D000328:Adult; D001683:Biological Clocks / Q000502:physiology*; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D059629:Signal-To-Noise Ratio; D013997:Time Factors; D055815:Young Adult
keywords: Event-related synchronization/desynchronization (ERS/ERD); Scalp topography; Time-frequency analysis; Time-frequency oscillatory features; Topographic segmentation analysis