title: Application of a novel measure of EEG non-stationarity as 'Shannon- entropy of the peak frequency shifting' for detecting residual abnormalities in concussed individuals.
abstract: OBJECTIVE
  The aim of this report was to propose a novel measure of non-stationarity of EEG signals, named Shannon- entropy of the peak frequency shifting (SEPFS). The feasibility of this method was documented comparing this measure with traditional time domain assessment of non-stationarity and its application to EEG data sets obtained from student-athletes before and after suffering a single episode of mild traumatic brain injury (mTBI) with age-matched normal controls.
  
  
  METHODS
  Instead of assessing the power density distribution on the time-frequency plane, like previously proposed measures of signal non-stationarity, this new measure is based on the shift of the dominant frequency of the EEG signal over time. We applied SEPFS measure to assess the properties of EEG non-stationarity in subjects before and shortly after they suffered mTBI. Student-athletes at high risk for mTBI (n=265) were tested prior to concussive episodes as a baseline. From this subject pool, 30 athletes who suffered from mTBI were retested on day 30 post-injury. Additional subjects pool (student-athletes without history of concussion, n=30) were recruited and test-re-tested within the same 30 day interval. Thirty-two channels EEG signals were acquired in sitting eyes closed condition.
  
  
  RESULTS
  The results showed that the SEPFS values significantly decreased in subjects suffering from mTBI. Specifically, reduced EEG non-stationarity was observed in occipital, temporal and central brain areas, indicating the possibility of residual brain dysfunctions in concussed individuals. A similar but less statistically significant trend was observed using traditional time domain analysis of EEG non-stationarity.
  
  
  CONCLUSIONS
  The proposed measure has at least two merits of interest: (1) it is less affected by the limited resolution of time-frequency representation of the EEG signal; (2) it takes into account the neural characteristics of the EEG signal that have not been considered in previously proposed measures of non-stationarity.
  
  
  CONCLUSIONS
  This new method may potentially be used as a complementary tool to assess the alteration of brain functions as a result of mTBI.
authors.affiliation: Department of Kinesiology, 19 Recreation Building, The Pennsylvania State University, University Park, PA 16802, USA.
authors.forename: Cheng
authors.initials: C
authors.lastname: Cao
authors.forename: Semyon
authors.initials: S
authors.lastname: Slobounov
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D056352:Athletes; D001265:Athletic Injuries / Q000503:physiopathology; D001924:Brain Concussion / Q000503:physiopathology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography; D019277:Entropy; D005221:Fatigue / Q000503:physiopathology; D005260:Female; D006801:Humans; D008297:Male; D009483:Neuropsychological Tests; D011597:Psychomotor Performance / Q000502:physiology; D057190:Stroop Test; D014145:Trail Making Test; D058067:Wavelet Analysis; D055815:Young Adult