Cheng Cao1, Semyon Slobounov. 1. Department of Kinesiology, 19 Recreation Building, The Pennsylvania State University, University Park, PA 16802, USA.
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. SIGNIFICANCE: This new method may potentially be used as a complementary tool to assess the alteration of brain functions as a result of mTBI. Published by Elsevier Ireland Ltd.
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. SIGNIFICANCE: This new method may potentially be used as a complementary tool to assess the alteration of brain functions as a result of mTBI. Published by Elsevier Ireland Ltd.
Authors: S M Slobounov; M Gay; K Zhang; B Johnson; D Pennell; W Sebastianelli; S Horovitz; M Hallett Journal: Neuroimage Date: 2011-01-19 Impact factor: 6.556
Authors: Sascha Gruss; Roi Treister; Philipp Werner; Harald C Traue; Stephen Crawcour; Adriano Andrade; Steffen Walter Journal: PLoS One Date: 2015-10-16 Impact factor: 3.240