Detrended Fluctuation, Coherence, and Spectral Power Analysis of Activation Rearrangement in EEG Dynamics During Cognitive Workload

Abstract

In the study of human cognitive activity using electroencephalogram (EEG), the brain dynamics parameters and characteristics play a crucial role. They allow to investigate the changes in functionality depending on the environment and task performance process, and also to access the intensity of the brain activity in various locations of the cortex and its dependencies. Usually, the dynamics of activation of different brain areas during the cognitive tasks are being studied by spectral analysis based on power spectral density (PSD) estimation, and coherence analysis, which are de facto standard tools in quantitative characterization of brain activity. PSD and coherence reflect the strength of oscillations and similarity of the emergence of these oscillations in the brain, respectively, while the concept of stability of brain activity over time is not well defined and less formalized. We propose to employ the detrended fluctuation analysis (DFA) as a measure of the EEG persistence over time, and use the DFA scaling exponent as its quantitative characteristics. We applied DFA to the study of the changes in activation in brain dynamics during mental calculations and united it with PSD and coherence estimation. In the experiment, EEGs during resting state and mental serial subtraction from 36 subjects were recorded and analyzed in four frequency ranges: θ1 (4.1–5.8 Hz), θ2 (5.9–7.4 Hz), β1 (13–19.9 Hz), and β2 (20–25 Hz). PSD maps to access the intensity of cortex activation and coherence to quantify the connections between different brain areas were calculated, the distribution of DFA scaling exponent over the head surface was exploited to measure the time characteristics of the dynamics of brain activity. Obtained arrangements of DFA scaling exponent suggest that normal functioning of the brain is characterized by long-term temporal correlations in the cortex. Topographical distribution of the DFA scaling exponent was comparable for θ and β frequency bands, demonstrating the largest values of DFA scaling exponent during cognitive activation. The study shows that the long-term temporal correlations evaluated by DFA can be of great interest for diagnosis of the variety of brain dysfunctions of different etiology in the future.

EEG microstate features according to performance on a mental arithmetic task

“…A publicly accessible EEG dataset was analysed in this study, which was obtained from the ‘EEG During Mental Arithmetic Tasks’ 66 in the EEG dataset on PhysioNet.org 67 , was approved by the Bioethics Commission of Educational and Scientific Centre ‘‘Institute of Biology and Medicine,’’ Taras Shevchenko National University of Kyiv and written informed consent was obtained from each subject in accordance with the World Medical Association (WMA) Declaration of Helsinki. Healthy university student volunteers with no visual, cognitive, or mental impairment were eligible to participate in the task.…”

“…In this study, the 3 min resting state and the first 1 min of the task state were selected for analysis, due to mental fatigue caused by the task later on. The achievement of the mental arithmetic task was assessed according to Zyma et al 68 and Seleznov et al 66 based on the number of operations performed per minute 73 . As a result, the subjects were divided into 26 good performers and ten poor performers.…”

A Cognitive Workload Identification using EEG Power Spectrum

“…The frequency domain feature extraction techniques have been used to capture EEG signal characteristics which may be relevant to evaluate cognitive workload. The Power Spectral Density (PSD) feature is generally used for feature extraction in the frequency domain [1,13,20] [21]. They also proposed that for a brain enhancement system, the brain connectome approach is very helpful.…”

“…The analysis of human cognitive activity has fascinated a lot of researchers [1][2][3][4]. The process of activation of various areas of the brain during the cognitive tasks has been mostly considered by the spectral analysis (power spectral density) which is based on Fast Fourier Transform [1].…”

“…The analysis of human cognitive activity has fascinated a lot of researchers [1][2][3][4]. The process of activation of various areas of the brain during the cognitive tasks has been mostly considered by the spectral analysis (power spectral density) which is based on Fast Fourier Transform [1]. The automated analysis of the electroencephalogram to evaluate mental states of human like the cognitive workload is increasing day by day [5,6].…”