Age-related variation in EEG complexity to photic stimulation: A multiscale entropy analysis

Abstract

Objective-This study was intended to examine variations in electroencephalographic (EEG) complexity in response to photic stimulation (PS) during aging to test the hypothesis that the aging process reduces physiologic complexity and functional responsiveness. Methods-Multiscale entropy (MSE), an estimate of time-series signal complexity associated with long-range temporal correlation, is used as a recently proposed method for quantifying EEG complexity with multiple coarse-grained sequences. We recorded EEG in 13 healthy elderly subjects and 12 healthy young subjects during pre-PS and post-PS conditions and estimated their respective MSE values.Results-For the pre-PS condition, no significant complexity difference was found between the groups. However, a significant MSE change (complexity increase) was found post-PS only in young subjects, thereby revealing a power-law scaling property, which means long-range temporal correlation.Conclusions-Enhancement of long-range temporal correlation in young subjects after PS might reflect a cortical response to stimuli, which was absent in elderly subjects. These results are consistent with the general "loss of complexity/diminished functional response to stimuli" theory of aging.Significance-Our findings demonstrate that application of MSE analysis to EEG is a powerful approach for studying age-related changes in brain function.

Dynamical Change of Signal Complexity in the Brain During Inhibitory Control Processes

“…The purpose of multivariate MSE is to reveal both the within and cross-channel dependencies in multichannel data [35][36][37][38]. Therefore, an alternative approach to demonstrate the results in the present study is to employ Multivariate Empirical Mode Decomposition (MEMD) [39] to detrend the EEG data, followed by using the multivariate MSE to quantify the complexity of the brain signals.…”

Dynamical complexity of human responses: a multivariate data-adaptive framework

“…In the case of white noise, however, the dyadic filter bank property of MEMD is well known [12]. Disregarding elements of coarse graining 6 , the averaging operation at scale ǫ is equivalent to low pass filtering with a cutoff frequency (normalized) of f c = 0.5/ǫ. Thus for the nth cumulative IMF index (Approach 1) of white noise, the equivalent coarse grained scale factor is given by ǫ ≈ 2 n−1 .…”

“…While the MSE measure has been successfully applied to distinguish between different real-world physiological time series based on their dynamical complexity [4][5][6][7], it also has some limitations stemming from the deterministic way of generating multiple scales of input data. The method uses the socalled coarse graining process which, owing to its low-pass filtering characteristics, is unsuitable for the extraction of high frequency components and also results in aliasing (see also Fig.…”

Multivariate multiscale entropy: A tool for complexity analysis of multichannel data

“…The method has been successfully applied across biomedical research, such as in fluctuations of the human heartbeat under pathologic conditions [13], EEG and MEG in patients with Alzheimer's disease [19], complexity of human gait under different walking conditions [20], variations in EEG complexity related to aging [21], and human red blood cell flickering [22]. These results strongly support the general 'complexity-loss' theory for systems under 'stress', for instance, through aging and disease [23].…”