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This paper investigates how acupuncture at ST 36 modulates the brain functional network. 20 channel EEG signals from 15 healthy subjects are respectively recorded before, during and after acupuncture. The correlation between two EEG channels is calculated by using Pearson’s coefficient. A data-driven approach is applied to determine the threshold, which is performed by considering the connected set, connected edge and network connectivity. Based on such thresholding approach, the functional network in each acupuncture period is built with graph theory, and the associated functional connectivity is determined. We show that acupuncturing at ST 36 increases the connectivity of the EEG-based functional network, especially for the long distance ones between two hemispheres. The properties of the functional network in five EEG sub-bands are also characterized. It is found that the delta and gamma bands are affected more obviously by acupuncture than the other sub-bands. These findings highlight the modulatory effects of acupuncture on the EEG-based functional connectivity, which is helpful for us to understand how it participates in the cortical or subcortical activities. Further, the data-driven threshold provides an alternative approach to infer the functional connectivity under other physiological conditions.

In this paper, we investigate signatures of variation in the behavior of correlated time series by analyzing changes in the topological properties of the corresponding visibility graph. Variations in six different network measures: assortativity, average path length, clustering, transitivity, density, and the average of the mean link length, are explored. We construct visibility graphs from the original and the magnitude and sign of its increment series. Both the horizontal and the natural visibility graphs are studied. Through extensive numerical studies on the time series of fractional Brownian motion (fBm), we first identify network measures that can reflect the changes in correlations in the time series. The efficacy of these markers is examined to identify the transitions in two systems, a two-dimensional (2D) Ising spin system and EEG data with seizures. While all the identified network measures capture the change in the thermal equilibrium correlations for the Ising spin system, they have limited success in the case of the time-dependent fluctuations in the EEG data. We identify some markers relevant to detecting seizures in the EEG data set.

Multiscale sample entropy (MSE) of human electroencephalogram (EEG) data from patients under different pathological conditions of Alzheimer's disease (AD) was evaluated to measure the complexity of the signal. Quantifying the complexity level with respect to various temporal scales, MSE analysis provides a dynamical description of AD development. When compared to EEG data from normal subjects, EEG data from subjects with mild cognitive impairment (MCI) showed nearly the same complexity profile, but a scale discrepancy which may occur from a spectral abnormality. EEG data from severe AD patients showed a loss of complexity over the wide range of time scales, indicating a destruction of nonlinear structures in brain dynamics. We compare the MSE method and spectral analysis to propose that nonlinear dynamical approach combining a multiscale method is crucial for revealing AD mechanisms.

Electroencephalography (EEG) is a non-invasive technique used to measure the electrical activity of the brain. The use of EEG is very important for the diagnosis of traumatic brain events and mental states such as injury, stroke, depression and many others including the COVID-19 brain fog syndrome. The quality of EEG signals largely depends on the nature of the interface between the surface of the electrode material and the surface of the scalp from where the electrical brain signals are acquired. The scalp surface is composed of an epidermic substrate with hair, grease, dirt, dandruff, skin peels and eventually many different hair products. The electrodes must combine several properties including electrical conductivity, mechanical strength, biocompatibility and corrosion resistance. They also must be manufactured with shapes designed to overcome the inherently complex nature of the scalp–electrode interface. This review reports the latest advances in the design of materials, surface coatings, conductive gels and information and communication technologies being developed to increase the quality of measurement of brain electrical signals in EEG protocols.

In bioelectrical potential measurement with restricted skin-electrode contact area, such as in dense array EEG measurement where the electrolyte bridging effect¹ is a major concern for signal reliability, an enhanced electrolyte solution is required for the skin-electrode impedance to reach the sufficiently low level within the minimum time interval. In this study, an electrolyte gel with its skin permeation ability enhanced by ethanol or propylene glycol has been investigated. The standard skin-electrode impedance measurement was carried out on the forehead in an area of 6 mm in diameter using standard Ag/AgCl EEG electrodes. The gel solutions with 0%, 7%, 18% and 28% of enhancers by volume are compared. The results show that both ethanol and propylene glycol reduce the permeation barrier of the stratum corneum so that ions in the electrolyte gel can penetrate more readily into the skin, enhancing the skin-electrode conductivity in reaching the steady value at a faster rate. It was further found that for the gel with higher percentage of ethanol, lower minimum skin-electrode impedance value was obtained. However, as the percentage of propylene glycol increased, it fails to attain low steady impedance values in the skin-electrode impedance measurements.