General laws for the formation of the state of monotony

Abstract

Experimental studies have demonstrated that the formation of the state of optimum working ability in conditions of monotonous activity is associated with a decrease (compared to the rest state) in the level of "preventive" arousal of the CNS, manifest on the EEG as an increase in the power of slow rhythms on the background of a selective increase in the level of coherence at frequencies in the alpha rhythm in the caudal areas of the cortex. Prolonged exposure to monotonous conditions leads to increases in the power of slow (theta and alpha(1)) and beta frequencies in the EEG spectrum without marked changes in the level of coherence. Our results, along with published data, were used to propose the hypothesis that unlike the basic states of the sleep-waking cycle (sleep, rest, active waking, etc.), the state of monotony lacks intrinsic ("built-in") self-regulatory mechanisms; this results in instability in the operation of the regulatory systems of the brain. This leads to the appearance of marked periodicity in changes in both the functional state of the CNS and the quality of work performed by humans.

Graph analysis of functional brain network topology using minimum spanning tree in driver drowsiness

“…Differences in functional network topology between drowsy state and alert state may reflect the ability of the brain to modulate either drowsiness or alertness. Distinguishing the conventional EEG power analysis (Kiroj and Aslanjan 2005) and conventional network analysis (Zhao et al 2017), the novelty of the current study lies in the fact that the MST graph theory is employed to gain more understanding of the reorganization of brain networks with the variation of drowsiness. When drowsiness occurs, the brain regional synchronous activities are increased significantly in the delta and theta frequency band, which accordingly induces the changes in brain network configurations.…”

Brain biomarkers of motor adaptation using phase synchronization

“…As previously mentioned, these PLV (multivariate) brain biomarkers complement well those derived from univariate methods such as spectral power. It is important to realize that changes in phase synchronization for a specific frequency range do not necessarily imply similar power changes for the same electrodes [15]. For future real-time EEG ecological applications, brain biomarkers for individual subjects and single trials need to be available.…”

“…Beforehand, for each subject and each single-trial, a filter bank using a series of band-pass FIR (Finite Impulse Response of length 450 ms) filters was used to extract, from the EEG signal, seven frequency bands corresponding to the low (Lα: [8][9][10] Hz; Lβ: [13][14][15][16][17][18][19][20] Hz; Lθ: [4][5] Hz) and the high (Hα: [11][12][13]; Hβ: [21-35]; Hθ: [6][7] Hz) components of the alpha (α), beta (β), and theta (θ) bands. The gamma frequency (γ; [36-44] Hz) was also extracted.…”

Signal Processing for Non-Invasive Brain Biomarkers of Sensorimotor Performance and Brain Monitoring

“…Therefore, beyond certain specificities that are task-dependent, these biomarkers of human performance share a common consistent topology in term of frequency and spatial scalp locations across different tasks. Moreover, it must be noted that changes in phase synchronization for a specific frequency range do not necessarily imply similar power changes for the same electrodes (Kiroi & Aslanyan, 2006). Therefore, the availability of processing techniques for extracting and combining both univariate (i.e., spectral power) and multivariate (i.e., spectral coherence/PLV) cortical measures might provide "multidimensional" brain biomarkers in the future.…”