scholarly journals The impact of mental fatigue on brain activity: A comparative study between resting state and task state using EEG

2019 ◽  
Author(s):  
Gang Li ◽  
Youdong Luo ◽  
Weidong Jiao ◽  
Yonghua Jiang ◽  
Zhao Gao ◽  
...  
Keyword(s):  
Resting State ◽  
Mental Arithmetic ◽  
Brain Activity ◽  
Mental Fatigue ◽  
Relative Power ◽  
Arithmetic Task ◽  
Eeg Data ◽  
Mental Arithmetic Task ◽  
The Brain ◽  
And Task

Abstract Background: Mental fatigue is usually caused by long-term cognitive activities, mainly manifested as drowsiness, difficulty in concentrating, decreased alertness, disordered thinking, slow reaction, lethargy, reduced work efficiency, error-prone and so on. Mental fatigue has become a widespread sub-health condition, and has a serious impact on the cognitive function of the brain. However, seldom researches explore the differences of mental fatigue on electrophysiological activity between resting state and task state. In the present study, 20 healthy male individuals were recruited to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) data were collected before and after the task. The power and relative power of five EEG rhythms both in resting state and task state were analyzed statistically. Results: The results of brain topographies and statistical analysis indicated that mental arithmetic task can successfully induce mental fatigue in the enrolled subjects. The relative power index was more sensitive than the power index in response to mental fatigue, and the relative power for assessing mental fatigue was better in resting state than in task state. Furthermore, we found that it is of great physiological significance to divide alpha frequency band into alpha1 band and alpha2 band in fatigue related studies, and at the same time improve the statistical differences of sub-bands. Conclusions: Our current results suggested that the brain activity in mental fatigue state has great differences between resting state and task state, and it is imperative to select the appropriate state in EEG data acquisition and divide alpha band into alpha1 and alpha2 bands in mental fatigue related researches.

scholarly journals BCI for a brain state control in a dual-task paradigm

2019 ◽  
pp. 262-266
Author(s):  
Vladimir Maksimenko
Keyword(s):  
Mental Arithmetic ◽  
Brain State ◽  
Main Task ◽  
State Control ◽  
Arithmetic Task ◽  
Concurrent Tasks ◽  
Additional Task ◽  
Mental Arithmetic Task ◽  
Dual Task Paradigm ◽  
The Brain

The brain resource is limited and it needs to be distributed among the different concurrent tasks. When the subject accomplishes the resource-demanding main task requiring sustained attention, the additional task leads to the reduction of resources allocated for the main task accomplishing. This additional task can be either important or caused by the distraction. Anyway, it causes a performance decrease in the main task. In this paper, we propose a brain-computer interface (BCI) to control the cognitive performance of the visual task accomplishing in the presence of the additional (mental arithmetic) task. We demonstrate how the additional task affects the performance of the main task accomplishing.

scholarly journals Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach

2021 ◽  
Vol 11 (11) ◽  
pp. 1216
Author(s):  
Povilas Tarailis ◽  
Dovilė Šimkutė ◽  
Thomas Koenig ◽  
Inga Griškova-Bulanova
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Subjective Experiences ◽  
Somatic Awareness ◽  
Substantial Evidence ◽  
Temporal Properties ◽  
Eeg Data ◽  
Spontaneous Thought ◽  
Intrinsic Brain Activity ◽  
The Brain

Rationale: The resting-state paradigm is frequently applied in electroencephalography (EEG) research; however, it is associated with the inability to control participants’ thoughts. To quantify subjects’ subjective experiences at rest, the Amsterdam Resting-State Questionnaire (ARSQ) was introduced covering ten dimensions of mind wandering. We aimed to estimate associations between subjective experiences and resting-state microstates of EEG. Methods: 5 min resting-state EEG data of 197 subjects was used to evaluate temporal properties of seven microstate classes. Bayesian correlation approach was implemented to assess associations between ARSQ domains assessed after resting and parameters of microstates. Results: Several associations between Comfort, Self and Somatic Awareness domains and temporal properties of neuroelectric microstates were revealed. The positive correlation between Comfort and duration of microstates E showed the strongest evidence (BF10 > 10); remaining correlations showed substantial evidence (10 > BF10 > 3). Conclusion: Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the intrinsic brain activity reflected in microstates.

scholarly journals The impact of mental fatigue on brain activity: a comparative study both in resting state and task state using EEG

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Gang Li ◽  
Shan Huang ◽  
Wanxiu Xu ◽  
Weidong Jiao ◽  
Yonghua Jiang ◽  
...  
Keyword(s):  
Comparative Study ◽  
Resting State ◽  
Brain Activity ◽  
Mental Fatigue ◽  
The Impact ◽  
And Task

scholarly journals The impact of hyperoxia on brain activity: A resting-state and task-evoked electroencephalography (EEG) study

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0176610 ◽  
Author(s):  
Min Sheng ◽  
Peiying Liu ◽  
Deng Mao ◽  
Yulin Ge ◽  
Hanzhang Lu
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
The Impact ◽  
And Task

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

Estimation of Cognitive Distraction from Driver Gazing

2017 ◽  
Vol 9 (2) ◽  
pp. 50-60 ◽  
Author(s):  
Akira Yoshizama ◽  
Hiroyuki Nishiyama ◽  
Hirotoshi Iwasaki ◽  
Fumio Mizoguchi
Keyword(s):  
Qualitative Data ◽  
Mental Arithmetic ◽  
Support Vector ◽  
Simulation Environment ◽  
Time Intervals ◽  
Arithmetic Task ◽  
Task Load ◽  
Cognitive Distraction ◽  
Mental Arithmetic Task ◽  
Using Data

In their study, the authors sought to generate rules for cognitive distractions of car drivers using data from a driving simulation environment. They collected drivers' eye-movement and driving data from 18 research participants using a simulator. Each driver drove the same 15-minute course two times. The first drive was normal driving (no-load driving), and the second drive was driving with a mental arithmetic task (load driving), which the authors defined as cognitive-distraction driving. To generate rules of distraction driving using a machine-learning tool, they transformed the data at constant time intervals to generate qualitative data for learning. Finally, the authors generated rules using a Support Vector Machine (SVM).

scholarly journals Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation

2021 ◽  
Author(s):  
Toshiki Kusano ◽  
Hiroki Kurashige ◽  
Isao Nambu ◽  
Yoshiya Moriguchi ◽  
Takashi Hanakawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Body Part ◽  
Resting State Fmri ◽  
Body Parts ◽  
Finger Movements ◽  
Multiple Components ◽  
Brain Activity Pattern ◽  
Motor Representations ◽  
The Brain

AbstractSeveral functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.

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