Investigation of Mental Fatigue Induced by a Continuous Mental Arithmetic Task Based on EEG Coherence Analysis

Author(s):  
Lanlan Chen ◽  
Yu Zhao ◽  
Jian Zhang ◽  
Junzhong Zou
2018 ◽  
Vol 129 (11) ◽  
pp. 2315-2324 ◽  
Author(s):  
Yuliya Zaytseva ◽  
Zhanna Garakh ◽  
Vladimir Novototsky-Vlasov ◽  
Isaac Ya. Gurovich ◽  
Alexander Shmukler ◽  
...  

2019 ◽  
Author(s):  
Gang Li ◽  
Youdong Luo ◽  
Weidong Jiao ◽  
Yonghua Jiang ◽  
Zhao Gao ◽  
...  

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.


Author(s):  
Akira Yoshizama ◽  
Hiroyuki Nishiyama ◽  
Hirotoshi Iwasaki ◽  
Fumio Mizoguchi

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).


2021 ◽  
Author(s):  
Natalie Ein

This thesis examined the role of viewing a picture of one’s pet as a mechanism for alleviating the symptoms of stress. The mental arithmetic task (MAT), a psychosocial stressor was used to induce stress. Participants were randomly assigned into one of six visual conditions: either a picture of their personal pet (n = 9), an unfamiliar animal (n = 9), a person who is supportive and important to the participant (n = 9), an unfamiliar person to the participant (n =8), a pleasant image (control 1) (n = 8) or no image (control 2) (n = 8). Stress reactivity, both physical (e.g., blood pressure) and subjective (self-reported anxiety), were measured. Findings indicated that contrary to the hypothesis, viewing a picture of one’s personal pet did not reduce stress reactivity, measured either subjectively (self-report) or objectively (physiological assessment). However, the study suggests that various images can influence stress reactivity.


2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Dorottya Rusz ◽  
Erik Bijleveld ◽  
Michiel A. J. Kompier

Over a hundred prior studies show that reward-related distractors capture attention. It is less clear, however, whether and when reward-related distractors affect performance on tasks that require cognitive control. In this experiment, we examined whether reward-related distractors impair performance during a demanding arithmetic task. Participants (N = 81) solved math problems, while they were exposed to task-irrelevant stimuli that were previously associated with monetary rewards (vs. not). Although we found some evidence for reward learning in the training phase, results from the test phase showed no evidence that reward-related distractors harm cognitive performance. This null effect was invariant across different versions of our task. We examined the results further with Bayesian analyses, which showed positive evidence for the null. Altogether, the present study showed that reward-related distractors did not harm performance on a mental arithmetic task. When considered together with previous studies, the present study suggests that the negative impact of reward-related distractors on cognitive control is not as straightforward as it may seem, and that more research is needed to clarify the circumstances under which reward-related distractors harm cognitive control.


2021 ◽  
pp. 85-101
Author(s):  
Debatri Chatterjee ◽  
Rahul Gavas ◽  
Roopkatha Samanta ◽  
Sanjoy Kumar Saha

Anthrozoös ◽  
2019 ◽  
Vol 32 (4) ◽  
pp. 519-532 ◽  
Author(s):  
Natalie Ein ◽  
Marilyn Hadad ◽  
Maureen J. Reed ◽  
Kristin Vickers

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