The interconnection of mental fatigue and aging: An EEG study

Arnau, Stefan; Möckel, Tina; Rinkenauer, Gerhard; Wascher, Edmund

doi:10.1016/j.ijpsycho.2017.04.003

Cited by 64 publications

(45 citation statements)

References 61 publications

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“…A condition specific baseline forces the baseline to be zero and the higher baseline-power is then reflected by larger relative values in post stimulus onset episodes. An increased alpha power in non-stimulus locked segments of the EEG is a well-known phenomenon in the research on mental fatigue (Arnau et al, 2017;Fan et al, 2015;Getzmann et al, 2018). In the context of prolonged cognitive performance, alpha power was found to increase not just as a function of time on task (Arnau et al, 2017;Fan et al, 2015;, but also as a function of task load Wascher et al, 2018).…”

Section: Discussionmentioning

confidence: 92%

The Electrophysiological Signature of Mind Wandering

Arnau

Loeffler

Rummel

et al. 2019

Preprint

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Mind wandering during ongoing tasks can impede task performance and increase the risk of failure in laboratory as well as in daily-life tasks and work environments. Neurocognitive measures like the electroencephalography (EEG) offer the opportunity to assess mind wandering non-invasively without interfering with the primary task. However, the literature on electrophysiological correlates of mind wandering is rather inconsistent. The present study aims towards clarifying this picture by breaking down the temporal dynamics of mind-wandering encounters using a cluster-based permutation approach. Participants performed a switching task during which mind wandering was occasionally assessed via thought probes applied after trial completion at random time points. In line with previous studies, response accuracy was reduced during mind wandering. Moreover, alpha power during the inter-trial interval was significantly increased on those trials on which participants reported that they had been mindwandering. This spatially widely distributed effect is theoretically well in line with recent findings linking an increased alpha power to an internally oriented state of attention.Measurements of alpha power may therefore be used to detect mind wandering online during critical tasks in traffic and industry in order to prevent failures.

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Section: Discussionmentioning

confidence: 92%

The Electrophysiological Signature of Mind Wandering

Arnau

Loeffler

Rummel

et al. 2019

Preprint

Self Cite

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“…Mental fatigue is a complex physiological and psychological phenomenon [14] , which does not have a unified definition in academia [15] . At present, mental fatigue refers to a state of diminished alertness and overall performance decline caused by prolonged cognitive activity [16,17] . Work stress is the main cause of mental fatigue [18] .…”

Section: Discussionmentioning

confidence: 99%

Study on the Preferred Application-Oriented Index for Mental Fatigue Detection

Duan

Zhang

et al. 2018

Preprint

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Most of the research of mental fatigue evaluation mainly concentrated on some indexes that require sophisticate and large instruments which make the detection of mental fatigue cumbersome, time-consuming, and difficult to apply on a large scale. A quick and sensitive mental fatigue detection index is necessary so that mental workers can be alerted in time and take corresponding countermeasures. But to date, no studies have compared the sensitivity of common objective evaluation indexes. To solve these problems this study recruited 56 human subjects. These subjects were evaluated using six fatigue indexes: the Stanford sleepiness scale, digital span, digital decoding, short-term memory, critical flicker fusion frequency (CFF), and speed perception deviation. The results of fatigue tests before and after mental fatigue were compared, and a one-way analysis of variance (ANOVA) was performed on the speed perception deviation. The result indicated the significance of this index. Considering individual differences, the relative fatigue index (RFI) was proposed to compare the sensitivity of the indexes. The results showed that when the self-rated fatigue grade changed from non-fatigue to mild fatigue, the ranges of RFI values for digital span, digital decoding, short-term memory and CFF were 0.175–0.258, 0.194–0.316, 0.068–0.139, and 0.055–0.075, respectively. Correspondingly, when the self-rated fatigue grade changed from non-fatigue to severe fatigue, the ranges of RFI values for the above indexes were 0.175–0.258, 0.194–0.316, 0.068–0.139, and 0.055–0.075, respectively. These results suggest that the sensitivity of the digital decoding, digital span, short-term memory, and CFF decreased sequentially when the self-evaluated fatigue grade changed from no fatigue to mild or severe fatigue. The RFI individuality of the speed perception deviation is highly variable and is not suitable as an evaluation index. In mental fatigue testing, digital decoding testing can provide faster, more convenient, and more accurate results.

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“…The connection between mental fatigue and aging has been studied. The recognition of mental fatigue was found to be efficient when adaptive weights switch in deep belief networks (DBN) [22,23]. Even though DBN has also been applied in the recognition of emotion more studies use SVM which combines feature smoothing or selection methods, such as canonical correlation analysis (CCA) and principal component analysis (PCA) [24][25][26].…”

Section: Related Workmentioning

confidence: 99%

“…By using a fast Fourier transformation, the frequency features (60 power features, 16 power difference features) were prepared. In each channel, the power features were computed on four frequency bands, i.e., theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Power difference features were employed to detect the variation in cerebral activity between the left and right cortical areas.…”

Section: Feature Extraction and The Target Emotion Classesmentioning

confidence: 99%

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

2019

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Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.

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The interconnection of mental fatigue and aging: An EEG study

Cited by 64 publications

References 61 publications

The Electrophysiological Signature of Mind Wandering

The Electrophysiological Signature of Mind Wandering

Study on the Preferred Application-Oriented Index for Mental Fatigue Detection

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

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