The neural effects of positively and negatively re-experiencing mental fatigue sensation: a magnetoencephalography study

Ishii, Akira; Ishizuka, Takuya; Muta, Yuki; Tanaka, Masaaki; Yamano, Emi; Watanabe, Yasuyoshi

doi:10.1007/s00221-018-5260-0

Cited by 8 publications

(4 citation statements)

References 55 publications

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“…A recent fMRI study showed that the left BA 7 was connected with the whole task-related brain regions during the performance of the symbol digit modalities test and that the BA 7 works as an in-between hub connecting the task related brain regions to other brain regions in the default mode network [47], parts of which were involved in signaling of reward outcomes [48] and achievement motivation [49]. In fact, it has been reported that oscillatory brain activity in BA 7 assessed by MEG was associated with increased motivation to perform forthcoming cognitive task [50]. Taking theses into consideration, it can be hypothesized that the BA 7 is a key brain region in enhancing cognitive performance through motivation.…”

Section: Plos Onementioning

confidence: 99%

Neural correlates of the improvement of cognitive performance resulting from enhanced sense of competence: A magnetoencephalography study

et al. 2021

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The alterations in neural activity related to the improvement of cognitive performance, which would be leading to better academic performance, remain poorly understood. In the present study, we assessed neural activity related to the improvement of task performance resulting from academic rewards. Twenty healthy male volunteers participated in this study. All participants performed four sessions of a 1-back-Stroop task under both target and control conditions. An image indicating that the task performance of each participant was above average and categorized as being at almost the highest level was presented immediately after each session under the target condition, whereas a control image did not indicate task performance. Neural activity during the 1-back-Stroop task was recorded by magnetoencephalography. The correction rate of the 1-back-Stroop task in the final session relative to that in the first under the target condition was increased compared with the control condition. Correlation analysis revealed that the decreases in alpha band power in right Brodmann’s area (BA) 47 and left BA 7 were positively associated with the increased correction rate caused by the target condition. These findings are expected to contribute to a better understanding of the neural mechanisms underlying the improvement of cognitive performance.

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Section: Plos Onementioning

confidence: 99%

Neural correlates of the improvement of cognitive performance resulting from enhanced sense of competence: A magnetoencephalography study

et al. 2021

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“…More recently, we performed the MEG study (Ishii et al 2018) to examine whether thinking positively about the fatigue sensation would increase motivation to accomplish the workload. Fourteen healthy male volunteers participated in this study and performed a two-back test for 30 min to induce mental fatigue sensation.…”

Section: Meg Studymentioning

confidence: 99%

Integrated Imaging on Fatigue and Chronic Fatigue

Watanabe¹,

Tanaka

Ishii

et al. 2019

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“…Analysing the spectral content of EEG signals allows different waves of interest to be identified. For example, the Alpha wave ) is linked to relaxation and creativity, while the Theta (4-8Hz) and Beta waves indicate the concentration and alertness levels [6,7], respectively. Regarding the analysis of ECG data, it has been reported that the Heart Rate Variation (HRV) helps to characterize drowsiness [8].…”

Section: Introductionmentioning

confidence: 99%

Drowsiness Detection Using Joint EEG-ECG Data With Deep Learning

Geoffroy

Chaâri

Tourneret

et al. 2021

2021 29th European Signal Processing Conference (EUSIPCO)

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Drowsiness detection is still an open issue, especially when detection is based on physiological signals. In this sense, light non-invasive modalities such as electroencephalography (EEG) are usually considered. EEG data provides informations about the physiological brain state, directly linked to the drowsy state. Electrocardigrams (ECG) signals can also be considered to involve informations related to the heart state. In this study, we propose a method for drowsiness detection using joint EEG and ECG data. The proposed method is based on a deep learning architecture involving convolutional neural networks (CNN) and recurrent neural networks (RNN). High efficiency level is obtained with accuracy scores up to 97% on validation set. We also demonstrate that a modification of the proposed architecture by adding autoencoders helps to compensate the performance drop when analysing subjects whose data is not presented during the learning step.

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The neural effects of positively and negatively re-experiencing mental fatigue sensation: a magnetoencephalography study

Cited by 8 publications

References 55 publications

Neural correlates of the improvement of cognitive performance resulting from enhanced sense of competence: A magnetoencephalography study

Neural correlates of the improvement of cognitive performance resulting from enhanced sense of competence: A magnetoencephalography study

Integrated Imaging on Fatigue and Chronic Fatigue

Drowsiness Detection Using Joint EEG-ECG Data With Deep Learning

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