EEG Theta Power Activity Reflects Workload among Army Combat Drivers: An Experimental Study

Díaz-Piedra, Carolina; Sebastián, María Victoria; Stasi, Leandro L. Di

doi:10.3390/brainsci10040199

Cited by 38 publications

(24 citation statements)

References 77 publications

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“…After removing the outliers, ANOVA results showed that the differences PSD response were mainly concentrated in all regions of θ band, prefrontal and occipital regions of α band, and occipital regions of 1 and 2 band. and PO5, showed a good correlation with task difficulty and increased with the increase of load level, which was consistent with the previous research [28]; Power of β-band in O1 and PO5 also increased with the increase of task load; The α-band energy of O1 channel is negatively correlated with the load, which is consistent with the study [12]. After the brain enters the working state, local neuronal activations increase metabolic rate, leading to increased blood flow and volume [29].…”

Section: Eeg Features Change Analysis Resultssupporting

confidence: 92%

Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

Chu¹,

Cao²,

Jiang³

et al. 2021

Preprint

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Background: Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating EEG and fNIRS signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application.Method: The signal acquisition configuration was optimized and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical MATB task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected.Results: A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 78.25±4.71%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O2Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of 𝛽1 and 𝛽2 bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O2Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks.Conclusions: The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 78.25±4.71% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.

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Section: Eeg Features Change Analysis Resultssupporting

confidence: 92%

Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

Chu¹,

Cao²,

Jiang³

et al. 2021

Preprint

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“…These can be said to be in line with Swarnalatha (2013) statement that the concept of workload has diverse facets to include physical, mental, physiological demand and frustration stages. This is because physical, physiological and mental loads that arise from these conflicts, challenges, combats and wars tend to amplify military personnel workload (Hollands, Spivak, & Kramkowski, 2019), which in turn compromise their operational performance , combat experiences (Mao, Xie, Hu, & Su, 2014), job characteristics, thriving at work (DeBusk, Hill, Chander, Knight, & Babski-Reeves, 2018), organizational commitment and job satisfaction (Diaz-Piedra, Sebastián, & Di Stasi, 2020) and career adaptation. Military workload in countries facing challenges, conflicts, Fulani banditry and resurgences is unavoidable, no matter the type of cognitive and physical training introduced by the defense organization designed to enhance practical and elevated-order cognitive ability (Ujoatuonu, Onyishi, Okafor, Amazue, & Umeh, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The answer is inconclusive as Nigerian military tasks most times require soldiers' participation in politics (Ujoatuonu, Kanu, & Apex-Apeh, 2017) which can lead to mental fatigue, increased workload, decreased performance and influence happiness. As a soldier, being in the warfront means facing life threatening work situation and workload for one's nation, state and family (Diaz-Piedra, Sebastián, & Di Stasi, 2020) which can influence experiences of happiness. Fear of being attacked at combat, non-payment of allowances, questions of meaning in life and downplayed causalities of military personnel in combat (Ujoatuonu, Kanu, Ugwuibe, & Mbah, 2019) are other factors that might alter the experiences of happiness and workload of Nigerian soldiers.…”

Section: Introductionmentioning

confidence: 99%

Roles of Fear of Happiness and Fragility of Happiness in Nigerian Military Personnel Workload

Ujoatuonu¹,

Okafor²,

Kanu³

et al. 2020

IJPS

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In the light of conflicts and Boko Haram insurgency facing Nigeria as a county and the involvement of the military in the fight against terrorists, the study of workload and happiness need to be researched more to gain greater acceptance and become one of the global indices on which nations’ military are measured and ranked. Our study anchors on multiple resource and information processing theory to assess the roles of fear and fragility of happiness in workload among Nigerian military personnel. Two hundred and twenty (220) Nigerian soldiers, drawn from Nigerian Army and Nigerian Air Force participated in this study. Three instruments , namely, Workload Scale, Fear of Happiness Scale and Fragility of Happiness Scale, were used to collect data from the participants. Results of hierarchical multiple regression analysis indicated that fear of happiness was not significantly associated with work load. Fragility of happiness positively predicted work load, indicating that military personnel who had greater fragility of happiness reported greater workload. It was concluded that workplace interventions and programs to curtail fragility of happiness would be helpful in reducing the perceptions of excess workload among Nigerian military personnel.

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“…This, hypothetically, may explain that changes in the neurophysiological measures were not found. For instance, it could be expected, as occurred in the healthy group, that beta and theta bands would increase during the DT condition due to higher cognitive demands or workload ( Fuentes-García et al, 2019 ; Diaz-Piedra et al, 2020 ). This is because increases in beta and theta bands, due to increases in cognitive workload, could be expected.…”

Section: Discussionmentioning

confidence: 99%

Neurophysiological Differences Between Women With Fibromyalgia and Healthy Controls During Dual Task: A Pilot Study

Villafaina¹,

Fuentes-García²,

Cano-Plasencia³

et al. 2020

Front. Psychol.

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Background Women with FM have a reduced ability to perform two simultaneous tasks. However, the impact of dual task (DT) on the neurophysiological response of women with FM has not been studied. Objective To explore both the neurophysiological response and physical performance of women with FM and healthy controls while performing a DT (motor–cognitive). Design Cross-sectional study. Methods A total of 17 women with FM and 19 age- and sex-matched healthy controls (1:1 ratio) were recruited. The electroencephalographic (EEG) activity was recorded while participants performed two simultaneous tasks: a motor (30 seconds arm-curl test) and a cognitive (remembering three unrelated words). Theta (4–7 Hz), alpha (8–12 Hz), and beta (13–30) frequency bands were analyzed by using EEGLAB. Results Significant differences were obtained in the healthy control group between single task (ST) and DT in the theta, alpha, and beta frequency bands ( p -value < 0.05). Neurophysiological differences between ST and DT were not found in women with FM. In addition, between-group differences were found in the alpha and beta frequency bands between healthy and FM groups, with lower values of beta and alpha in the FM group. Therefore, significant group ∗ condition interactions were detected in the alpha and beta frequency bands. Regarding physical condition performance, between groups, analyses showed that women with FM obtained significantly worse results in the arm curl test than healthy controls, in both ST and DT. Conclusion Women with FM showed the same electrical brain activity pattern during ST and DT conditions, whereas healthy controls seem to adapt their brain activity to task commitment. This is the first study that investigates the neurophysiological response of women with FM while simultaneously performing a motor and a cognitive task.

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EEG Theta Power Activity Reflects Workload among Army Combat Drivers: An Experimental Study

Cited by 38 publications

References 77 publications

Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

Optimized EEG–fNIRS Based Mental Workload Detection Method for Practical Applications

Roles of Fear of Happiness and Fragility of Happiness in Nigerian Military Personnel Workload

Neurophysiological Differences Between Women With Fibromyalgia and Healthy Controls During Dual Task: A Pilot Study

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