Study of EEG characteristics while solving scientific problems with different mental effort

Zhu, Yanmei; Wang, Qian; Zhang, Li

doi:10.1038/s41598-021-03321-9

Cited by 25 publications

(16 citation statements)

References 67 publications

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“…Although not statistically significant, such results align with the previous studies that used ERD/ERS (e.g. Zhu et al, 2021) and TRP (e.g. Jia et al, 2021) for measuring and comparing cognitive load in design activities.…”

Section: Discussionsupporting

confidence: 90%

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Eeg-Based Cognitive Load Indicators in Cad Modelling Tasks of Varying Complexity

2023

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As an initial step towards a better understanding of cognitive load in computer-aided design (CAD), the herein presented study investigated cognitive load imposed on 24 mechanical engineers during two CAD modelling tasks of intentionally different complexity levels. The cognitive load has been rarely studied in the CAD context, which resulted in the lack of understanding if and how the EEG-based indicators available from the literature reflect the changes in cognitive load imposed on engineering designers in CAD activities. Therefore, cognitive load was measured and analysed using three EEG-based indicators to explore insights that might be obtained from them. The initial analysis revealed different cognitive load results from the employed indicators for the same EEG data. In addition, the study implies that the cognitive load results obtained through the used indicators are only partially coherent with the CAD modelling task complexity. Hence, the results imply that the chosen EEG-based indicator matters when measuring and analysing cognitive load in CAD modelling tasks and that its adjustment for CAD context might be needed.

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Section: Discussionsupporting

confidence: 90%

“…The previous studies using the ERD/ERS indicator often observed higher theta ERS and alpha ERD with increasing task complexity and the associated cognitive load (e.g. Zhu et al, 2021). It has been calculated according to the following equation:…”

Section: Eeg-based Cognitive Load Indicatorsmentioning

confidence: 99%

Eeg-Based Cognitive Load Indicators in Cad Modelling Tasks of Varying Complexity

2023

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“…First, too few amputees lead to the lack of universality of results and we will increase the number of amputees in future. Second, more evaluations, such as the time stability of the coding in combination with the forgetting curve ( D’Anna et al, 2019 ), control performance of a prosthetic hand ( Luo et al, 2021 ), and the activation of sensory cortex and mental burden through electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) ( Midha et al, 2021 ; Zhu et al, 2021 ), need to be performed and the results need to be verified. Third, we focused on more reasonable experimental paradigms by setting the stimulation time and rest time ( Buma et al, 2007 ; Marion et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Substitutive proprioception feedback of a prosthetic wrist by electrotactile stimulation

Yichen

Yufeng

et al. 2023

Front. Neurosci.

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ObjectiveSensory feedback of upper-limb prostheses is widely desired and studied. As important components of proprioception, position, and movement feedback help users to control prostheses better. Among various feedback methods, electrotactile stimulation is a potential method for coding proprioceptive information of a prosthesis. This study was motivated by the need for proprioception information for a prosthetic wrist. The flexion-extension (FE) position and movement information of the prosthetic wrist are transmitted back to the human body through multichannel electrotactile stimulation.ApproachWe developed an electrotactile scheme to encode the FE position and movement of the prosthetic wrist and designed an integrated experimental platform. A preliminary experiment on the sensory threshold and discomfort threshold was performed. Then, two proprioceptive feedback experiments were performed: a position sense experiment (Exp 1) and a movement sense experiment (Exp 2). Each experiment included a learning session and a test session. The success rate (SR) and discrimination reaction time (DRT) were analyzed to evaluate the recognition effect. The acceptance of the electrotactile scheme was evaluated by a questionnaire.Main resultsOur results showed that the average position SRs of five able-bodied subjects, amputee 1, and amputee 2 were 83.78, 97.78, and 84.44%, respectively. The average movement SR, and the direction and range SR of wrist movement in five able-bodied subjects were 76.25, 96.67%, respectively. Amputee 1 and amputee 2 had movement SRs of 87.78 and 90.00% and direction and range SRs of 64.58 and 77.08%, respectively. The average DRT of five able-bodied subjects was less than 1.5 s and that of amputees was less than 3.5 s.ConclusionThe results indicate that after a short period of learning, the subjects can sense the position and movement of wrist FE. The proposed substitutive scheme has the potential for amputees to sense a prosthetic wrist, thus enhancing the human-machine interaction.

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“…On the other hand, such spatial and spectral information has several advantages, as it leaves the forehead, essential for working memory and executive control, free to be used for measuring other interesting mental states, such as mental effort. This will reduce the probability of incurring confounding effects between the measure of the two different mental states also from a spectral point of view, since mental effort is usually measured in the theta band [62].…”

Section: Discussionmentioning

confidence: 99%

Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving

Sciaraffa¹,

Flumeri

Germano³

et al. 2022

Brain Sciences

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Driver’s stress affects decision-making and the probability of risk occurrence, and it is therefore a key factor in road safety. This suggests the need for continuous stress monitoring. This work aims at validating a stress neurophysiological measure—a Neurometric—for out-of-the-lab use obtained from lightweight EEG relying on two wet sensors, in real-time, and without calibration. The Neurometric was tested during a multitasking experiment and validated with a realistic driving simulator. Twenty subjects participated in the experiment, and the resulting stress Neurometric was compared with the Random Forest (RF) model, calibrated by using EEG features and both intra-subject and cross-task approaches. The Neurometric was also compared with a measure based on skin conductance level (SCL), representing one of the physiological parameters investigated in the literature mostly correlated with stress variations. We found that during both multitasking and realistic driving experiments, the Neurometric was able to discriminate between low and high levels of stress with an average Area Under Curve (AUC) value higher than 0.9. Furthermore, the stress Neurometric showed higher AUC and stability than both the SCL measure and the RF calibrated with a cross-task approach. In conclusion, the Neurometric proposed in this work proved to be suitable for out-of-the-lab monitoring of stress levels.

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Study of EEG characteristics while solving scientific problems with different mental effort

Cited by 25 publications

References 67 publications

Eeg-Based Cognitive Load Indicators in Cad Modelling Tasks of Varying Complexity

Eeg-Based Cognitive Load Indicators in Cad Modelling Tasks of Varying Complexity

Substitutive proprioception feedback of a prosthetic wrist by electrotactile stimulation

Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving

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