Psycho Physiological and Subjective Responses to Mental Workload Levels during N-Back Task

Fallahi, Majid; Heidarimoghadam, Rashid; Motamedzade, Majid; Farhadian, Maryam

doi:10.4172/2165-7556.1000181

Cited by 12 publications

(10 citation statements)

References 36 publications

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“…Slightly higher accuracy was expected because the N-back is a more sustained and controlled stressor. Further, the N-back task is well validated at eliciting different levels of mental workload, specifically different levels of working memory, and physiological stress indices [48,78]. In contrast, the VR-ISS was a more complex task involving a variety of stressors including, noise, task load, decreased visibility, and simulated physical threat.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

et al. 2023

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When training for hazardous operations, real-time stress detection is an asset for optimizing task performance and reducing stress. Stress detection systems train a machine-learning model with physiological signals to classify stress levels of unseen data. Unfortunately, individual differences and the time-series nature of physiological signals limit the effectiveness of generalized models and hinder both posthoc stress detection and real-time monitoring. This study evaluated a personalized stress detection system that selects a personalized subset of features for model training. The system was evaluated post-hoc for realtime deployment. Further, traditional classifiers were assessed for error caused by indirect approximations against a benchmark, optimal probability classifier (Approximate Bayes; ABayes). Healthy participants completed a task with three levels of stressors (low, medium, high), either a complex task in virtual reality (responding to spaceflight emergency fires, n=27) or a simple laboratory-based task (N-back, n=14). Heart rate, blood pressure, electrodermal activity, and respiration were assessed. Personalized features and different interval window sizes were compared. Classification performance was compared for ABayes, support vector machine, decision tree, and random forest. The results demonstrate that a personalized model with time series intervals can classify three stress levels with high accuracy better than a generalized model. However, crossvalidation and holdout performance varied for traditional classifiers vs. ABayes, suggesting error from indirect approximations. The selected features changed with windows size and differed between tasks, but blood pressure contributed to the most prominent features. The capability to account for individual difference is a prominent advantage of personalized models, and will likely have a growing presence in future detection systems.

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Section: Discussionmentioning

confidence: 99%

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

et al. 2023

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“…The participant has to respond by using the keyboard when the position of the currently displayed frame is the same as the one previously presented. This type of a workload is comparable to monotonous monitoring tasks where the participant has to maintain his attention at the same level task ("1-back position") [27]. A sample screenshot for position 1-Back task is shown in Figure 2.…”

Section: Figure 1 the Outline Of The Experimental Taskmentioning

confidence: 99%

Evaluation of the EEG Signals and Eye Tracker Data for Working Different N-Back Modes

Gündoğdu¹,

Doğan²,

Gülbetekin³

et al. 2019

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In this study, it is aimed to determine the effects of different modes N-back test which is one of the measurement tools frequently used in measurement of working memory, on the Electroencephalography (EEG) and Eye-tracker data. Eight healthy volunteers participated in this study. The volunteers performed tasks inducing stress and mental fatigue for almost 4 minutes. Each experimental task consists of 72-seconds stress and fatigue inducing 1-position back, 2-position-color and 2-position-image test sessions and three evaluation sessions performed for task. During these sessions, the volunteers were assessed using EEG, Eye Tracker and Visual analogue scale (VAS). VAS was also used to evaluate perceived stress and mental fatigue before and after the N-back test. Power values of EEG signals from volunteers for different test modes were evaluated according to test scores in theta, alpha, beta, and low gamma bands. According to the obtained results, the power values in each subband of the EEG change according to the test mode and the test scores. Beta, alpha and theta frequency bands' power in the frontal cortex (AF7+AF8) increased with the n back test score and difficulty level of the game. When the task gets harder, it shows that the heat map of eye tracking is spread over a wider area. The VAS scores for both mental fatigue and stress increased after the Nback test with low and high test scores. According to the graphical results obtained from EEG with subjective evaluation, stress and mental fatigue increased with N-back tests. These results help understanding of the physiological changes of stress and mental fatigue and contribute to improve new approaches to assess stress and mental fatigue.

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“…Several studies have demonstrated the ability to classify mental workload associated with various laboratory tasks (e.g. N-back [13], mental arithmetic [14], Sternberg memory [15], auditory oddball target paradigm [16,17], visual search [17], and a laboratory version of air traffic control for vigilance tests [18]), as well as in more realistic task scenarios like flight [1,17,[19][20][21] or driving [1,22] simulation, using EEG signals. Typically in such studies, different levels of mental workload are induced by altering the difficulty of the task being performed or by introducing secondary tasks.…”

Section: Introductionmentioning

confidence: 99%

EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing

Massaeli,

Power

2024

J. Neural Eng.

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Objective. To date, most research on EEG-based mental workload detection for passive BCI applications has focused on identifying the overall level of cognitive resources required, such as whether the workload is high or low. We propose, however, that being able to determine the specific type of cognitive resources being used, such as visual or auditory, would also be useful. This would enable the pBCI to take more appropriate action to reduce the overall level of cognitive demand on the user. For example, if a high level of workload was detected and it is determined that the user is primarily engaged in visual information processing, then the pBCI could cause some information to be presented aurally instead. In our previous work we showed that EEG could be used to differentiate visual from auditory processing tasks when the level of processing is high, but the two modalities could not be distinguished when the level of cognitive processing demand was very low. The current study aims to build on this work and move toward the overall objective of developing a pBCI that is capable of predicting both the level and the type of cognitive resources being used. Approach. Fifteen individuals undertook carefully designed visual and auditory tasks while their EEG data was being recorded. In this study, we incorporated a more diverse range of sensory processing conditions including not only single modality conditions (i.e., those requiring one of either visual or auditory processing) as in our previous study, but also dual-modality conditions (i.e., those requiring both visual and auditory processing) and no-task/baseline conditions (i.e., when the individual is not engaged in either visual or auditory processing). Main results. Using regularized linear discriminant analysis within a hierarchical classification algorithm, the overall cognitive demand was predicted with an accuracy of more than 86%, while the presence or absence of visual and auditory sensory processing were each predicted with an accuracy of approximately 70%. Significance. The findings support the feasibility of establishing a pBCI that can determine both the level and type of attentional resources required by the user at any given moment. This pBCI could assist in enhancing safety in hazardous jobs by triggering the most effective and efficient adaptation strategies when high workload conditions are detected.

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Psycho Physiological and Subjective Responses to Mental Workload Levels during N-Back Task

Cited by 12 publications

References 36 publications

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

Evaluation of the EEG Signals and Eye Tracker Data for Working Different N-Back Modes

EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing

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