应用直接心理生理指标的基于脑电图的神经工效学集成便携式系统的理论设计

Awad M. Aljuaid
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引用次数: 5

摘要

确保人的心智和认知能力及其局限性是至关重要的,近年来随着技术的快速发展,这一问题得到了深入的研究。量化和预测人在工作环境中的身心状态(如行为、功能、绩效)是非常重要的,在设计工作任务和工作场所时总是要考虑到这一点,以提高绩效和降低风险。在工作环境中,注意力不集中、困倦、头晕、愤怒和压力通常会发生,并可能导致严重的后果。本文提出了一种基于心理生理神经工效学的直接神经成像指标集成便携式系统的设计。在系统设计和编程中使用的人体生物信号的心理生理测量是脑电图、肌电图、血压、体温和心率。这种心理生理信息是使用采集的数据和ON/OFF控制算法来测量的。提出的设计包括两种设置;贴在头皮上,贴在手臂上。该系统将使用脑机接口(BCI)方法实时提供所有心理生理信息,如果出现不必要的心理和认知行为,它将向操作员和主管发出警报并通知。该设备的局限性可能会激怒用户,如果工人进行高强度的肌肉运动,则大脑EEG信号需要强大的伪影去除。本文的预期结果将为工作场所的全面人为监控开辟一条新的途径,从而提高绩效并降低潜在的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Theoretical design of EEG-based Neuroergonomics integrated portable system, applying direct Psychophysiological indicators
Ensuring human mental and cognitive capabilities and limitations is crucial and it is studied deeply with the rapid development in technology recently. Quantifying and predicting human physical and mental state (e.g. Behavior, function, performance) in work environment is very important and always considered in designing job tasks and workplace to increase the performance and reduce risks. Lack/excess of attention, drowsiness, dizziness, anger and stress are usually occur in work environment and may lead to severe consequences. This paper presents design of an integrated portable system applying direct neuroimaging indicators based on psychophysiological neuroergonomics in addition to the physiological indicators. The psychophysiological measures of human biosignals which used in the design and programming of the proposed system design are EEG, EMG, blood pressure, body temperature and heart rate. This psychophysiological information is measured using acquired data and the ON/OFF control algorithm. The proposed design includes two setups; to be attached to the scalp and to be placed on the arm. The system will keep live feeds of all psychophysiological information using BCI approach and it will alert and notify both operator and supervisors if there is unwanted mental and cognitive behavior. Limitations of the device could be irritating to the user, and the brain EEG signals need a robust artifact removal if the worker perform high muscular effort. The expected results of the proposed paper will open a new approach of a comprehensive human monitoring in workplace as a result it will increase the performance and reduce potential of risks.
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