Aricò P, Borghini G, Di Flumeri G, Colosimo A, Pozzi S, Babiloni F
University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome, Italy; Neuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy.
University of Rome "Sapienza", Rome, Italy.
Prog Brain Res. 2016;228:295-328. doi: 10.1016/bs.pbr.2016.04.021. Epub 2016 Jun 3.
In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states. The objective of the proposed study was to provide an example of p-BCIs used to evaluate the users' mental workload in a real operational environment. For this purpose, through the facilities provided by the École Nationale de l'Aviation Civile of Toulouse (France), the cerebral activity of 12 professional air traffic control officers (ATCOs) has been recorded while performing high realistic air traffic management scenarios. By the analysis of the ATCOs' brain activity (electroencephalographic signal-EEG) and the subjective workload perception (instantaneous self-assessment) provided by both the examined ATCOs and external air traffic control experts, it has been possible to estimate and evaluate the variation of the mental workload under which the controllers were operating. The results showed (i) a high significant correlation between the neurophysiological and the subjective workload assessment, and (ii) a high reliability over time (up to a month) of the proposed algorithm that was also able to maintain high discrimination accuracies by using a low number of EEG electrodes (~3 EEG channels). In conclusion, the proposed methodology demonstrated the suitability of p-BCI systems in operational environments and the advantages of the neurophysiological measures with respect to the subjective ones.
在过去几十年里,这一直是神经科学领域中一个快速发展的概念。被动式脑机接口(p-BCI)系统通过利用操作员的隐蔽脑活动(如心理负荷),能够在操作环境中改善人机交互(HMI)。然而,p-BCI技术在实验室之外使用时可能会遇到一些实际问题。特别是,最重要的限制之一是每次使用p-BCI系统之前都需要重新校准,以避免在检测所考虑的心理状态时其可靠性大幅降低。本研究的目的是提供一个p-BCI用于评估实际操作环境中用户心理负荷的示例。为此,通过法国图卢兹国立民用航空学院提供的设施,在12名专业空中交通管制员(ATCO)执行高度逼真的空中交通管理场景时记录了他们的大脑活动。通过分析ATCOs的大脑活动(脑电图信号-EEG)以及被检查的ATCOs和外部空中交通管制专家提供的主观负荷感知(即时自我评估),能够估计和评估管制员操作时心理负荷的变化。结果表明:(i)神经生理学评估与主观负荷评估之间存在高度显著的相关性;(ii)所提出的算法具有很高的时间可靠性(长达一个月),该算法通过使用少量EEG电极(约3个EEG通道)也能够保持较高的辨别准确率。总之,所提出的方法证明了p-BCI系统在操作环境中的适用性以及神经生理学测量相对于主观测量的优势。