Quantitative Assessment of the Training Improvement in a Motor-Cognitive Task by Using EEG, ECG and EOG Signals

Abstract

Generally, the training evaluation methods consist in experts supervision and qualitative check of the operator's skills improvement by asking them to perform specific tasks and by verifying the final performance. The aim of this work is to find out if it is possible to obtain quantitative information about the degree of the learning process throughout the training period by analyzing neuro-physiological signals, such as the electroencephalogram, the electrocardiogram and the electrooculogram. In fact, it is well known that such signals correlate with a variety of cognitive processes, e.g. attention, information processing, and working memory. A group of 10 subjects have been asked to train daily with the NASA multi-attribute-task-battery. During such training period the neuro-physiological, behavioral and subjective data have been collected. In particular, the neuro-physiological signals have been recorded on the first (T1), on the third (T3) and on the last training day (T5), while the behavioral and subjective data have been collected every day. Finally, all these data have been compared for a complete overview of the learning process and its relations with the neuro-physiological parameters. It has been shown how the integration of brain activity, in the theta and alpha frequency bands, with the autonomic parameters of heart rate and eyeblink rate could be used as metric for the evaluation of the learning progress, as well as the final training level reached by the subjects, in terms of request of cognitive resources.

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

“…Several empirical investigations have suggested that performance declines at either far ends of the workload demand profile, i.e., when event rates are excessively high ( overload ) or extremely low ( underload ) (Yerkes and Dodson, 1908; Calabrese, 2008). Therefore, it is crucial to have a reliable estimation of the actual mental workload experienced by the operator along the execution of the task, in order to make the user interface able to preserve a proper level of the user's mental workload, avoiding under- or overload state (Hancock and Warm, 1989; Borghini et al, 2012, 2015b). In this regard, neurophysiological techniques have been demonstrated to be able to assess mental workload of humans with a high reliability, even in operational environments (Mühl et al, 2014; Borghini et al, 2015a; Di Flumeri et al, 2015).…”

ICA-Derived EEG Correlates to Mental Fatigue, Effort, and Workload in a Realistically Simulated Air Traffic Control Task

“…For example, theta band power changes were usually found at the frontal midline channels to be linked to the development of mental fatigue (Yamamoto and Matsuoka, 1990; Gevins et al, 1995; Onton et al, 2005; Chai et al, 2016), variations of mental effort (Miyata et al, 1990), and mental workload (Yamamoto and Matsuoka, 1990; Gundel and Wilson, 1992). Alpha band power changes sensitive to complex motor function (Pfurtscheller et al, 1994), mental workload, and mental effort in attentive stimulus processing and expectancy (Ray and Cole, 1985; Keil et al, 2001; Lin et al, 2010) were identified over the centro-parietal and parietal areas (Gevins et al, 1995; Gevins and Smith, 1999; Borghini et al, 2016; de Vries et al, 2017). Alpha power changes are also associated with reduction in attention with TOT (Klimesch, 1999; Schier, 2000).…”

A New Perspective for the Training Assessment: Machine Learning-Based Neurometric for Augmented User's Evaluation

“…This second phase requires longer time. The literature dealing with the effect of practice on the functional anatomy of task performance is extensive and complex, comprising a wide range of papers from disparate research perspectives (Chein and Schneider, 2005; Doyon and Benali, 2005; Parsons et al, 2005; Erickson et al, 2007; Dux et al, 2009; Wiestler and Diedrichsen, 2013; Parasuraman and McKinley, 2014; Sampaio-Baptista et al, 2014; Borghini et al, 2016). Across these studies, three main patterns of practice-related activation change can be distinguished.…”

“…In fact, one of the most prominent neurophysiological events linked to the increase of information processing, working memory, decision making process, and sustained attention (Botvinick et al, 2004; Mitchell et al, 2008) is the increase (e.g., synchronization ) of the theta activity over the prefrontal and frontal brain areas (Berka et al, 2007; Berka and Johnson, 2011; Galán and Beal, 2012; Jaušovec and Jaušovec, 2012; Borghini et al, 2013, 2016; Mackie et al, 2013; Cartocci et al, 2015). Additionally, frontal theta synchronization has also been demonstrated to be correlated with memory load (Jensen and Tesche, 2002), task difficulty (Gevins et al, 1997; Aricò et al, 2015), error processing (Luu et al, 2004), and recognition of previously viewed stimuli (Arrighi et al, 2016).…”