Phase Fluctuation Analysis in Functional Brain Networks of Scaling EEG for Driver Fatigue Detection

The characterization of complex patterns arising from electroencephalogram (EEG) is an important problem with significant applications in identifying different mental states. Based on the operational EEG of drivers, a method is proposed to characterize and distinguish different EEG patterns. The EEG measurements from seven professional taxi drivers were collected under different states. The phase characterization method was used to calculate the instantaneous phase from the EEG measurements. Then, the optimization of drivers’ EEG was realized through performing common spatial pattern analysis. The structures and scaling components of the brain networks from optimized EEG measurements are sensitive to the EEG patterns. The effectiveness of the method is demonstrated, and its applicability is articulated.</p

Regression Based Continuous Driving Fatigue Estimation: Towards Practical Implementation

Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG) based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a pre-processing pipeline with low computational complexity, which can be easily and practically implemented in real-time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising towards practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-second window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75%, the proposed framework retained comparable performance in fatigue estimation. This study demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation

Topological Changes in the Functional Brain Networks Induced by Isometric Force Exertions Using a Graph Theoretical Approach: An EEG-based Neuroergonomics Study

Neuroergonomics, the application of neuroscience to human factors and ergonomics, is an emerging science focusing on the human brain concerning performance at work and in everyday settings. The advent of portable neurophysiological methods, including electroencephalography (EEG), has enabled measurements of real-time brain activity during physical tasks without restricting body movements. However, the EEG signatures of different physical exertion activity levels that involve the musculoskeletal system in everyday settings remain poorly understood. Furthermore, the assessment of functional connectivity among different brain regions during different force exertion levels remains unclear. One approach to investigating the brain connectome is to model the underlying mechanism of the brain as a complex network. This study applied employed a graph-theoretical approach to characterize the topological properties of the functional brain network induced by predefined force exertion levels, namely extremely light (EL), light (L), somewhat hard (SWH), hard (H), and extremely hard (EH) in two frequency bands, i.e., alpha and beta. Twelve female participants performed an isometric force exertion task and rated their perception of physical comfort at different physical exertion levels. A CGX-Mobile-64 EEG was used for recording spontaneous brain electrical activity. After preprocessing the EEG data, a source localization method was applied to study the functional brain connectivity at the source level. Subsequently, the alpha and beta networks were constructed by calculating the coherence between all pairs of 84 brain regions of interests that were selected using Brodmann Areas. Graph -theoretical measures were then employed to quantify the topological properties of the functional brain networks at different levels of force exertions at each frequency band. During an \u27extremely hard\u27 exertion level, a small-world network was observed for the alpha coherence network, whereas an ordered network was observed for the beta coherence network. The results suggest that high-level force exertions are associated with brain networks characterized by a more significant clustering coefficient, more global and local efficiency, and shorter characteristic path length under alpha coherence. The above suggests that brain regions are communicating and cooperating to a more considerable degree when the muscle force exertions increase to meet physically challenging tasks. The exploration of the present study extends the current understanding of the neurophysiological basis of physical efforts with different force levels of human physical exertion to reduce work-related musculoskeletal disorders

Cognitive fatigue in young, middle-aged, and older people: behavioral and functional neuroimaging investigations

In our modern societies, humans are constantly cognitively solicited until a relatively advanced age. This continuous cognitive stimulation can obviously be experienced at work but it can also more insidiously come from overcrowded environments, social networks, or constant advertisement on the internet, which eventually bury people in an uninterrupted flow of information. Cognitive fatigue has progressively become one of the most prevalent causes of accidents in everyday life (Dinges, 1995; Shen et al., 2008) but also in the workplace (McCormick et al., 2012). If cognitive fatigue can be considered a normal and adaptive response to long-lasting tasks (Boksem & Tops, 2008), it can also lead to tragic consequences in certain professions. For example, studies have already found evidence of attention drops in airplane pilots (Bartlett, 1943) or large speed variation in car or train drivers under cognitive fatigue (Brown, 1994; Campagne et al., 2004; Kecklund & Akerstedt, 1993; Torsvall & Akerstedt, 1987). This phenomenon is also striking in emergency services like in firefighters (Aisbett & Nichols, 2007; Aisbett et al., 2012; Ferguson et al., 2016) and in intensive care unit physicians (Maltese et al., 2016). When continuously exposed to cognitive fatigue, some individuals can unfortunately develop the so-called burnout condition (Maslach et al., 2001) with its inherent costs for the public health care system but also for the employer (Ricci et al., 2007), in addition to the burden for the individual.Cognitive fatigue can be observed in various domains: Blain et al. (2016) showed that daylong intense cognitive work tends to enhance impulsivity in economic decisions. Likewise, cognitive fatigue has been shown to impair economic decisions, preferences, strategies (Mullette-Gillman et al., 2015), emotion regulation (Grillon et al., 2015), as well as cognitive flexibility in university students (Plukaard et al., 2015). In the sport domain, cognitive fatigue has also been found to alter soccer-specific decision-making (Smith et al., 2016), intermittent running performance (Smith et al., 2015) as well as table tennis performance (Le Mansec et al., 2018).In more severe cases, cognitive fatigue can further develop into a permanent condition, such as Chronic Fatigue Syndrome (CFS; Tanaka & Watanabe, 2010). Cognitive fatigue is also frequently reported in psychological conditions such as depression (Demyttenaere et al., 2005; Lavidor et al., 2002) and neurological illnesses such as Parkinson’s disease (PD), Multiple Sclerosis (MS), traumatic brain injury, stroke, myasthenia gravis, amyotrophic lateral sclerosis, or postpolio syndrome (Chaudhuri & Behan, 2000; Kluger et al., 2013). Obviously, given the potentially tragic consequences of cognitive fatigue, studies are needed to better understand this phenomenon. On the other hand, medical progress has radically increased life expectancy in the last decades, reaching the age of 81.44 in Belgium (in 2017). At the same time, people have been progressively required to work until a more advanced age although diminished cognitive functioning efficiency has been found in older age (Collette & Salmon, 2014; Crawford et al., 2000; Salthouse et al., 2003; West, 1996, 2000). Therefore, it seems crucial to become aware of how cognitive fatigue manifests in advancing age. Surprisingly, very few studies have investigated cognitive fatigue, behaviorally or at the cerebral level, in aging populations.In addition to older age, the middle-aged population also seems particularly at risk for cognitive fatigue. Indeed, midlife has sometimes been considered as the most challenging life period due to the presence of many cognitive requirements (children to care for, work, social life, everyday duties). However, middle-aged people have scarcely been the focus of interest in the literature, probably because of the difficulty reaching this busy population. In an attempt to understand cognitive fatigue at different life stages, studies presented in this Thesis work have systematically focused on three age groups: young, middle-aged, and older people. The first chapter of this Thesis work starts by presenting definitions and models of cognitive fatigue, from those emphasizing energy depletion as the consequence of long-lasting work to those integrating notions that more particularly focus on the voluntarily controlled effort (e.g., executive function, costs/benefits or effort/reward calculation, opportunity cost) invested by the individual into a cognitive activity. For the sake of completeness, this chapter ends by the presentation of some pathological fatigue models. The second chapter describes studies investigating cognitive fatigue in young people. This chapter makes the distinction between experimental protocols based on the Time-on-Task approach (i.e., performing a unique long-lasting task) and those based on the Probe approach (i.e., performing two consecutive tasks in order to test transfer fatigue effects from the first to the second). The presentation of studies also distinguishes between objective (behavioral, electrophysiologic, neuroimaging, connectivity, motivation-related) and subjective (self-reported scales) assessment of cognitive fatigue.The third chapter is dedicated to the presentation of models of cognitive and cerebral aging. It starts by describing cognitive functions that are known to decline with age as well as potential mediators (i.e., processing speed and inhibition) of age-related declines. It presents the well-recognized patterns of cognitive reserve (Stern, 2002, 2009) as well as cerebral compensation postulated in the PASA, ELSA, CRUNCH, and HAROLD hypotheses. It also presents models that more largely integrate factors potentially influencing cognitive aging (Dennis & Cabeza, 2013; STAC; STAC-R) as well as the hypothesis of the declining dopaminergic system. This chapter ends by describing cognitive efficiency in the middle-aged population. The last introductory chapter is dedicated to the presentation of studies about cognitive fatigue in older as well as in middle-aged population. Regarding the experimental part of the Thesis, the first study was based on a Time-on-Task approach in which a 160-minute Stroop task was continuously administrated to young, middle-aged, and older people in order to test performance decrement (increase in extreme reaction times (RTs)) as a function of both the time spent on task and age. The second study was based on the same protocol as the first one, except that rest breaks were given every 40 minutes. This study allowed us to test whether periodically interrupting the task with short breaks (5 minutes) might relieve cognitive fatigue and allow people to maintain performance. The extent to which the three age groups benefit from breaks was also investigated.The third study used a Probe approach in which a fatigue condition (i.e., a long-lasting Stroop task) or a control condition (i.e., watching videos) was directly followed by an N-Back task during functional magnetic resonance imaging (fMRI) acquisition. This procedure allowed us to test whether cerebral activity is differentially modulated by a fatigue state as a function of age. This work ends by a general discussion of the results of the three studies and proposes future lines of investigation in this research field.We hope our results will contribute to advance knowledge about cognitive fatigue in aging and will be the starting point of many other studies afterwards. We already thank all readers for their interest and wish them a compelling reading