Combined assessment of attentional reserve and cognitive‐motor effort under various levels of challenge with a dry EEG system

Abstract

A novel ERP approach was proposed to index variations in mental workload, particularly in attentional reserve, which is complementary to EEG spectral content thought to reflect mental effort. To our knowledge, no study has assessed mental effort and attentional reserve simultaneously in EEG gel-based and, importantly, dry systems, which are particularly well suited for real-world settings. Therefore, by systematically considering ERP, EEG spectral, and importantly the combination of both, this study examined if a small set of dry EEG electrodes could detect changes in both spectral and ERP metrics to assess the mental workload under various challenges with a similar fidelity to their gel-based counterparts in a laboratory setting. By employing both EEG gel-based and dry systems, the ERP and spectral markers were computed while participants executed a visuomotor task under three levels of challenge. For both EEG systems, more challenging levels of difficulty were associated with concomitant changes in ERP amplitude, and spectral power reflected a reduction of the attentional reserve and an increase in cognitive-motor effort, respectively. Those variations in attentional reserve and cognitive-motor effort collectively indexed mental workload with nearly identical fidelity for both gel-based and dry EEG systems. These findings promise to assess the mental workload in situations where the use of dry EEG systems could be advantageously employed to examine human cognitive-motor performance.

A Comparison of Mental Workload in Individuals with Transtibial and Transfemoral Lower Limb Loss during Dual-Task Walking under Varying Demand

“…Second, this investigation employed a system with preamplified EEG signals and secured shielded cables limiting the influence of motion-related artifact (Kline et al, 2015;Nathan & Contreras-Vidal, 2016;Reis et al, 2014). Third, this study employed similar signal processing methods and obtained similar findings as those from previous work which successfully assessed mental workload via EEG spectral analyses during dual-task walking (Beurskens et al, 2016;De Sanctis et al, 2014;Marcar et al, 2014;Pruziner et al, 2019;Shaw et al, 2018) as well as upper-extremity tasks executed while seated (Dyke et al, 2015;Gentili et al, 2018;Jaquess et al, 2018;Rietschel et al, 2012).…”

“…While EEG theta power is positively related to mental workload, and likely reflects the recruitment of attentional-related processes (e.g., working memory and action monitoring), EEG alpha power is inversely related to cortical activation, which reflects the level of inhibition of nonessential neural processes. Together, these measures can reveal the nature of the brain processes during cognitive-motor performance (Babu Henry Samuel et al, 2018;Chuang et al, 2013;Gentili et al, 2018;Gevins & Smith, 2003;Kao et al, 2013;Rietschel et al, 2012;Wang et al, 2016). The ratio of theta to alpha power at the frontal and parietal midline sites can also serve as a robust index of cognitive-motor effort during both upper-and lower-extremity performance (Hockey et al, 2009;Holm et al, 2009).…”

The Neural Blending of Words and Movement: Event-Related Potential Signatures of Semantic and Action Processes during Motor–Language Coupling

“…For all remaining participants, data were band-pass filtered during recording (0.1-100 Hz) and offline (0.5-40 Hz) to remove undesired frequency components. The latter cutoff (40 Hz) was based on gold-standard recommendations to maximize data quality and temporal precision (Cohen, 2014) and to favor comparability with ERP studies on motor responses (Gentili et al, 2018;Fabi & Leuthold, 2017;Kadosh et al, 2007), action-verb processing (Sokoliuk, Calzolari, & Cruse, 2019;Casado et al, 2018), and other embodied language categories (García et al, 2020;Daly et al, 2019;Gentsch, Sel, Marshall, & Schütz-Bosbach, 2019).…”

A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data

“…Alpha and theta variations were also analyzed by Putze et al in their paper [16], where EEG, GSR, and breathing rate were combined to estimate mental fatigue among drivers in a driving simulator performing a secondary cognitive task. Frontal theta power was also proved to be positively related to working memory engagement and attentional control in a study of different difficulty tasks combined with practice [17,18].…”

“…This measure allowed to decrease the number of features down to 570, which was still high. In order to further decrease the feature number, the next step was taken: The power of the frequency components were averaged within the following intervals: Delta(1-3 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta1 (13)(14)(15)(16)(17)(18)(19)(20), and beta2 (20-30 Hz) for 19 electrodes, which led to the total feature number equaling 95.…”