Mental workload assessment by monitoring brain, heart, and eye with six biomedical modalities during six cognitive tasks.

Introduction: The efficiency and safety of complex high precision human-machine systems such as in aerospace and robotic surgery are closely related to the cognitive readiness, ability to manage workload, and situational awareness of their operators. Accurate assessment of mental workload could help in preventing operator error and allow for pertinent intervention by predicting performance declines that can arise from either work overload or under stimulation. Neuroergonomic approaches based on measures of human body and brain activity collectively can provide sensitive and reliable assessment of human mental workload in complex training and work environments. Methods: In this study, we developed a new six-cognitive-domain task protocol, coupling it with six biomedical monitoring modalities to concurrently capture performance and cognitive workload correlates across a longitudinal multi-day investigation. Utilizing two distinct modalities for each aspect of cardiac activity (ECG and PPG), ocular activity (EOG and eye-tracking), and brain activity (EEG and fNIRS), 23 participants engaged in four sessions over 4 weeks, performing tasks associated with working memory, vigilance, risk assessment, shifting attention, situation awareness, and inhibitory control. Results: The results revealed varying levels of sensitivity to workload within each modality. While certain measures exhibited consistency across tasks, neuroimaging modalities, in particular, unveiled meaningful differences between task conditions and cognitive domains. Discussion: This is the first comprehensive comparison of these six brain-body measures across multiple days and cognitive domains. The findings underscore the potential of wearable brain and body sensing methods for evaluating mental workload. Such comprehensive neuroergonomic assessment can inform development of next generation neuroadaptive interfaces and training approaches for more efficient human-machine interaction and operator skill acquisition.

Neuroadaptive Training via fNIRS in Flight Simulators.

Training to master a new skill often takes a lot of time, effort, and financial resources, particularly when the desired skill is complex, time sensitive, or high pressure where lives may be at risk. Professions such as aircraft pilots, surgeons, and other mission-critical operators that fall under this umbrella require extensive domain-specific dedicated training to enable learners to meet real-world demands. In this study, we describe a novel neuroadaptive training protocol to enhance learning speed and efficiency using a neuroimaging-based cognitive workload measurement system in a flight simulator. We used functional near-infrared spectroscopy (fNIRS), which is a wearable, mobile, non-invasive neuroimaging modality that can capture localized hemodynamic response and has been used extensively to monitor the anterior prefrontal cortex to estimate cognitive workload. The training protocol included four sessions over 2 weeks and utilized realistic piloting tasks with up to nine levels of difficulty. Learners started at the lowest level and their progress adapted based on either behavioral performance and fNIRS measures combined (neuroadaptive) or performance measures alone (control). Participants in the neuroadaptive group were found to have significantly more efficient training, reaching higher levels of difficulty or significantly improved performance depending on the task, and showing consistent patterns of hemodynamic-derived workload in the dorsolateral prefrontal cortex. The results of this study suggest that a neuroadaptive personalized training protocol using non-invasive neuroimaging is able to enhance learning of new tasks. Finally, we outline here potential avenues for further optimization of this fNIRS based neuroadaptive training approach. As fNIRS mobile neuroimaging is becoming more practical and accessible, the approaches developed here can be applied in the real world in scale.

Mapping articulatory and grammatical subcomponents of fluency deficits in post-stroke aphasia.

Fluent speech production is a critical aspect of language processing and is central to aphasia diagnosis and treatment. Multiple cognitive processes and neural subsystems must be coordinated to produce fluent narrative speech. To refine the understanding of these systems, measures that minimize the influence of other cognitive processes were defined for articulatory deficits and grammatical deficits. Articulatory deficits were measured by the proportion of phonetic errors (articulatory and prosodic) in a word repetition task in 115 participants with aphasia following left hemisphere stroke. Grammatical deficits were assessed in 46 participants based on two measures-proportion of closed class words and proportion of words in sentences-generated during semistructured narrative speech production (telling the Cinderella story). These measures were used to identify brain regions critical for articulatory and grammatical aspects of speech production using a multivariate lesion-symptom mapping approach based on support vector regression. Phonetic error proportion was associated with damage to the postcentral gyrus and the inferior parietal lobule (particularly the supramarginal gyrus). Proportion of closed class words in narrative speech did not have consistent lesion correlates. Proportion of words in sentences was strongly associated with frontal lobe damage, particularly the inferior and middle frontal gyri. Grammatical sentence structuring relies on frontal regions, particularly the inferior and middle frontal gyri, whereas phonetic-articulatory planning and execution relies on parietal regions, particularly the postcentral and supramarginal gyri. These results clarify and extend current understanding of the functional components of the frontoparietal speech production system.