UNASSIGNED: Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging technique used to assess surgeons\' brain function. The aim of this narrative review is to outline the effect of expertise, stress, surgical technology, and neurostimulation on surgeons\' neural activation patterns, and highlight key progress areas required in surgical neuroergonomics to modulate training and performance.
UNASSIGNED: A literature search of PubMed and Embase was conducted to identify neuroimaging studies using fNIRS and neurostimulation in surgeons performing simulated tasks.
UNASSIGNED: Novice surgeons exhibit greater haemodynamic responses across the pre-frontal cortex than experts during simple surgical tasks, whilst expert surgical performance is characterized by relative prefrontal attenuation and upregulation of activation foci across other regions such as the supplementary motor area. The association between PFC activation and mental workload follows an inverted-U shaped curve, activation increasing then attenuating past a critical inflection point at which demands outstrip cognitive capacity Neuroimages are sensitive to the impact of laparoscopic and robotic tools on cognitive workload, helping inform the development of training programs which target neural learning curves. FNIRS differs in comparison to current tools to assess proficiency by depicting a cognitive state during surgery, enabling the development of cognitive benchmarks of expertise. Finally, neurostimulation using transcranial direct-current-stimulation may accelerate skill acquisition and enhance technical performance.
UNASSIGNED: FNIRS can inform the development of surgical training programs which modulate stress responses, cognitive learning curves, and motor skill performance. Improved data processing with machine learning offers the possibility of live feedback regarding surgeons\' cognitive states during operative procedures.

Stroke is characterized by high morbidity and disability, and proposing effective methods for assessing and designing rehabilitation products is an attractive topic in current research. In this study, a hand function rehabilitation aid was developed for stroke patients. Ten stroke patients and 20 healthy older people as a control group were recruited to perform a 600 s task after a 600 s resting by gripping a stick while clicking on a flashing light in an electronic insert in sequence according to a pattern. The functional near-infrared spectroscopy (fNIRS) and behavioral data were collected during their rehabilitation training. Brain function was analyzed using three indicators, namely brain area activation, functional connectivity and effective connectivity, while behavioral performance was analyzed using ten indicators, such as velocity and acceleration, and correlations were made between both. Followed by proposing a quantitative assessment method based on the fusion of multiple data sources. The results showed that the developed rehabilitation tool could effectively stimulate the patient\'s brain and help recover their cognitive and behavioral capacities. The scientific validity of the proposed assessment approach was further confirmed by contrasting the data results of the stroke group with those of the healthy elderly group. This study has integrated brain function and behavioral data, providing a practical quantitative evaluation method of product ergonomics and data-driven product design concepts for stroke patients.

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.