Measurement of Brain Function of Car Driver Using Functional Near-Infrared Spectroscopy (fNIRS)

The aim of this study is to propose a method for analyzing measured signal obtained from functional Near-Infrared Spectroscopy (fNIRS), which is applicable for neuroimaging studies for car drivers. We developed a signal processing method by multiresolution analysis (MRA) based on discrete wavelet transform. Statistical group analysis using Z-score is conducted after the extraction of task-related signal using MRA. Brain activities of subjects with different level of mental calculation are measured by fNIRS and fMRI. Results of mental calculation with nine subjects by using fNIRS and fMRI showed that the proposed methods were effective for the evaluation of brain activities due to the task. Finally, the proposed method is applied for evaluating brain function of car driver with and without adaptive cruise control (ACC) system for demonstrating the effectiveness of the proposed method. The results showed that frontal lobe was less active when the subject drove with ACC

Prefrontal cortex activation upon a demanding virtual hand-controlled task: A new frontier for neuroergonomics

open9noFunctional near-infrared spectroscopy (fNIRS) is a non-invasive vascular-based functional neuroimaging technology that can assess, simultaneously from multiple cortical areas, concentration changes in oxygenated-deoxygenated hemoglobin at the level of the cortical microcirculation blood vessels. fNIRS, with its high degree of ecological validity and its very limited requirement of physical constraints to subjects, could represent a valid tool for monitoring cortical responses in the research field of neuroergonomics. In virtual reality (VR) real situations can be replicated with greater control than those obtainable in the real world. Therefore, VR is the ideal setting where studies about neuroergonomics applications can be performed. The aim of the present study was to investigate, by a 20-channel fNIRS system, the dorsolateral/ventrolateral prefrontal cortex (DLPFC/VLPFC) in subjects while performing a demanding VR hand-controlled task (HCT). Considering the complexity of the HCT, its execution should require the attentional resources allocation and the integration of different executive functions. The HCT simulates the interaction with a real, remotely-driven, system operating in a critical environment. The hand movements were captured by a high spatial and temporal resolution 3-dimensional (3D) hand-sensing device, the LEAP motion controller, a gesture-based control interface that could be used in VR for tele-operated applications. Fifteen University students were asked to guide, with their right hand/forearm, a virtual ball (VB) over a virtual route (VROU) reproducing a 42 m narrow road including some critical points. The subjects tried to travel as long as possible without making VB fall. The distance traveled by the guided VB was 70.2 ± 37.2 m. The less skilled subjects failed several times in guiding the VB over the VROU. Nevertheless, a bilateral VLPFC activation, in response to the HCT execution, was observed in all the subjects. No correlation was found between the distance traveled by the guided VB and the corresponding cortical activation. These results confirm the suitability of fNIRS technology to objectively evaluate cortical hemodynamic changes occurring in VR environments. Future studies could give a contribution to a better understanding of the cognitive mechanisms underlying human performance either in expert or non-expert operators during the simulation of different demanding/fatiguing activities.openCarrieri, Marika; Petracca, Andrea; Lancia, Stefania; Basso Moro, Sara; Brigadoi, Sabrina; Spezialetti, Matteo; Ferrari, Marco; Placidi, Giuseppe; Quaresima, ValentinaCarrieri, Marika; Petracca, Andrea; Lancia, Stefania; BASSO MORO, Sara; Brigadoi, Sabrina; Spezialetti, Matteo; Ferrari, Marco; Placidi, Giuseppe; Quaresima, Valentin

Relationship Between Brain Activity and Real-Road Driving Behavior: A Vector-Based Whole-Brain Functional Near-Infrared Spectroscopy Study

Automobile driving requires multiple brain functions. However, the brain regions related to driving behavior are unknown. Therefore, we measured activity of the frontal, parietal and occipital lobes during driving using functional near-infrared spectroscopy (fNIRS). Cortical activation patterns were examined in relation to driving behaviors, such as steering motion, accelerator pedal motion, and speed control. Six healthy adults participated in the experiment. Cerebral oxygen exchange (COE) was calculated based on the oxyhemoglobin and deoxyhemoglobin concentrations measured by fNIRS. The COE and driving behavior data were collected every 1 m and averaged for all subjects. Functional NIRS data for all 98 channels were extracted using principal component analysis. Similarity between extracted components and driving behaviors were confirmed by |cosine similarity|\u3e0.3. Among the factors with confirmed similarity, we identified brain regions with high principal component loading (|PCL|\u3e0.4). Among the 16 COE factors extracted, COE factor 1 and factor 5 exhibited similarity with steering motion (cosine similarity: factor 1, -0.538; factor 5, 0.551). The PCLs of COE factor 1 and factor 5 were high in the frontal lobe (Brodmann areas [BAs] 9, 8, and 4/3) (PCL\u3e0.8). COE factor 6 exhibited a similarity with accelerator pedal motion (cosine similarity: 0.369), and the PCL of COE factor 6 was highest in the parietal lobe (BA7) (PCL= -0.62). Speed control did not exhibit similarity with any COE factor. These findings will contribute to the selection of brain measurement areas when fNIRS is used for vehicle driving assessment

Prefrontal cortex activation and young driver behaviour: a fNIRS study

Road traffic accidents consistently show a significant over-representation for young, novice and particularly male drivers. This research examines the prefrontal cortex activation of young drivers and the changes in activation associated with manipulations of mental workload and inhibitory control. It also considers the explanation that a lack of prefrontal cortex maturation is a contributing factor to the higher accident risk in this young driver population. The prefrontal cortex is associated with a number of factors including mental workload and inhibitory control, both of which are also related to road traffic accidents. This experiment used functional near infrared spectroscopy to measure prefrontal cortex activity during five simulated driving tasks: one following task and four overtaking tasks at varying traffic densities which aimed to dissociate workload and inhibitory control. Age, experience and gender were controlled for throughout the experiment. The results showed that younger drivers had reduced prefrontal cortex activity compared to older drivers. When both mental workload and inhibitory control increased prefrontal cortex activity also increased, however when inhibitory control alone increased there were no changes in activity. Along with an increase in activity during overtaking manoeuvres, these results suggest that prefrontal cortex activation is more indicative of workload in the current task. There were no differences in the number of overtakes completed by younger and older drivers but males overtook significantly more than females. We conclude that prefrontal cortex activity is associated with the mental workload required for overtaking. We additionally suggest that the reduced activation in younger drivers may be related to a lack of prefrontal maturation which could contribute to the increased crash risk seen in this population

Measurement and Evaluation of Brain Activity for Train Drivers Using Wearable NIRS

Human errors of train drivers may cause serious damage. Therefore, research on human error prevention has been conducted by many researchers. In this context, brain activity measurement of train drivers using near-infrared spectroscopy (NIRS) has been conducted to monitor the condition of train drivers. In this study, we developed a compact wireless wearable NIRS that can be used in natural environments. The wearable NIRS has been used to measure train drivers’ brain function using a train driving simulator. Experimental results showed that brain activity of the dorsolateral prefrontal cortex (DLPFC) increased when the driver made braking operation. The experiment for train driving with an accidental event was carried out to evaluate the relation between drivers’ attention and the brain activity. As a result, there was a difference in brain activity between with and without prior notice. Results showed that the increased attention of the train driver can be shown in the NIRS signal from the outer part of the prefrontal cortex

Exploring the Brain Responses to Driving Fatigue through Simultaneous EEG and fNIRS Measurements

© 2020 World Scientific Publishing Company. Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO2) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO2 decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO2 oscillations suggest that activations in HbO2 are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving

Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy:A systematic review

Cognitive load theory suggests that overloading of working memory may negatively affect the performance of human in cognitively demanding tasks. Evaluation of cognitive load is a difficult task; it is often assessed through feedback and evaluation from experts. Cognitive load classification based on Functional Near-InfraRed Spectroscopy (fNIRS) is now one of the key research areas in recent years, due to its resistance of artefacts, cost-effectiveness, and portability. To make fNIRS more practical in various applications, it is necessary to develop robust algorithms that can automatically classify fNIRS signals and less reliant on trained signals. Many of the analytical tools used in cognitive sciences have used Deep Learning (DL) modalities to uncover relevant information for mental workload classification. This review investigates the research questions on the design and overall effectiveness of DL as well as its key characteristics. We have identified 45 studies published between 2011 and 2023, that specifically proposed Machine Learning (ML) models for classifying cognitive load using data obtained from fNIRS devices. Those studies were analyzed based on type of feature selection methods, input, and DL model architectures. Most of the existing cognitive load studies are based on ML algorithms, which follow signal filtration and hand-crafted features. It is observed that hybrid DL architectures that integrate convolution and LSTM operators performed significantly better in comparison with other models. However, DL models especially hybrid models have not been extensively investigated for the classification of cognitive load captured by fNIRS devices. The current trends and challenges are highlighted to provide directions for the development of DL models pertaining to fNIRS research