Brain mechanisms of social signalling in live social interactions with autistic and neurotypical adults.

The simple act of watching another person can change a person's behaviour in subtle but important ways; the individual being watched is now capable of signalling to the watcher, and may use this opportunity to communicate to the watcher. Recent data shows that people will spontaneously imitate more when being watched. Here, we examine the neural and cognitive mechanisms of being watched during spontaneous social imitation in autistic and neurotypical adults using fNIRS brain imaging. Participants (n = 44) took part in a block-moving task where they were instructed only to copy the block sequence which people normally do using a straight low action trajectory. Here, the demonstrator sometimes used an atypical 'high' action trajectory, giving participants the opportunity to spontaneously copy the high trajectory even if this slowed their performance. The confederate who demonstrated each block sequence could watch the participant's actions or close her eyes, giving a factorial design with factors of trajectory (high/low) and watched (watched/unwatched). Throughout the task, brain signals were captured from bilateral temporal/parietal/occipital cortex using fNIRS. We found that all participants performed higher actions when being watched by the confederate than when not being watched, with no differences between autistic and neurotypical participants. The unwatched conditions were associated with higher activity of the right inferior parietal lobule in all participants and also engagement of left STS only in autistic participants. These findings are consistent with the claim that people engage different neural mechanisms when watched and unwatched and that participants with autism may engage additional brain mechanisms to match neurotypical behaviour and compensate for social difficulties. However, further studies will be needed to replicate these results in a larger sample of participants.

Using multi-modal neuroimaging to characterise social brain specialisation in infants.

The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate, and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4- to 7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing, coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high-frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high-frequency brain activity. The development of simultaneous multimodal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain.

Developing customized NIRS-EEG for infant sleep research: methodological considerations.

Significance: Studies using simultaneous functional near-infrared spectroscopy (fNIRS)-electroencephalography (EEG) during natural sleep in infancy are rare. Developments for combined fNIRS-EEG for sleep research that ensure optimal comfort as well as good coupling and data quality are needed. Aim: We describe the steps toward developing a comfortable, wearable NIRS-EEG headgear adapted specifically for sleeping infants ages 5 to 9 months and present the experimental procedures and data quality to conduct infant sleep research using combined fNIRS-EEG. Approach: N=49 5- to 9-month-old infants participated. In phase 1, N=26 (10 = slept) participated using the non-wearable version of the NIRS-EEG headgear with 13-channel-wearable EEG and 39-channel fiber-based NIRS. In phase 2, N=23 infants (21 = slept) participated with the wireless version of the headgear with 20-channel-wearable EEG and 47-channel wearable NIRS. We used QT-NIRS to assess the NIRS data quality based on the good time window percentage, included channels, nap duration, and valid EEG percentage. Results: The infant nap rate during phase 1 was ∼40% (45% valid EEG data) and increased to 90% during phase 2 (100% valid EEG data). Infants slept significantly longer with the wearable system than the non-wearable system. However, there were more included good channels based on QT-NIRS in study phase 1 (61%) than phase 2 (50%), though this difference was not statistically significant. Conclusions: We demonstrated the usability of an integrated NIRS-EEG headgear during natural infant sleep with both non-wearable and wearable NIRS systems. The wearable NIRS-EEG headgear represents a good compromise between data quality, opportunities of applications (home visits and toddlers), and experiment success (infants' comfort, longer sleep duration, and opportunities for caregiver-child interaction).

A calculus of probability or belief? Neural underpinnings of social decision-making in a card game.

For decades, the prefrontal cortex (PFC) has been the focus of social neuroscience research, specifically regarding its role in competitive social decision-making. However, the distinct contributions of PFC subregions when making strategic decisions involving multiple types of information (social, non-social, and mixed information) remain unclear. This study investigates decision-making strategies (pure probability calculation vs. mentalizing) and their neural representations using functional near-infrared spectroscopy (fNIRS) data collected during a two-person card game. We observed individual differences in information processing strategy, indicating that some participants relied more on probability than others. Overall, the use of pure probability decreased over time in favor of other types of information (e.g., mixed information), with this effect being more pronounced within-round trials than across rounds. In the brain, (1) the lateral PFC activates when decisions are driven by probability calculations; (2) the right lateral PFC responds to trial difficulty; and (3) the anterior medial PFC is engaged when decision-making involves mentalizing. Furthermore, neural synchrony, which reflects the real-time interplay between individuals' cognitive processes, did not consistently contribute to correct decisions and fluctuated throughout the experiment, suggesting a hierarchical mentalizing mechanism at work.

Wearable, high-density fNIRS and diffuse optical tomography technologies: a perspective.

Recent progress in optoelectronics has made wearable and high-density functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) technologies possible for the first time. These technologies have the potential to open new fields of real-world neuroscience by enabling functional neuroimaging of the human cortex at a resolution comparable to fMRI in almost any environment and population. In this perspective article, we provide a brief overview of the history and the current status of wearable high-density fNIRS and DOT approaches, discuss the greatest ongoing challenges, and provide our thoughts on the future of this remarkable technology.

Combining wearable fNIRS and immersive virtual reality to study preschoolers' social development: a proof-of-principle study on preschoolers' social preference.

A child's social world is complex and rich, but has traditionally been assessed with conventional experiments where children are presented with repeated stimuli on a screen. These assessments are impoverished relative to the dynamics of social interactions in real life, and can be challenging to implement with preschoolers, who struggle to comply with strict lab rules. The current work meets the need to develop new platforms to assess preschoolers' social development, by presenting a unique virtual-reality set-up combined with wearable functional near-infrared spectroscopy (fNIRS). As a proof-of-principle, we validated this platform by measuring brain activity during self-guided social interaction in 3-to-5-year-olds, which is under-investigated, yet crucial to understand the basis of social interactions in preschoolers. 37 preschoolers chose an interaction partner from one of 4 human-like avatars of different gender and age. We recorded spontaneous brain fluctuations from the frontal and temporoparietal regions (notably engaged in social-categorization and preference) while children played a bubble-popping game with a preferred and an assigned avatar. 60% of the participants chose to play with the same-gender and same-age avatar. However, this result was driven by females (>80% vs. 50% in males). Different fronto-temporoparietal connectivity patterns when playing with the two avatars were observed, especially in females. We showed the feasibility of using a novel set-up to naturalistically assess social preference in preschoolers, which was assessed at the behavioural and functional connectivity level. This work provides a first proof-of-principle for using cutting-edge technologies and naturalistic experiments to study social development, opening new avenues of research.

Exploring Theater Neuroscience: Using Wearable Functional Near-infrared Spectroscopy to Measure the Sense of Self and Interpersonal Coordination in Professional Actors.

Ecologically valid research and wearable brain imaging are increasingly important in cognitive neuroscience as they enable researchers to measure neural mechanisms of complex social behaviors in real-world environments. This article presents a proof of principle study that aims to push the limits of what wearable brain imaging can capture and find new ways to explore the neuroscience of acting. Specifically, we focus on how to build an interdisciplinary paradigm to investigate the effects of taking on a role on an actor's sense of self and present methods to quantify interpersonal coordination at different levels (brain, physiology, behavior) as pairs of actors rehearse an extract of a play prepared for live performance. Participants were six actors from Flute Theatre, rehearsing an extract from Shakespeare's A Midsummer Night's Dream. Sense of self was measured in terms of the response of the pFC to hearing one's own name (compared with another person's name). Interpersonal coordination was measured using wavelet coherence analysis of brain signals, heartbeats, breathing, and behavior. Findings show that it is possible to capture an actor's pFC response to their own name and that this response is suppressed when an actor rehearses a segment of the play. In addition, we found that it is possible to measure interpersonal synchrony across three modalities simultaneously. These methods open the way to new studies that can use wearable neuroimaging and hyperscanning to understand the neuroscience of social interaction and the complex social-emotional processes involved in theatrical training and performing theater.

Prefrontal cortical activation associated with prospective memory while walking around a real-world street environment.

Rostral PFC (area 10) activation is common during prospective memory (PM) tasks. But it is not clear what mental processes these activations index. Three candidate explanations from cognitive neuroscience theory are: (i) monitoring of the environment; (ii) spontaneous intention retrieval; (iii) a combination of the two. These explanations make different predictions about the temporal and spatial patterns of activation that would be seen in rostral PFC in naturalistic settings. Accordingly, we plotted functional events in PFC using portable fNIRS while people were carrying out a PM task outside the lab and responding to cues when they were encountered, to decide between these explanations. Nineteen people were asked to walk around a street in London, U.K. and perform various tasks while also remembering to respond to prospective memory (PM) cues when they detected them. The prospective memory cues could be either social (involving greeting a person) or non-social (interacting with a parking meter) in nature. There were also a number of contrast conditions which allowed us to determine activation specifically related to the prospective memory components of the tasks. We found that maintaining both social and non-social intentions was associated with widespread activation within medial and right hemisphere rostral prefrontal cortex (BA 10), in agreement with numerous previous lab-based fMRI studies of prospective memory. In addition, increased activation was found within lateral prefrontal cortex (BA 45 and 46) when people were maintaining a social intention compared to a non-social one. The data were then subjected to a GLM-based method for automatic identification of functional events (AIDE), and the position of the participants at the time of the activation events were located on a map of the physical space. The results showed that the spatial and temporal distribution of these events was not random, but aggregated around areas in which the participants appeared to retrieve their future intentions (i.e., where they saw intentional cues), as well as where they executed them. Functional events were detected most frequently in BA 10 during the PM conditions compared to other regions and tasks. Mobile fNIRS can be used to measure higher cognitive functions of the prefrontal cortex in "real world" situations outside the laboratory in freely ambulant individuals. The addition of a "brain-first" approach to the data permits the experimenter to determine not only when haemodynamic changes occur, but also where the participant was when it happened. This can be extremely valuable when trying to link brain and cognition.

Investigation of functional near-infrared spectroscopy signal quality and development of the hemodynamic phase correlation signal.

Significance: There is a longstanding recommendation within the field of fNIRS to use oxygenated ( HbO 2 ) and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus on HbO 2 only. Previous work has shown that HbO 2 on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using both HbO 2 and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination of HbO 2 and HHb has been recommended as a method to utilize both signals in analysis. Aim: We present the development of the hemodynamic phase correlation (HPC) signal to combine HbO 2 and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. Approach: About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal's mean T -value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [ HbO 2 , HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. Results: Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives on HbO 2 and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. Conclusions: We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of using HbO 2 and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives.

Inferior parietal lobule is sensitive to different semantic similarity relations for concrete and abstract words.

Similarity measures, the extent to which two concepts have similar meanings, are the key to understand how concepts are represented, with different theoretical perspectives relying on very different sources of data from which similarity can be calculated. While there is some commonality in similarity measures, the extent of their correlation is limited. Previous studies also suggested that the relative performance of different similarity measures may also vary depending on concept concreteness and that the inferior parietal lobule (IPL) may be involved in the integration of conceptual features in a multimodal system for the semantic categorization. Here, we tested for the first time whether theory-based similarity measures predict the pattern of brain activity in the IPL differently for abstract and concrete concepts. English speakers performed a semantic decision task, while we recorded their brain activity in IPL through fNIRS. Using representational similarity analysis, results indicated that the neural representational similarity in IPL conformed to the lexical co-occurrence among concrete concepts (regardless of the hemisphere) and to the affective similarity among abstract concepts in the left hemisphere only, implying that semantic representations of abstract and concrete concepts are characterized along different organizational principles in the IPL. We observed null results for the decoding accuracy. Our study suggests that the use of the representational similarity analysis as a complementary analysis to the decoding accuracy is a promising tool to reveal similarity patterns between theoretical models and brain activity recorded through fNIRS.

Regional Haemodynamic and Metabolic Coupling in Infants.

Metabolic pathways underlying brain function remain largely unexplored during neurodevelopment, predominantly due to the lack of feasible techniques for use with awake infants. Broadband near-infrared spectroscopy (bNIRS) provides the opportunity to explore the relationship between cerebral energy metabolism and blood oxygenation/haemodynamics through the measurement of changes in the oxidation state of mitochondrial respiratory chain enzyme cytochrome-c-oxidase (ΔoxCCO) alongside haemodynamic changes. We used a bNIRS system to measure ΔoxCCO and haemodynamics during functional activation in a group of 42 typically developing infants aged between 4 and 7 months. bNIRS measurements were made over the right hemisphere over temporal, parietal and central cortical regions, in response to social and non-social visual and auditory stimuli. Both ΔoxCCO and Δ[HbO2] displayed larger activation for the social condition in comparison to the non-social condition. Integration of haemodynamic and metabolic signals revealed networks of stimulus-selective cortical regions that were not apparent from analysis of the individual bNIRS signals. These results provide the first spatially resolved measures of cerebral metabolic activity alongside haemodynamics during functional activation in infants. Measuring synchronised changes in metabolism and haemodynamics have the potential for uncovering the development of cortical specialisation in early infancy.

The role of anterior prefrontal cortex (area 10) in face-to-face deception measured with fNIRS.

Anterior prefrontal cortex (PFC, Brodmann area 10) activations are often, but not always, found in neuroimaging studies investigating deception, and the precise role of this area remains unclear. To explore the role of the PFC in face-to-face deception, we invited pairs of participants to play a card game involving lying and lie detection while we used functional near infrared spectroscopy (fNIRS) to record brain activity in the PFC. Participants could win points for successfully lying about the value of their cards or for detecting lies. We contrasted patterns of brain activation when the participants either told the truth or lied, when they were either forced into this or did so voluntarily and when they either succeeded or failed to detect a lie. Activation in the anterior PFC was found in both lie production and detection, unrelated to reward. Analysis of cross-brain activation patterns between participants identified areas of the PFC where the lead player's brain activity synchronized their partner's later brain activity. These results suggest that during situations that involve close interpersonal interaction, the anterior PFC supports processing widely involved in deception, possibly relating to the demands of monitoring one's own and other people's behaviour.

The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience.

The past few decades have seen a rapid increase in the use of functional near-infrared spectroscopy (fNIRS) in cognitive neuroscience. This fast growth is due to the several advances that fNIRS offers over the other neuroimaging modalities such as functional magnetic resonance imaging and electroencephalography/magnetoencephalography. In particular, fNIRS is harmless, tolerant to bodily movements, and highly portable, being suitable for all possible participant populations, from newborns to the elderly and experimental settings, both inside and outside the laboratory. In this review we aim to provide a comprehensive and state-of-the-art review of fNIRS basics, technical developments, and applications. In particular, we discuss some of the open challenges and the potential of fNIRS for cognitive neuroscience research, with a particular focus on neuroimaging in naturalistic environments and social cognitive neuroscience.

A Review on the Use of Wearable Functional Near-Infrared Spectroscopy in Naturalistic Environments.

The development of novel miniaturized wireless and wearable functional Near-Infrared Spectroscopy (fNIRS) devices have paved the way to new functional brain imaging that can revolutionize the cognitive research fields. Over the past few decades, several studies have been conducted with conventional fNIRS systems that have demonstrated the suitability of this technology for a wide variety of populations and applications, to investigate both the healthy brain and the diseased brain. However, what makes wearable fNIRS even more appealing is its capability to allow measurements in everyday life scenarios that are not possible with other gold-standard neuroimaging modalities, such as functional Magnetic Resonance Imaging. This can have a huge impact on the way we explore the neural bases and mechanisms underpinning human brain functioning. The aim of this review is to provide an overview of studies conducted with wearable fNIRS in naturalistic settings in the field of cognitive neuroscience. In addition, we present the challenges associated with the use of wearable fNIRS in unrestrained contexts, discussing solutions that will allow accurate inference of functional brain activity. Finally, we provide an overview of the future perspectives in cognitive neuroscience that we believe would benefit the most by using wearable fNIRS.

Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework.

Functional near-infrared spectroscopy (fNIRS) research articles show a large heterogeneity in the analysis approaches and pre-processing procedures. Additionally, there is often a lack of a complete description of the methods applied, necessary for study replication or for results comparison. The aims of this paper were (i) to review and investigate which information is generally included in published fNIRS papers, and (ii) to define a signal pre-processing procedure to set a common ground for standardization guidelines. To this goal, we have reviewed 110 fNIRS articles published in 2016 in the field of cognitive neuroscience, and performed a simulation analysis with synthetic fNIRS data to optimize the signal filtering step before applying the GLM method for statistical inference. Our results highlight the fact that many papers lack important information, and there is a large variability in the filtering methods used. Our simulations demonstrated that the optimal approach to remove noise and recover the hemodynamic response from fNIRS data in a GLM framework is to use a 1000th order band-pass Finite Impulse Response filter. Based on these results, we give preliminary recommendations as to the first step toward improving the analysis of fNIRS data and dissemination of the results.

The role of expectation in multisensory body representation - neural evidence.

Sensory events contribute to body ownership, the feeling that the body belongs to me. However, the encoding of sensory events is not only reactive, but also proactive in that our brain generates prediction about forthcoming stimuli. In previous studies, we have shown that prediction of sensory events is a sufficient condition to induce the sense of body ownership. In this study, we investigated the underlying neural mechanisms. Participants were seated with their right arm resting upon a table just below another smaller table. Hence, the real hand was hidden from the participant's view and a life-sized rubber model of a right hand was placed on the small table in front of them. Participants observed a wooden plank while approaching - without touching - the rubber hand. We measured the phenomenology of the illusion by means of questionnaire. Neural activity was recorded by means of near-infrared spectroscopy (fNIRS). Results showed higher activation of multisensory parietal cortices in the rubber hand illusion induced by touch expectation. Furthermore, such activity was correlated with the subjective feeling of owning the rubber hand. Our results enrich current models of body ownership suggesting that our multisensory brain regions generate prediction on what could be my body and what could not. This finding might have interesting implications in all those cases in which body representation is altered, anorexia, bulimia nervosa and obesity, among others.

A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments.

Recent technological advances have allowed the development of portable functional Near-Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real-world. However, as real-world experiments are designed to mimic everyday life situations, the identification of event onsets can be extremely challenging and time-consuming. Here, we present a novel analysis method based on the general linear model (GLM) least square fit analysis for the Automatic IDentification of functional Events (or AIDE) directly from real-world fNIRS neuroimaging data. In order to investigate the accuracy and feasibility of this method, as a proof-of-principle we applied the algorithm to (i) synthetic fNIRS data simulating both block-, event-related and mixed-design experiments and (ii) experimental fNIRS data recorded during a conventional lab-based task (involving maths). AIDE was able to recover functional events from simulated fNIRS data with an accuracy of 89%, 97% and 91% for the simulated block-, event-related and mixed-design experiments respectively. For the lab-based experiment, AIDE recovered more than the 66.7% of the functional events from the fNIRS experimental measured data. To illustrate the strength of this method, we then applied AIDE to fNIRS data recorded by a wearable system on one participant during a complex real-world prospective memory experiment conducted outside the lab. As part of the experiment, there were four and six events (actions where participants had to interact with a target) for the two different conditions respectively (condition 1: social-interact with a person; condition 2: non-social-interact with an object). AIDE managed to recover 3/4 events and 3/6 events for conditions 1 and 2 respectively. The identified functional events were then corresponded to behavioural data from the video recordings of the movements and actions of the participant. Our results suggest that "brain-first" rather than "behaviour-first" analysis is possible and that the present method can provide a novel solution to analyse real-world fNIRS data, filling the gap between real-life testing and functional neuroimaging.

Simultaneous fNIRS and thermal infrared imaging during cognitive task reveal autonomic correlates of prefrontal cortex activity.

Functional Near Infrared-Spectroscopy (fNIRS) represents a powerful tool to non-invasively study task-evoked brain activity. fNIRS assessment of cortical activity may suffer for contamination by physiological noises of different origin (e.g. heart beat, respiration, blood pressure, skin blood flow), both task-evoked and spontaneous. Spontaneous changes occur at different time scales and, even if they are not directly elicited by tasks, their amplitude may result task-modulated. In this study, concentration changes of hemoglobin were recorded over the prefrontal cortex while simultaneously recording the facial temperature variations of the participants through functional infrared thermal (fIR) imaging. fIR imaging provides touch-less estimation of the thermal expression of peripheral autonomic. Wavelet analysis revealed task-modulation of the very low frequency (VLF) components of both fNIRS and fIR signals and strong coherence between them. Our results indicate that subjective cognitive and autonomic activities are intimately linked and that the VLF component of the fNIRS signal is affected by the autonomic activity elicited by the cognitive task. Moreover, we showed that task-modulated changes in vascular tone occur both at a superficial and at larger depth in the brain. Combined use of fNIRS and fIR imaging can effectively quantify the impact of VLF autonomic activity on the fNIRS signals.

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks.

Functional Near Infrared Spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared light to monitor brain activity. Based on neurovascular coupling, fNIRS is able to measure the haemoglobin concentration changes secondary to neuronal activity. Compared to other neuroimaging techniques, fNIRS represents a good compromise in terms of spatial and temporal resolution. Moreover, it is portable, lightweight, less sensitive to motion artifacts and does not impose significant physical restraints. It is therefore appropriate to monitor a wide range of cognitive tasks (e.g., auditory, gait analysis, social interaction) and different age populations (e.g., new-borns, adults, elderly people). The recent development of fiberless fNIRS devices has opened the way to new applications in neuroscience research. This represents a unique opportunity to study functional activity during real-world tests, which can be more sensitive and accurate in assessing cognitive function and dysfunction than lab-based tests. This study explored the use of fiberless fNIRS to monitor brain activity during a real-world prospective memory task. This protocol is performed outside the lab and brain haemoglobin concentration changes are continuously measured over the prefrontal cortex while the subject walks around in order to accomplish several different tasks.

Thermal Infrared Imaging-Based Computational Psychophysiology for Psychometrics.

Thermal infrared imaging has been proposed as a potential system for the computational assessment of human autonomic nervous activity and psychophysiological states in a contactless and noninvasive way. Through bioheat modeling of facial thermal imagery, several vital signs can be extracted, including localized blood perfusion, cardiac pulse, breath rate, and sudomotor response, since all these parameters impact the cutaneous temperature. The obtained physiological information could then be used to draw inferences about a variety of psychophysiological or affective states, as proved by the increasing number of psychophysiological studies using thermal infrared imaging. This paper presents therefore a review of the principal achievements of thermal infrared imaging in computational physiology with regard to its capability of monitoring psychophysiological activity.