Authors:Johann Benerradi, Jeremie Clos, Aleksandra Landowska, Michel F. Valstar, Max L. Wilson Abstract: BackgroundWhile efforts to establish best practices with functional near infrared spectroscopy (fNIRS) signal processing have been published, there are still no community standards for applying machine learning to fNIRS data. Moreover, the lack of open source benchmarks and standard expectations for reporting means that published works often claim high generalisation capabilities, but with poor practices or missing details in the paper. These issues make it hard to evaluate the performance of models when it comes to choosing them for brain-computer interfaces.MethodsWe present an open-source benchmarking framework, BenchNIRS, to establish a best practice machine learning methodology to evaluate models applied to fNIRS data, using five open access datasets for brain-computer interface (BCI) applications. The BenchNIRS framework, using a robust methodology with nested cross-validation, enables researchers to optimise models and evaluate them without bias. The framework also enables us to produce useful metrics and figures to detail the performance of new models for comparison. To demonstrate the utility of the framework, we present a benchmarking of six baseline models [linear discriminant analysis (LDA), support-vector machine (SVM), k-nearest neighbours (kNN), artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM)] on the five datasets and investigate the influence of different factors on the classification performance, including: number of training examples and size of the time window of each fNIRS sample used for classification. We also present results with a sliding window as opposed to simple classification of epochs, and with a personalised approach (within subject data classification) as opposed to a generalised approach (unseen subject data classification).Results and discussionResults show that the performance is typically lower than the scores often reported in literature, and without great differences between models, highlighting that predicting unseen data remains a difficult task. Our benchmarking framework provides future authors, who are achieving significant high classification scores, with a tool to demonstrate the advances in a comparable way. To complement our framework, we contribute a set of recommendations for methodology decisions and writing papers, when applying machine learning to fNIRS data. PubDate: 2023-03-03T00:00:00Z
Authors:Moussa Diarra, Mauro Marchitto, Marie-Christine Bressolle, Thierry Baccino, Véronique Drai-Zerbib Abstract: Aviation remains one of the safest modes of transportation. However, an inappropriate response to an unexpected event can lead to flight incidents and accidents. Among several contributory factors, startle and surprise, which can lead to or exacerbate the pilot's state of stress, are often cited. Unlike stress, which has been the subject of much study in the context of driving and piloting, studies on startle and surprise are less numerous and these concepts are sometimes used interchangeably. Thus, the definitions of stress, startle, and surprise are reviewed, and related differences are put in evidence. Furthermore, it is proposed to distinguish these notions in the evaluation and to add physiological measures to subjective measures in their study. Indeed, Landman's theoretical model makes it possible to show the links between these concepts and studies using physiological parameters show that they would make it possible to disentangle the links between stress, startle and surprise in the context of aviation. Finally, we draw some perspectives to set up further studies focusing specifically on these concepts and their measurement. PubDate: 2023-02-23T00:00:00Z
Authors:Marc Rosenkranz, Timur Cetin, Verena N. Uslar, Martin G. Bleichner Abstract: IntroductionIn demanding work situations (e.g., during a surgery), the processing of complex soundscapes varies over time and can be a burden for medical personnel. Here we study, using mobile electroencephalography (EEG), how humans process workplace-related soundscapes while performing a complex audio-visual-motor task (3D Tetris). Specifically, we wanted to know how the attentional focus changes the processing of the soundscape as a whole.MethodParticipants played a game of 3D Tetris in which they had to use both hands to control falling blocks. At the same time, participants listened to a complex soundscape, similar to what is found in an operating room (i.e., the sound of machinery, people talking in the background, alarm sounds, and instructions). In this within-subject design, participants had to react to instructions (e.g., “place the next block in the upper left corner”) and to sounds depending on the experimental condition, either to a specific alarm sound originating from a fixed location or to a beep sound that originated from varying locations. Attention to the alarm reflected a narrow attentional focus, as it was easy to detect and most of the soundscape could be ignored. Attention to the beep reflected a wide attentional focus, as it required the participants to monitor multiple different sound streams.Results and discussionResults show the robustness of the N1 and P3 event related potential response during this dynamic task with a complex auditory soundscape. Furthermore, we used temporal response functions to study auditory processing to the whole soundscape. This work is a step toward studying workplace-related sound processing in the operating room using mobile EEG. PubDate: 2023-02-02T00:00:00Z
Authors:Elise Grevet, Killyam Forge, Sebastien Tadiello, Margaux Izac, Franck Amadieu, Lionel Brunel, Léa Pillette, Jacques Py, David Gasq, Camille Jeunet-Kelway Abstract: IntroductionStrokes leave around 40% of survivors dependent in their activities of daily living, notably due to severe motor disabilities. Brain-computer interfaces (BCIs) have been shown to be efficiency for improving motor recovery after stroke, but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified (e.g., sensors and signal processing), fully optimized BCIs are pointless if patients and clinicians cannot or do not want to use them. We hypothesize that improving BCI acceptability will reduce patients' anxiety levels, while increasing their motivation and engagement in the procedure, thereby favoring learning, ultimately, and motor recovery. In other terms, acceptability could be used as a lever to improve BCI efficiency. Yet, studies on BCI based on acceptability/acceptance literature are missing. Thus, our goal was to model BCI acceptability in the context of motor rehabilitation after stroke, and to identify its determinants.MethodsThe main outcomes of this paper are the following: i) we designed the first model of acceptability of BCIs for motor rehabilitation after stroke, ii) we created a questionnaire to assess acceptability based on that model and distributed it on a sample representative of the general public in France (N = 753, this high response rate strengthens the reliability of our results), iii) we validated the structure of this model and iv) quantified the impact of the different factors on this population.ResultsResults show that BCIs are associated with high levels of acceptability in the context of motor rehabilitation after stroke and that the intention to use them in that context is mainly driven by the perceived usefulness of the system. In addition, providing people with clear information regarding BCI functioning and scientific relevance had a positive influence on acceptability factors and behavioral intention.DiscussionWith this paper we propose a basis (model) and a methodology that could be adapted in the future in order to study and compare the results obtained with: i) different stakeholders, i.e., patients and caregivers; ii) different populations of different cultures around the world; and iii) different targets, i.e., other clinical and non-clinical BCI applications. PubDate: 2023-02-01T00:00:00Z
Authors:M. L. Cummings Abstract: The recent shift from predominantly hardware-based systems in complex settings to systems that heavily leverage non-deterministic artificial intelligence (AI) reasoning means that typical systems engineering processes must also adapt, especially when humans are direct or indirect users. Systems with embedded AI rely on probabilistic reasoning, which can fail in unexpected ways, and any overestimation of AI capabilities can result in systems with latent functionality gaps. This is especially true when humans oversee such systems, and such oversight has the potential to be deadly, but there is little-to-no consensus on how such system should be tested to ensure they can gracefully fail. To this end, this work outlines a roadmap for emerging research areas for complex human-centric systems with embedded AI. Fourteen new functional and tasks requirement considerations are proposed that highlight the interconnectedness between uncertainty and AI, as well as the role humans might need to play in the supervision and secure operation of such systems. In addition, 11 new and modified non-functional requirements, i.e., “ilities,” are provided and two new “ilities,” auditability and passive vulnerability, are also introduced. Ten problem areas with AI test, evaluation, verification and validation are noted, along with the need to determine reasonable risk estimates and acceptable thresholds for system performance. Lastly, multidisciplinary teams are needed for the design of effective and safe systems with embedded AI, and a new AI maintenance workforce should be developed for quality assurance of both underlying data and models. PubDate: 2023-01-26T00:00:00Z
Authors:Christopher D'Ambrosia, Eliah Aronoff-Spencer, Estella Y. Huang, Nicole H. Goldhaber, Henrik I. Christensen, Ryan C. Broderick, Lawrence G. Appelbaum Abstract: Surgeons operate in mentally and physically demanding workspaces where the impact of error is highly consequential. Accurately characterizing the neurophysiology of surgeons during intraoperative error will help guide more accurate performance assessment and precision training for surgeons and other teleoperators. To better understand the neurophysiology of intraoperative error, we build and deploy a system for intraoperative error detection and electroencephalography (EEG) signal synchronization during robot-assisted surgery (RAS). We then examine the association between EEG data and detected errors. Our results suggest that there are significant EEG changes during intraoperative error that are detectable irrespective of surgical experience level. PubDate: 2023-01-09T00:00:00Z
Authors:Luigi Bianchi, Raffaele Ferrante, Yaoping Hu, Guillermo Sahonero-Alvarez, Nusrat Z. Zenia Abstract: BackgroundIn the last decades, the P300 Speller paradigm was replicated in many experiments, and collected data were released to the public domain to allow research groups, particularly those in the field of machine learning, to test and improve their algorithms for higher performances of brain-computer interface (BCI) systems. Training data is needed to learn the identification of brain activity. The more training data are available, the better the algorithms will perform. The availability of larger datasets is highly desirable, eventually obtained by merging datasets from different repositories. The main obstacle to such merging is that all public datasets are released in various file formats because no standard way is established to share these data. Additionally, all datasets necessitate reading documents or scientific papers to retrieve relevant information, which prevents automating the processing. In this study, we thus adopted a unique file format to demonstrate the importance of having a standard and to propose which information should be stored and why.MethodsWe described our process to convert a dozen of P300 Speller datasets and reported the main encountered problems while converting them into the same file format. All the datasets are characterized by the same 6 × 6 matrix of alphanumeric symbols (characters and numbers or symbols) and by the same subset of acquired signals (8 EEG sensors at the same recording sites).Results and discussionNearly a million stimuli were converted, relative to about 7000 spelled characters and belonging to 127 subjects. The converted stimuli represent the most extensively available platform for training and testing new algorithms on the specific paradigm – the P300 Speller. The platform could potentially allow exploring transfer learning procedures to reduce or eliminate the time needed for training a classifier to improve the performance and accuracy of such BCI systems. PubDate: 2022-12-21T00:00:00Z
Authors:Martine Van Puyvelde, Daisy Gijbels, Thomas Van Caelenberg, Nathan Smith, Loredana Bessone, Susan Buckle-Charlesworth, Nathalie Pattyn Abstract: IntroductionIsolated, confined, and extreme (ICE) environments such as found at Antarctic, Arctic, and other remote research stations are considered space-analogs to study the long duration isolation aspects of operational space mission conditions.MethodsWe interviewed 24 sojourners that participated in different short/long duration missions in an Antarctic (Concordia, Halley VI, Rothera, Neumayer II) or non-Antarctic (e.g., MDRS, HI-SEAS) station or in polar treks, offering a unique insight based on first-hand information on the nature of demands by ICE-personnel at multiple levels of functioning. We conducted a qualitative thematic analysis to explore how sojourners were trained, prepared, how they experienced the ICE-impact in function of varieties in environment, provided trainings, station-culture, and type of mission.ResultsThe ICE-environment shapes the impact of organizational, interpersonal, and individual working- and living systems, thus influencing the ICE-sojourners' functioning. Moreover, more specific training for operating in these settings would be beneficial. The identified pillars such as sensory deprivation, sleep, fatigue, group dynamics, displacement of negative emotions, gender-issues along with coping strategies such as positivity, salutogenic effects, job dedication and collectivistic thinking confirm previous literature. However, in this work, we applied a systemic perspective, assembling the multiple levels of functioning in ICE-environments.DiscussionA systemic approach could serve as a guide to develop future preparatory ICE-training programs, including all the involved parties of the crew system (e.g., family, on-ground crew) with attention for the impact of organization- and station-related subcultures and the risk of unawareness about the impact of poor sleep, fatigue, and isolation on operational safety that may occur on location. PubDate: 2022-12-14T00:00:00Z
Authors:Elizabeth L. Fox, Margaret Ugolini, Joseph W. Houpt Abstract: IntroductionA well-designed brain-computer interface (BCI) can make accurate and reliable predictions of a user's state through the passive assessment of their brain activity; in turn, BCI can inform an adaptive system (such as artificial intelligence, or AI) to intelligently and optimally aid the user to maximize the human-machine team (HMT) performance. Various groupings of spectro-temporal neural features have shown to predict the same underlying cognitive state (e.g., workload) but vary in their accuracy to generalize across contexts, experimental manipulations, and beyond a single session. In our work we address an outstanding challenge in neuroergonomic research: we quantify if (how) identified neural features and a chosen modeling approach will generalize to various manipulations defined by the same underlying psychological construct, (multi)task cognitive workload.MethodsTo do this, we train and test 20 different support vector machine (SVM) models, each given a subset of neural features as recommended from previous research or matching the capabilities of commercial devices. We compute each model's accuracy to predict which (monitoring, communications, tracking) and how many (one, two, or three) task(s) were completed simultaneously. Additionally, we investigate machine learning model accuracy to predict task(s) within- vs. between-sessions, all at the individual-level.ResultsOur results indicate gamma activity across all recording locations consistently outperformed all other subsets from the full model. Our work demonstrates that modelers must consider multiple types of manipulations which may each influence a common underlying psychological construct.DiscussionWe offer a novel and practical modeling solution for system designers to predict task through brain activity and suggest next steps in expanding our framework to further contribute to research and development in the neuroergonomics community. Further, we quantified the cost in model accuracy should one choose to deploy our BCI approach using a mobile EEG-systems with fewer electrodes—a practical recommendation from our work. PubDate: 2022-11-23T00:00:00Z
Authors:Octavio Marin-Pardo, Miranda Rennie Donnelly, Coralie S. Phanord, Kira Wong, Jessica Pan, Sook-Lei Liew Abstract: Stroke is a leading cause of adult disability in the United States. High doses of repeated task-specific practice have shown promising results in restoring upper limb function in chronic stroke. However, it is currently challenging to provide such doses in clinical practice. At-home telerehabilitation supervised by a clinician is a potential solution to provide higher-dose interventions. However, telerehabilitation systems developed for repeated task-specific practice typically require a minimum level of active movement. Therefore, severely impaired people necessitate alternative therapeutic approaches. Measurement and feedback of electrical muscle activity via electromyography (EMG) have been previously implemented in the presence of minimal or no volitional movement to improve motor performance in people with stroke. Specifically, muscle neurofeedback training to reduce unintended co-contractions of the impaired hand may be a targeted intervention to improve motor control in severely impaired populations. Here, we present the preliminary results of a low-cost, portable EMG biofeedback system (Tele-REINVENT) for supervised and unsupervised upper limb telerehabilitation after stroke. We aimed to explore the feasibility of providing higher doses of repeated task-specific practice during at-home training. Therefore, we recruited 5 participants (age = 44–73 years) with chronic, severe impairment due to stroke (Fugl-Meyer = 19–40/66). They completed a 6-week home-based training program that reinforced activity of the wrist extensor muscles while avoiding coactivation of flexor muscles via computer games. We used EMG signals to quantify the contribution of two antagonistic muscles and provide biofeedback of individuated activity, defined as a ratio of extensor and flexor activity during movement attempt. Our data suggest that 30 1-h sessions over 6 weeks of at-home training with our Tele-REINVENT system is feasible and may improve individuated muscle activity as well as scores on standard clinical assessments (e.g., Fugl-Meyer Assessment, Action Research Arm Test, active wrist range of motion) for some individuals. Furthermore, tests of neuromuscular control suggest modest changes in the synchronization of electroencephalography (EEG) and EMG signals within the beta band (12–30 Hz). Finally, all participants showed high adherence to the training protocol and reported enjoying using the system. These preliminary results suggest that using low-cost technology for home-based telerehabilitation after severe chronic stroke is feasible and may be effective in improving motor control via feedback of individuated muscle activity. PubDate: 2022-11-17T00:00:00Z
Authors:Laura Angioletti, Michela Balconi Abstract: Little is known about how the modulation of the interoceptive focus impacts the neural correlates of high-level social processes, such as synchronization mechanisms. Therefore, the current study aims to explore the intraindividual electrophysiological (EEG) patterns induced by the interoceptive focus on breath when performing cognitive and motor tasks requiring interpersonal synchronization. A sample of 28 healthy caucasian adults was recruited and asked to perform two tasks requiring interpersonal synchronization during two distinct conditions: while focusing on the breath or without the focus on the breath. EEG frequency bands (delta, theta, alpha, and beta band) were recorded from the frontal, temporo-central, and parieto-occipital regions of interest. Significant results were observed for the delta and alpha bands. Notably, higher mean delta values and alpha desynchronization were observed in the temporo-central area during the focus on the breath condition when performing the motor compared to the cognitive synchronization task. Taken together these results could be interpreted considering the functional meaning of delta and alpha band in relation to motor synchronization. Indeed, motor delta oscillations shape the dynamics of motor behaviors and motor neural processes, while alpha band attenuation was previously observed during generation, observation, and imagery of movement and is considered to reflect cortical motor activity and action-perception coupling. Overall, the research shows that an EEG delta-alpha pattern emerges in the temporo-central areas at the intra-individual level, indicating the attention to visceral signals, particularly during interpersonal motor synchrony. PubDate: 2022-10-25T00:00:00Z
Authors:Hiroki Watanabe, Yasushi Naruse Abstract: The challenge level of goal achievement affects intrinsic motivation. Thus, the goal score learners are required to achieve is an important element in gamified educational applications to foster users' intrinsic motivation. However, determining optimal goal scores that enhance the intrinsic motivation of each learner is not easy because individual competence and preferences for the challenge level (e.g., preference for difficult-to-achieve challenges) vary. One approach is to determine the goal score using physiological measurements to estimate when an individual's intrinsic motivation is reinforced. Measurement of event-related potentials (ERPs) is considered useful for this purpose. ERPs time-locked to feedback onset, such as feedback-related negativity and P300, reflect intrinsic motivation. However, it remains unclear whether these ERPs can serve as indicators of optimal goal scores for gamified educational applications in terms of intrinsic motivation. The present study aimed to examine whether ERP measures vary with the challenge levels of the goal score determined by participants' competence (too-easy, moderate and too-hard levels) and/or with their preference for these levels when using a gamified mental arithmetic application. Thirty-three participants solved 64 addition problems in one session in this application and received auditory feedback immediately after each answer entry. Scores were then calculated based on their task performance. Before each session, participants were informed of the goal score and instructed to exceed it as much as possible. Sessions were repeated six times at easy, moderate, and hard levels of goal scores, with two sessions per level. Goal score preferences were quantified based on subjective ratings of the motivation to achieve each level of goal score using a 7-point Likert scale. The mean amplitudes of ERPs were obtained for each participant. Results showed that P300 was significantly related to subjective ratings but not to levels of goal scores, indicating that P300 could be an indicator of participant preference for goal score levels. This study suggests that measurement of P300 may serve as a neural indicator providing an optimal goal score for individual learners that maximizes their intrinsic motivation in gamified learning applications. PubDate: 2022-10-21T00:00:00Z
Authors:Ali Momen, Kurt Hugenberg, Eva Wiese Abstract: Robot faces often differ from human faces in terms of their facial features (e.g., lack of eyebrows) and spatial relationships between these features (e.g., disproportionately large eyes), which can influence the degree to which social brain [i.e., Fusiform Face Area (FFA), Superior Temporal Sulcus (STS); Haxby et al., 2000] areas process them as social individuals that can be discriminated from other agents in terms of their perceptual features and person attributes. Of interest in this work is whether robot stimuli are processed in a less social manner than human stimuli. If true, this could undermine human–robot interactions (HRIs) because human partners could potentially fail to perceive robots as individual agents with unique features and capabilities—a phenomenon known as outgroup homogeneity—potentially leading to miscalibration of trust and errors in allocation of task responsibilities. In this experiment, we use the face inversion paradigm (as a proxy for neural activation in social brain areas) to examine whether face processing differs between human and robot face stimuli: if robot faces are perceived as less face-like than human-faces, the difference in recognition performance for faces presented upright compared to upside down (i.e., inversion effect) should be less pronounced for robot faces than human faces. The results demonstrate a reduced face inversion effect with robot vs. human faces, supporting the hypothesis that robot faces are processed in a less face-like manner. This suggests that roboticists should attend carefully to the design of robot faces and evaluate them based on their ability to engage face-typical processes. Specific design recommendations on how to accomplish this goal are provided in the discussion. PubDate: 2022-10-20T00:00:00Z
Authors:Luciane Aparecida Moscaleski, André Fonseca, Rodrigo Brito, Edgard Morya, Ryland Morgans, Alexandre Moreira, Alexandre Hideki Okano Abstract: Differentiated brain activation in high-performance athletes supports neuronal mechanisms relevant to sports performance. Preparation for the motor action involves cortical and sub-cortical regions that can be non-invasively modulated by electrical current stimulation. This study aimed to investigate the effect of high-definition transcranial direct current stimulation (HD-tDCS) on electrical brain activity in professional female basketball players during free-throw shooting. Successful free-throw shooting (n = 2,361) from seven professional female basketball players was analyzed during two experimental conditions (HD-tDCS cathodic and sham) separated by 72 h. Three spectral bio-markers, Power Ratio Index (PRI), Delta Alpha Ratio (DAR), and Theta Beta Ratio (TBR) were measured (electroencephalography [EEG] Brain Products). Multi-channel HD-tDCS was applied for 20 min, considering current location and intensity for cathodic stimulation: FCC1h, AFF5h, AFF1h (−0.5 mA each), and FCC5h (ground). The within EEG analyses (pre and post HD-tDCS) of frontal channels (Fp1, Fp2, F3, F4, FC1, FC3) for 1 second epoch pre-shooting, showed increases in PRI (p < 0.001) and DAR (p < 0.001) for HD-tDCS cathodic condition, and in TBR for both conditions (cathodic, p = 0.01; sham, p = 0.002). Sub-group analysis divided the sample into less (n = 3; LSG) and more (n = 4; MSG) stable free-throw-shooting performers and revealed that increases in pre to post HD-tDCS in PRI only occurred for the LSG. These results suggest that the effect of HD-tDCS may induce changes in slow frontal frequency brain activities and that this alteration seems to be greater for players demonstrating a less stable free-throw shooting performance. PubDate: 2022-09-14T00:00:00Z
Authors:Marcel F. Hinss, Anke M. Brock, Raphaëlle N. Roy Abstract: Operators of complex systems across multiple domains (e.g., aviation, automotive, and nuclear power industry) are required to perform their tasks over prolonged and continuous periods of time. Mental fatigue as well as reduced cognitive flexibility, attention, and situational awareness all result from prolonged continuous use, putting at risk the safety and efficiency of complex operations. Mental state-based adaptive systems may be a solution to this problem. These systems infer the current mental state of an operator based on a selection of metrics ranging from operator independent measures (e.g., weather and time of day), to behavioral (e.g., reaction time and lane deviation) as well as physiological markers (e.g., electroencephalography and cardiac activity). The interaction between operator and system may then be adapted in one of many ways to mitigate any detected degraded cognitive state, thereby ensuring continued safety and efficiency. Depending on the task at hand and its specific problems, possible adaptations -usually based on machine learning estimations- e.g., include modifications of information, presentation modality or stimuli salience, as well as task scheduling. Research on adaptive systems is at the interface of several domains, including neuroergonomics, human factors, and human-computer interaction in an applied and ecological context, necessitating careful consideration of each of the aforementioned aspects. This article provides an overview of some of the key questions and aspects to be considered by researchers for the design of mental state-based adaptive systems, while also promoting their application during prolonged continuous use to pave the way toward safer and more efficient human-machine interaction. PubDate: 2022-08-26T00:00:00Z
Authors:Alexander Trende, Anirudh Unni, Mischa Jablonski, Bianca Biebl, Andreas Lüdtke, Martin Fränzle, Jochem W. Rieger Abstract: Traffic situations like turning at intersections are destined for safety-critical situations and accidents. Human errors are one of the main reasons for accidents in these situations. A model that recognizes the driver's turning intent could help to reduce accidents by warning the driver or stopping the vehicle before a dangerous turning maneuver. Most models that aim at predicting the probability of a driver's turning intent use only contextual information, such as gap size or waiting time. The objective of this study is to investigate whether the combination of context information and brain activation measurements enhances the recognition of turning intent. We conducted a driving simulator study while simultaneously measuring brain activation using high-density fNIRS. A neural network model for turning intent recognition was trained on the fNIRS and contextual data. The input variables were analyzed using SHAP (SHapley Additive exPlanations) feature importance analysis to show the positive effect of the inclusion of brain activation data. Both the model's evaluation and the feature importance analysis suggest that the combination of context information and brain activation leads to an improved turning intent recognition. The fNIRS results showed increased brain activation differences during the “turn” decision-making phase before turning execution in parts of the left motor cortices, such as the primary motor cortex (PMC; putative BA 4), premotor area (PMA; putative BA 6), and supplementary motor area (SMA; putative BA 8). Furthermore, we also observed increased activation differences in the left prefrontal areas, potentially in the left middle frontal gyrus (putative BA 9), which has been associated with the control of executive functions, such as decision-making and action planning. We hypothesize that brain activation measurements could be a more direct indicator with potentially high specificity for the turning behavior and thus help to increase the recognition model's performance. PubDate: 2022-08-16T00:00:00Z
Authors:Nicolas J. Bourguignon, Salvatore Lo Bue, Carlos Guerrero-Mosquera, Guillermo Borragán Abstract: Neuroergonomics focuses on the brain signatures and associated mental states underlying behavior to design human-machine interfaces enhancing performance in the cognitive and physical domains. Brain imaging techniques such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have been considered key methods for achieving this goal. Recent research stresses the value of combining EEG and fNIRS in improving these interface systems' mental state decoding abilities, but little is known about whether these improvements generalize over different paradigms and methodologies, nor about the potentialities for using these systems in the real world. We review 33 studies comparing mental state decoding accuracy between bimodal EEG-fNIRS and unimodal EEG and fNIRS in several subdomains of neuroergonomics. In light of these studies, we also consider the challenges of exploiting wearable versions of these systems in real-world contexts. Overall the studies reviewed suggest that bimodal EEG-fNIRS outperforms unimodal EEG or fNIRS despite major differences in their conceptual and methodological aspects. Much work however remains to be done to reach practical applications of bimodal EEG-fNIRS in naturalistic conditions. We consider these points to identify aspects of bimodal EEG-fNIRS research in which progress is expected or desired. PubDate: 2022-08-12T00:00:00Z
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