Frontiers in Neuroergonomics最新文献

{"title":"Investigating the impact of visual perspective in a motor imagery-based brain-robot interaction: A pilot study with healthy participants","authors":"Andrea Farabbi, P. Figueiredo, Fabiola Ghiringhelli, L. Mainardi, J. Sanches, Plinio Moreno, J. Santos-Victor, A. Vourvopoulos","doi":"10.3389/fnrgo.2023.1080794","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1080794","url":null,"abstract":"Introduction Motor Imagery (MI)-based Brain Computer Interfaces (BCI) have raised gained attention for their use in rehabilitation therapies since they allow controlling an external device by using brain activity, in this way promoting brain plasticity mechanisms that could lead to motor recovery. Specifically, rehabilitation robotics can provide precision and consistency for movement exercises, while embodied robotics could provide sensory feedback that can help patients improve their motor skills and coordination. However, it is still not clear whether different types of visual feedback may affect the elicited brain response and hence the effectiveness of MI-BCI for rehabilitation. Methods In this paper, we compare two visual feedback strategies based on controlling the movement of robotic arms through a MI-BCI system: 1) first-person perspective, with visual information that the user receives when they view the robot arms from their own perspective; and 2) third-person perspective, whereby the subjects observe the robot from an external perspective. We studied 10 healthy subjects over three consecutive sessions. The electroencephalographic (EEG) signals were recorded and evaluated in terms of the power of the sensorimotor rhythms, as well as their lateralization, and spatial distribution. Results Our results show that both feedback perspectives can elicit motor-related brain responses, but without any significant differences between them. Moreover, the evoked responses remained consistent across all sessions, showing no significant differences between the first and the last session. Discussion Overall, these results suggest that the type of perspective may not influence the brain responses during a MI- BCI task based on a robotic feedback, although, due to the limited sample size, more evidence is required. Finally, this study resulted into the production of 180 labeled MI EEG datasets, publicly available for research purposes.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122519840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical neuroimaging and neurostimulation in surgical training and assessment: A state-of-the-art review","authors":"M. Goble, V. Caddick, Ronak Patel, H. Modi, A. Darzi, F. Orihuela-Espina, D. Leff","doi":"10.3389/fnrgo.2023.1142182","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1142182","url":null,"abstract":"Introduction 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. Methods A literature search of PubMed and Embase was conducted to identify neuroimaging studies using fNIRS and neurostimulation in surgeons performing simulated tasks. Results 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. Conclusion 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.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131196860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking framework for machine learning classification from fNIRS data","authors":"Johann Benerradi, Jérémie Clos, A. Landowska, M. Valstar, Max L Wilson","doi":"10.3389/fnrgo.2023.994969","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.994969","url":null,"abstract":"Background While 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. Methods We 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 discussion Results 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.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122147882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A narrative review of the interconnection between pilot acute stress, startle, and surprise effects in the aviation context: Contribution of physiological measurements","authors":"M. Diarra, M. Marchitto, M. Bressolle, T. Baccino, Véronique Drai-Zerbib","doi":"10.3389/fnrgo.2023.1059476","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1059476","url":null,"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.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133451892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grand challenges in physical neuroergonomics","authors":"S. Perrey","doi":"10.3389/fnrgo.2023.1137854","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1137854","url":null,"abstract":"COPYRIGHT © 2023 Perrey. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Grand challenges in physical neuroergonomics","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124064395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the acceptability of BCIs for motor rehabilitation after stroke: A large scale study on the general public","authors":"Elise Grevet, Killyam Forge, Sebastien Tadiello, Margaux Izac, F. Amadieu, L. Brunel, Léa Pillette, J. Py, D. Gasq, Camille Jeunet-Kelway","doi":"10.3389/fnrgo.2022.1082901","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.1082901","url":null,"abstract":"Introduction Strokes 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. Methods The 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. Results Results 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. Discussion With 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.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116620684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revising human-systems engineering principles for embedded AI applications","authors":"M. Cummings","doi":"10.3389/fnrgo.2023.1102165","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1102165","url":null,"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.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125264638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The neurophysiology of intraoperative error: An EEG study of trainee surgeons during robotic-assisted surgery simulations","authors":"Christopher D'Ambrosia, Eliah Aronoff-Spencer, Estella Y. Huang, N. Goldhaber, H. Christensen, R. Broderick, L. G. Appelbaum","doi":"10.3389/fnrgo.2022.1052411","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.1052411","url":null,"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.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125070741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls","authors":"L. Bianchi, R. Ferrante, Yaoping Hu, Guillermo Sahonero-Alvarez, N. Z. Zenia","doi":"10.3389/fnrgo.2022.1045653","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.1045653","url":null,"abstract":"Background In 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. Methods We 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 discussion Nearly 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.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115599757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Living on the edge: How to prepare for it?","authors":"Martine Van Puyvelde, Daisy Gijbels, Thomas Van Caelenberg, Nathan J. Smith, L. Bessone, Susan Buckle-Charlesworth, N. Pattyn","doi":"10.3389/fnrgo.2022.1007774","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.1007774","url":null,"abstract":"Introduction Isolated, 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. Methods We 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. Results The 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. Discussion A 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.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126497656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}