Frontiers in neuroergonomics最新文献

{"title":"Editorial: Neurotechnology for brain-body performance and health: insights from the 2022 Neuroergonomics and NYC Neuromodulation Conference.","authors":"Marom Bikson, Leigh Charvet, Giuseppina Pilloni, Frederic Dehais, Hasan Ayaz","doi":"10.3389/fnrgo.2024.1454889","DOIUrl":"https://doi.org/10.3389/fnrgo.2024.1454889","url":null,"abstract":"","PeriodicalId":517413,"journal":{"name":"Frontiers in neuroergonomics","volume":"5 ","pages":"1454889"},"PeriodicalIF":1.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11405341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142305620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Stress and the brain: advances in neurophysiological measures for mental stress detection and reduction.","authors":"Stefania Coelli, Eleonora Maggioni, Martin O Mendez","doi":"10.3389/fnrgo.2024.1466783","DOIUrl":"https://doi.org/10.3389/fnrgo.2024.1466783","url":null,"abstract":"","PeriodicalId":517413,"journal":{"name":"Frontiers in neuroergonomics","volume":"5 ","pages":"1466783"},"PeriodicalIF":1.5,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142116781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Advances and challenges to bridge computational intelligence and neuroscience for brain-computer interface.","authors":"Avinash Kumar Singh, Luigi Bianchi, Davide Valeriani, Masaki Nakanishi","doi":"10.3389/fnrgo.2024.1461494","DOIUrl":"10.3389/fnrgo.2024.1461494","url":null,"abstract":"","PeriodicalId":517413,"journal":{"name":"Frontiers in neuroergonomics","volume":"5 ","pages":"1461494"},"PeriodicalIF":1.5,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142010200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Neurotechnology for sensing the brain out of the lab: methods and applications for mobile functional neuroimaging.","authors":"Hasan Ayaz, Frederic Dehais, Giuseppina Pilloni, Leigh Charvet, Marom Bikson","doi":"10.3389/fnrgo.2024.1454894","DOIUrl":"10.3389/fnrgo.2024.1454894","url":null,"abstract":"","PeriodicalId":517413,"journal":{"name":"Frontiers in neuroergonomics","volume":"5 ","pages":"1454894"},"PeriodicalIF":1.5,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11334157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142010201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Open science to support replicability in neuroergonomic research.","authors":"Klaus Gramann, Fabien Lotte, Frederic Dehais, Hasan Ayaz, Mathias Vukelić, Waldemar Karwowski, Stephen Fairclough, Anne-Marie Brouwer, Raphaëlle N Roy","doi":"10.3389/fnrgo.2024.1459204","DOIUrl":"10.3389/fnrgo.2024.1459204","url":null,"abstract":"","PeriodicalId":517413,"journal":{"name":"Frontiers in neuroergonomics","volume":"5 ","pages":"1459204"},"PeriodicalIF":1.5,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11319283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141977553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA.","authors":"Daniel E Callan, Juan Jesus Torre-Tresols, Jamie Laguerta, Shin Ishii","doi":"10.3389/fnrgo.2024.1358660","DOIUrl":"10.3389/fnrgo.2024.1358660","url":null,"abstract":"<p><strong>Introduction: </strong>To understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp.</p><p><strong>Methods: </strong>A dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment.</p><p><strong>Results: </strong>Results showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects.</p><p><strong>Discussion: </strong>This study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.</p>","PeriodicalId":517413,"journal":{"name":"Frontiers in neuroergonomics","volume":"5 ","pages":"1358660"},"PeriodicalIF":1.5,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11233536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Encoding temporal information in deep convolution neural network.","authors":"Avinash Kumar Singh, Luigi Bianchi","doi":"10.3389/fnrgo.2024.1287794","DOIUrl":"10.3389/fnrgo.2024.1287794","url":null,"abstract":"<p><p>A recent development in deep learning techniques has attracted attention to the decoding and classification of electroencephalogram (EEG) signals. Despite several efforts to utilize different features in EEG signals, a significant research challenge is using time-dependent features in combination with local and global features. Several attempts have been made to remodel the deep learning convolution neural networks (CNNs) to capture time-dependency information. These features are usually either handcrafted features, such as power ratios, or splitting data into smaller-sized windows related to specific properties, such as a peak at 300 ms. However, these approaches partially solve the problem but simultaneously hinder CNNs' capability to learn from unknown information that might be present in the data. Other approaches, like recurrent neural networks, are very suitable for learning time-dependent information from EEG signals in the presence of unrelated sequential data. To solve this, we have proposed an encoding kernel (EnK), a novel time-encoding approach, which uniquely introduces time decomposition information during the vertical convolution operation in CNNs. The encoded information lets CNNs learn time-dependent features in addition to local and global features. We performed extensive experiments on several EEG data sets-physical human-robot collaborations, P300 visual-evoked potentials, motor imagery, movement-related cortical potentials, and the Dataset for Emotion Analysis Using Physiological Signals. The EnK outperforms the state of the art with an up to 6.5% reduction in mean squared error (MSE) and a 9.5% improvement in F1-scores compared to the average for all data sets together compared to base models. These results support our approach and show a high potential to improve the performance of physiological and non-physiological data. Moreover, the EnK can be applied to virtually any deep learning architecture with minimal effort.</p>","PeriodicalId":517413,"journal":{"name":"Frontiers in neuroergonomics","volume":"5 ","pages":"1287794"},"PeriodicalIF":1.5,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11220250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Affective computing and mental workload assessment to enhance human-machine interaction.","authors":"Sabrina Iarlori, Andrea Monteriú, David Perpetuini, Chiara Filippini, Daniela Cardone","doi":"10.3389/fnrgo.2024.1412744","DOIUrl":"https://doi.org/10.3389/fnrgo.2024.1412744","url":null,"abstract":"","PeriodicalId":517413,"journal":{"name":"Frontiers in neuroergonomics","volume":"5 ","pages":"1412744"},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11167130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid framework of fatigue: connecting motivational control and computational moderators to gamma oscillations.","authors":"Lorraine Borghetti, Taylor Curley, L Jack Rhodes, Megan B Morris, Bella Z Veksler","doi":"10.3389/fnrgo.2024.1375913","DOIUrl":"10.3389/fnrgo.2024.1375913","url":null,"abstract":"<p><strong>Introduction: </strong>There is a need to develop a comprehensive account of time-on-task fatigue effects on performance (i.e., the vigilance decrement) to increase predictive accuracy. We address this need by integrating three independent accounts into a novel hybrid framework. This framework unites (1) a motivational system balancing goal and comfort drives as described by an influential cognitive-energetic theory with (2) accumulating microlapses from a recent computational model of fatigue, and (3) frontal gamma oscillations indexing fluctuations in motivational control. Moreover, the hybrid framework formally links brief lapses (occurring over milliseconds) to the dynamics of the motivational system at a temporal scale not otherwise described in the fatigue literature.</p><p><strong>Methods: </strong>EEG and behavioral data was collected from a brief vigilance task. High frequency gamma oscillations were assayed, indexing effortful controlled processes with motivation as a latent factor. Binned and single-trial gamma power was evaluated for changes in real- and lagged-time and correlated with behavior. Functional connectivity analyses assessed the directionality of gamma power in frontal-parietal communication across time-on-task. As a high-resolution representation of latent motivation, gamma power was scaled by fatigue moderators in two computational models. Microlapses modulated transitions from an effortful controlled state to a minimal-effort default state. The hybrid models were compared to a computational microlapse-only model for goodness-of-fit with simulated data.</p><p><strong>Results: </strong>Findings suggested real-time high gamma power exhibited properties consistent with effortful motivational control. However, gamma power failed to correlate with increases in response times over time, indicating electrophysiology and behavior relations are insufficient in capturing the full range of fatigue effects. Directional connectivity affirmed the dominance of frontal gamma activity in controlled processes in the frontal-parietal network. Parameterizing high frontal gamma power, as an index of fluctuating relative motivational control, produced results that are as accurate or superior to a previous microlapse-only computational model.</p><p><strong>Discussion: </strong>The hybrid framework views fatigue as a function of a energetical motivational system, managing the trade-space between controlled processes and competing wellbeing needs. Two gamma computational models provided compelling and parsimonious support for this framework, which can potentially be applied to fatigue intervention technologies and related effectiveness measures.</p>","PeriodicalId":517413,"journal":{"name":"Frontiers in neuroergonomics","volume":"5 ","pages":"1375913"},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11165150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Right prefrontal activation associated with deviations from expected lipstick texture assessed with functional near-infrared spectroscopy.","authors":"Kazue Hirabayashi, Keith Kawabata Duncan, Keiko Tagai, Yasushi Kyutoku, Ippeita Dan","doi":"10.3389/fnrgo.2024.1331083","DOIUrl":"10.3389/fnrgo.2024.1331083","url":null,"abstract":"<p><strong>Introduction: </strong>There is a continuous consumer demand for ever superior cosmetic products. In marketing, various forms of sensory evaluation are used to measure the consumer experience and provide data with which to improve cosmetics. Nonetheless, potential downsides of existing approaches have led to the exploration of the use of neuroimaging methods, such as functional near-infrared spectroscopy (fNIRS), to provide addition information about consumers' experiences with cosmetics. The aim of the present study was to investigate the feasibility of a real-time brain-based product evaluation method which detects the incongruency between a product, in this case lipstick, and a consumer's expectations.</p><p><strong>Method: </strong>Thirty healthy, female, habitual lipstick users were asked to apply six different lipsticks varying in softness and to rate the softness of and their willingness to pay (WTP) for each lipstick. Cerebral hemodynamic responses in frontal areas were measured with fNIRS during lipstick application and analyzed using the general linear model (GLM). Incongruency scores between softness and expectation were calculated in order to understand how far removed each lipstick was from a participant's optimal softness preference. The correlation between brain activation (beta scores) during the application of each lipstick and the respective incongruency scores from each participant were acquired using semi-partial correlation analysis, controlling for the effects of WTP.</p><p><strong>Results: </strong>We revealed a significant intra-subject correlation between incongruency scores and activation in the right inferior frontal gyrus (IFG). This confirms that as the texture incongruency scores increased for the lipstick samples, activation in each individual's right IFG also increased.</p><p><strong>Conclusion: </strong>The correlation observed between incongruency perceived by participants and activation of the right IFG not only suggests that the right IFG may play an important role in detecting incongruity when there is a discrepancy between the perceived texture and the consumer's expectations but also that measuring activity in the IFG may provide a new objective measurement of the consumer experience, thus contributing to the development of superior cosmetics.</p>","PeriodicalId":517413,"journal":{"name":"Frontiers in neuroergonomics","volume":"5 ","pages":"1331083"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11094294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}