Frontiers in Neuroergonomics最新文献

{"title":"Transcranial Direct Current Stimulation Enhances Exercise Performance: A Mini Review of the Underlying Mechanisms","authors":"S. Jaberzadeh, M. Zoghi","doi":"10.3389/fnrgo.2022.841911","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.841911","url":null,"abstract":"Exercise performance (EP) is affected by a combination of factors including physical, physiological, and psychological factors. This includes factors such as peripheral, central, and mental fatigue, external peripheral factors such as pain and temperature, and psychological factors such as motivation and self-confidence. During the last century, numerous studies from different fields of research were carried out to improve EP by modifying these factors. During the last two decades, the focus of research has been mainly moved toward the brain as a dynamic ever-changing organ and the ways changes in this organ may lead to improvements in physical performance. Development of centrally-acting performance modifiers such as level of motivation or sleep deprivation and the emergence of novel non-invasive brain stimulation (NIBS) techniques such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are the key motives behind this move. This article includes three sections. Section Introduction provides an overview of the mechanisms behind the reduction of EP. The main focus of the Effects of tDCS on EP section is to provide a brief description of the effects of tDCS on maximal and submaximal types of exercise and finally, the section Mechanisms Behind the Effects of tDCS on EP provides description of the mechanisms behind the effects of tDCS on EP.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131780765","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":"Generating Flight Illusions Using Galvanic Vestibular Stimulation in Virtual Reality Flight Simulations","authors":"G. Pradhan, R. Galvan-Garza, Alison M. Perez, Jamie M. Bogle, M. Cevette","doi":"10.3389/fnrgo.2022.883962","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.883962","url":null,"abstract":"Background Vestibular flight illusions remain a significant source of concern for aviation training. Most fixed-based simulation training environments, including new virtual reality (VR) technology, lack the ability to recreate vestibular flight illusions as vestibular cues cannot be provided without stimulating the vestibular end organs. Galvanic vestibular stimulation (GVS) has long been used to create vestibular perception. The purpose of this study is to evaluate the ability of GVS to simulate common flight illusions by intentionally providing mismatched GVS during flight simulation scenarios in VR. Methods Nineteen participants performed two flight simulation tasks—take off and sustained turn—during two separate VR flight simulation sessions, with and without GVS (control). In the GVS session, specific multi-axis GVS stimulation (i.e., electric currents) was provided to induce approximate somatogravic and Coriolis illusions during the take-off and sustained turn tasks, respectively. The participants used the joystick to self-report their subjective motion perception. The angular joystick movement along the roll, yaw, and pitch axes was used to measure cumulative angular distance and peak angular velocity as continuous variables of motion perception across corresponding axes. Presence and Simulator Sickness Questionnaires were administered at the end of each session. Results The magnitude and variability of perceived somatogravic illusion during take-off task in the form of cumulative angular distance (p < 0.001) and peak velocity (p < 0.001) along the pitch-up axis among participants were significantly larger in the GVS session than in the NO GVS session. Similarly, during the sustained turn task, perceived Coriolis illusion in the form of cumulative angular distances (roll: p = 0.005, yaw: p = 0.015, pitch: p = 0.007) and peak velocities (roll: p = 0.003, yaw: p = 0.01, pitch: p = 0.007) across all three axes were significantly larger in the GVS session than in the NO GVS session. Subjective nausea was low overall, but significantly higher in the GVS session than in the NO GVS session (p = 0.026). Discussion Our findings demonstrated that intentionally mismatched GVS can significantly affect motion perception and create flight illusion perceptions during fixed-based VR flight simulation. This has the potential to enhance future training paradigms, providing pilots the ability to safely experience, identify, and learn to appropriately respond to flight illusions during ground training.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123766284","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":"Editorial: Cognitive Mechanisms for Safe Road Traffic Systems","authors":"G. Vecchiato, Christer Ahlström, L. Chuang","doi":"10.3389/fnrgo.2022.897659","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.897659","url":null,"abstract":"","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122136378","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 Transfer Learning Algorithm to Reduce Brain-Computer Interface Calibration Time for Long-Term Users","authors":"Joshua Giles, K. Ang, K. Phua, M. Arvaneh","doi":"10.3389/fnrgo.2022.837307","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.837307","url":null,"abstract":"Current motor imagery-based brain-computer interface (BCI) systems require a long calibration time at the beginning of each session before they can be used with adequate levels of classification accuracy. In particular, this issue can be a significant burden for long term BCI users. This article proposes a novel transfer learning algorithm, called r-KLwDSA, to reduce the BCI calibration time for long-term users. The proposed r-KLwDSA algorithm aligns the user's EEG data collected in previous sessions to the few EEG trials collected in the current session, using a novel linear alignment method. Thereafter, the aligned EEG trials from the previous sessions and the few EEG trials from the current sessions are fused through a weighting mechanism before they are used for calibrating the BCI model. To validate the proposed algorithm, a large dataset containing the EEG data from 11 stroke patients, each performing 18 BCI sessions, was used. The proposed framework demonstrated a significant improvement in the classification accuracy, of over 4% compared to the session-specific algorithm, when there were as few as two trials per class available from the current session. The proposed algorithm was particularly successful in improving the BCI accuracy of the sessions that had initial session-specific accuracy below 60%, with an average improvement of around 10% in the accuracy, leading to more stroke patients having meaningful BCI rehabilitation.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126502630","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":"Cognitive Performances: The Role of Digit Ratio (D2:D4) With a Protective Factor for Anxiety","authors":"Sergio Rinella, Simona Massimino, Alessia Sorbello, V. Perciavalle, M. Coco","doi":"10.3389/fnrgo.2022.870362","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.870362","url":null,"abstract":"This study aimed to identify a possible correlation between the D2:D4 ratio and state and/or trait anxiety in adult healthy subjects and, if so, whether it exists any difference between men and women. In addition, we also wanted to observe whether there is a relationship between participants' age and state and/or trait anxiety. The research involved 125 subjects of both sexes, who were calculated the D2:D4 ratio and were administered the self-assessment questionnaire State-Trait Anxiety Inventory (STAI-Y). Results show that there are positive significant correlations between the D2:D4 ratio and score at state anxiety and trait anxiety, in the total sample. However, if men are examined separately from women, it can be observed that only men have a statistically significant relationship between D2:D4 ratios and state anxiety and trait anxiety. Moreover, about possible relations between the age of participants and state and trait anxiety, a significant negative relationship was observed, without differences between men and women. However, only subjects with a D2:D4 ratio ≥ 1, without differences between men and women, showed a statistically significant negative linear correlation between their age and their state and trait anxiety. The present data allow us to conclude that a low D2:D4 ratio (<1) represents a protective factor against anxiety in both men and women and that this protection seems likely to act throughout life.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129085556","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":"Investigating the Single Trial Detectability of Cognitive Face Processing by a Passive Brain-Computer Interface","authors":"Rebecca Pham Xuan, Lena M. Andreessen, T. Zander","doi":"10.3389/fnrgo.2021.754472","DOIUrl":"https://doi.org/10.3389/fnrgo.2021.754472","url":null,"abstract":"An automated recognition of faces enables machines to visually identify a person and to gain access to non-verbal communication, including mimicry. Different approaches in lab settings or controlled realistic environments provided evidence that automated face detection and recognition can work in principle, although applications in complex real-world scenarios pose a different kind of problem that could not be solved yet. Specifically, in autonomous driving—it would be beneficial if the car could identify non-verbal communication of pedestrians or other drivers, as it is a common way of communication in daily traffic. Automated identification from observation whether pedestrians or other drivers communicate through subtle cues in mimicry is an unsolved problem so far, as intent and other cognitive factors are hard to derive from observation. In contrast, communicating persons usually have clear understanding whether they communicate or not, and such information is represented in their mindsets. This work investigates whether the mental processing of faces can be identified through means of a Passive Brain-Computer Interface (pBCI). This then could be used to support the cars' autonomous interpretation of facial mimicry of pedestrians to identify non-verbal communication. Furthermore, the attentive driver can be utilized as a sensor to improve the context awareness of the car in partly automated driving. This work presents a laboratory study in which a pBCI is calibrated to detect responses of the fusiform gyrus in the electroencephalogram (EEG), reflecting face recognition. Participants were shown pictures from three different categories: faces, abstracts, and houses evoking different responses used to calibrate the pBCI. The resulting classifier could distinguish responses to faces from that evoked by other stimuli with accuracy above 70%, in a single trial. Further analysis of the classification approach and the underlying data identified activation patterns in the EEG that corresponds to face recognition in the fusiform gyrus. The resulting pBCI approach is promising as it shows better-than-random accuracy and is based on relevant and intended brain responses. Future research has to investigate whether it can be transferred from the laboratory to the real world and how it can be implemented into artificial intelligences, as used in autonomous driving.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133750806","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":"Neuroscientific Methods for Exploring User Perceptions While Dealing With Mobile Advertising: A Novel and Integrated Approach","authors":"Marco Mancini, P. Cherubino, Gianluca di Flumeri, G. Cartocci, A. Martínez, Alessandro Sanchez, C. Santillo, E. Modica, A. Vozzi, V. Ronca, A. Trettel, G. Borghini, F. Babiloni","doi":"10.3389/fnrgo.2022.835648","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.835648","url":null,"abstract":"Display and native ads represent two of the most widely used digital advertising formats employed by advertisers that aim to grab the attention of online users. In recent years, the native format has become very popular because it relies on deceptive features that make harder the recognition of its advertising nature, reducing avoiding behaviors such as the banner blindness phenomena, traditionally associated to display advertising, and so increasing its advertising effectiveness. The present study, based on a forefront research protocol specifically designed for the advertising research on smartphone devices, aims to investigate through neurophysiological and self-reported measures, the perception of display and native ads placed within article webpages, and to assess the efficacy of an integrated approach. Eye-tracking results showed higher visual attention and longer viewing time associated with native advertisements in comparison to traditional display advertisements, confirming and extending evidence provided by previous research. Despite a significantly higher rate of self-reported advertising intent was detected for articles containing display ads when compared to articles containing native ads, no differences have been found while performing the same comparison for the neurophysiological measures of emotional involvement and approaching motivation of for the self-reported measures of pleasantness and annoyance. Such findings along with the employment of an innovative research protocol, contribute to providing further cues to the current debate related to the effectiveness of two of the most widely used digital advertising formats.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131432076","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":"Using fNIRS to Identify Transparency- and Reliability-Sensitive Markers of Trust Across Multiple Timescales in Collaborative Human-Human-Agent Triads","authors":"Lucca Eloy, Emily Doherty, Cara A. Spencer, P. Bobko, Leanne M. Hirshfield","doi":"10.3389/fnrgo.2022.838625","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.838625","url":null,"abstract":"Intelligent agents are rapidly evolving from assistants into teammates as they perform increasingly complex tasks. Successful human-agent teams leverage the computational power and sensory capabilities of automated agents while keeping the human operator's expectation consistent with the agent's ability. This helps prevent over-reliance on and under-utilization of the agent to optimize its effectiveness. Research at the intersection of human-computer interaction, social psychology, and neuroergonomics has identified trust as a governing factor of human-agent interactions that can be modulated to maintain an appropriate expectation. To achieve this calibration, trust can be monitored continuously and unobtrusively using neurophysiological sensors. While prior studies have demonstrated the potential of functional near-infrared spectroscopy (fNIRS), a lightweight neuroimaging technology, in the prediction of social, cognitive, and affective states, few have successfully used it to measure complex social constructs like trust in artificial agents. Even fewer studies have examined the dynamics of hybrid teams of more than 1 human or 1 agent. We address this gap by developing a highly collaborative task that requires knowledge sharing within teams of 2 humans and 1 agent. Using brain data obtained with fNIRS sensors, we aim to identify brain regions sensitive to changes in agent behavior on a long- and short-term scale. We manipulated agent reliability and transparency while measuring trust, mental demand, team processes, and affect. Transparency and reliability levels are found to significantly affect trust in the agent, while transparency explanations do not impact mental demand. Reducing agent communication is shown to disrupt interpersonal trust and team cohesion, suggesting similar dynamics as human-human teams. Contrasts of General Linear Model analyses identify dorsal medial prefrontal cortex activation specific to assessing the agent's transparency explanations and characterize increases in mental demand as signaled by dorsal lateral prefrontal cortex and frontopolar activation. Short scale event-level data is analyzed to show that predicting whether an individual will trust the agent, with data from 15 s before their decision, is feasible with fNIRS data. Discussing our results, we identify targets and directions for future neuroergonomics research as a step toward building an intelligent trust-modulation system to optimize human-agent collaborations in real time.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"7 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114100610","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":"Retrospective on the First Passive Brain-Computer Interface Competition on Cross-Session Workload Estimation","authors":"R. Roy, Marcel F. Hinss, L. Darmet, S. Ladouce, E. Jahanpour, B. Somon, Xiaoqi Xu, Nicolas Drougard, F. Dehais, F. Lotte","doi":"10.3389/fnrgo.2022.838342","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.838342","url":null,"abstract":"As is the case in several research domains, data sharing is still scarce in the field of Brain-Computer Interfaces (BCI), and particularly in that of passive BCIs—i.e., systems that enable implicit interaction or task adaptation based on a user's mental state(s) estimated from brain measures. Moreover, research in this field is currently hindered by a major challenge, which is tackling brain signal variability such as cross-session variability. Hence, with a view to develop good research practices in this field and to enable the whole community to join forces in working on cross-session estimation, we created the first passive brain-computer interface competition on cross-session workload estimation. This competition was part of the 3rd International Neuroergonomics conference. The data were electroencephalographic recordings acquired from 15 volunteers (6 females; average 25 y.o.) who performed 3 sessions—separated by 7 days—of the Multi-Attribute Task Battery-II (MATB-II) with 3 levels of difficulty per session (pseudo-randomized order). The data -training and testing sets—were made publicly available on Zenodo along with Matlab and Python toy code (https://doi.org/10.5281/zenodo.5055046). To this day, the database was downloaded more than 900 times (unique downloads of all version on the 10th of December 2021: 911). Eleven teams from 3 continents (31 participants) submitted their work. The best achieving processing pipelines included a Riemannian geometry-based method. Although better than the adjusted chance level (38% with an α at 0.05 for a 3-class classification problem), the results still remained under 60% of accuracy. These results clearly underline the real challenge that is cross-session estimation. Moreover, they confirmed once more the robustness and effectiveness of Riemannian methods for BCI. On the contrary, chance level results were obtained by one third of the methods—4 teams- based on Deep Learning. These methods have not demonstrated superior results in this contest compared to traditional methods, which may be due to severe overfitting. Yet this competition is the first step toward a joint effort to tackle BCI variability and to promote good research practices including reproducibility.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116601339","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":"Neuroadaptive Training via fNIRS in Flight Simulators","authors":"Jesse Mark, Amanda E. Kraft, M. Ziegler, H. Ayaz","doi":"10.3389/fnrgo.2022.820523","DOIUrl":"https://doi.org/10.3389/fnrgo.2022.820523","url":null,"abstract":"Training to master a new skill often takes a lot of time, effort, and financial resources, particularly when the desired skill is complex, time sensitive, or high pressure where lives may be at risk. Professions such as aircraft pilots, surgeons, and other mission-critical operators that fall under this umbrella require extensive domain-specific dedicated training to enable learners to meet real-world demands. In this study, we describe a novel neuroadaptive training protocol to enhance learning speed and efficiency using a neuroimaging-based cognitive workload measurement system in a flight simulator. We used functional near-infrared spectroscopy (fNIRS), which is a wearable, mobile, non-invasive neuroimaging modality that can capture localized hemodynamic response and has been used extensively to monitor the anterior prefrontal cortex to estimate cognitive workload. The training protocol included four sessions over 2 weeks and utilized realistic piloting tasks with up to nine levels of difficulty. Learners started at the lowest level and their progress adapted based on either behavioral performance and fNIRS measures combined (neuroadaptive) or performance measures alone (control). Participants in the neuroadaptive group were found to have significantly more efficient training, reaching higher levels of difficulty or significantly improved performance depending on the task, and showing consistent patterns of hemodynamic-derived workload in the dorsolateral prefrontal cortex. The results of this study suggest that a neuroadaptive personalized training protocol using non-invasive neuroimaging is able to enhance learning of new tasks. Finally, we outline here potential avenues for further optimization of this fNIRS based neuroadaptive training approach. As fNIRS mobile neuroimaging is becoming more practical and accessible, the approaches developed here can be applied in the real world in scale.","PeriodicalId":207447,"journal":{"name":"Frontiers in Neuroergonomics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127774320","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}