Frontiers in neuroergonomics最新文献

Introduction: Recent advancements in augmented reality (AR) technology have opened up potential applications across various industries. In this study, we assess the effectiveness of psychomotor learning in AR compared to video-based training methods.

Methods: Thirty-three participants (17 males) trained on four selection-based AR interactions by either watching a video or engaging in hands-on practice. Both groups were evaluated by executing these learned interactions in AR.

Results: The AR group reported a higher subjective workload during training but showed significantly faster completion times during evaluation. We analyzed brain activation and functional connectivity using functional near-infrared spectroscopy during the evaluation phase. Our findings indicate that participants who trained in AR displayed more efficient brain networks, suggesting improved neural efficiency.

Discussion: Differences in sex-related activation and connectivity hint at varying neural strategies used during motor learning in AR. Future studies should investigate how demographic factors might influence performance and user experience in AR-based training programs.

Introduction: This study investigates the neural basis of sound perception in everyday life using EEG data recorded in an office-like environment over 3.5 h. We aimed to understand how contextual factors such as personal relevance, task complexity, and stimulus properties influence auditory processing in ecologically valid settings.

Methods: By systematically increasing the complexity of acoustic scenes and tasks, we analyzed changes in neural responses, focusing on the N100 and P300 components.

Results: Our results show that while the P300 is a stable marker of attention in both isolated sounds and complex soundscapes, the N100 is more sensitive to task complexity and environmental factors.

Discussion: These findings highlight the importance of context in shaping auditory perception and suggest that laboratory-based findings can be partially generalized to real-world settings. At the same time, task demands significantly influence neural markers. This opens new opportunities to study sound perception in naturalistic environments without sacrificing the control typically afforded by laboratory studies.

Introduction: Mental Workload (MWL) is a concept that has garnered increasing interest in professional settings but remains challenging to define consensually. The literature reports a plurality of operational definitions and assessment methods, with no established unified framework. This review aims to identify objective and validated measurement methods for evaluating MWL in real-world work contexts. Particular attention is given to neurophysiological methods, recognized for their efficiency and robustness, enabling real-time assessment without disrupting operator activity.

Method: To conduct this analysis, a systematic search was performed in three databases (PubMed, ScienceDirect, and IEEEXplore), covering studies published from their inception until March 30, 2023. Selection criteria included research focusing on MWL and its derivatives, as well as neurophysiological measures applied in real-world conditions. An initial screening based on titles and abstracts was followed by an in-depth review, assisted by the bibliometric software Rayyan.

Results: The explored concepts, applied methods, and study results were compiled into a synthesis table. Ultimately, 35 studies were included, highlighting the diversity of measurement tools used in field settings, often combined with subjective assessments.

Discussion: Furthermore, key physiological indicators such as ECG, eye data, EEG and the relationship between MWL metrics and those uses to measure stress are emphasized and discussed. A better understanding of these interrelations could refine the assessment of their respective impacts and help anticipate their consequences on workers' mental health and safety.

Background: Decline in vigilance due to fatigue is a common concern in traffic safety. Partially automated driving (PAD) systems can aid driving but decrease the driver's vigilance over time, due to reduced task engagement. Mobile EEG solutions can obtain neural information while operating a vehicle. The purpose of this study was to investigate how the behavior and brain activity associated with vigilance (i.e., alpha, beta and theta power) differs between PAD and manual driving, as well as changes over time, and how these effects can be detected using two different EEG systems.

Methods: Twenty-eight participants performed two 1-h simulated driving tasks, while wearing both a standard 24 channel EEG cap and a newly developed, unobtrusive and easy to apply 10 channel mobile EEG sensor-grid system. One scenario required manual control of the vehicle (manual) while the other required only monitoring the vehicle (PAD). Additionally, lane deviation, percentage eye-closure (PERCLOS) and subjective ratings of workload, fatigue and stress were obtained.

Results: Alpha, beta and theta power of the EEG as well as PERCLOS were higher in the PAD condition and increased over time in both conditions. The same spectral EEG effects were evident in both EEG systems. Lane deviation as an index of driving performance in the manual driving condition increased over time.

Conclusion: These effects indicate significant increases in fatigue and vigilance decrement over time while driving, and overall higher levels of fatigue and vigilance decrement associated with PAD. The EEG measures revealed significant effects earlier than the behavioral measures, demonstrating that EEG might allow faster detection of decreased vigilance than behavioral driving measures. This new, mobile EEG-grid system could be used to evaluate and improve driver monitoring systems in the field or even be used in the future as additional sensor to inform drivers of critical changes in their level of vigilance. In addition to driving, further areas of application for this EEG-sensor grid are safety critical work environments where vigilance monitoring is pivotal.

Objective: This study aims to investigate the relationship between the subjective performance evaluations on pilot trainees' aircraft control abilities and their brainwave dynamics reflected in the results from EEG microstate analysis. Specifically, we seek to identify correlations between distinct microstate patterns and each dimension included in the subjective flight control evaluations, shedding light on the neurophysiological mechanisms underlying aviation expertise and possible directions for future improvements in pilot training.

Background: Proficiency in aircraft control is crucial for aviation safety and modern aviation where pilots need to maneuver aircraft through an array of situations, ranging from routine takeoffs and landings to complex weather conditions and emergencies. However, the neurophysiological aspects of aviation expertise remain largely unexplored. This research bridges the gap by examining the relationship between pilot trainees' specific brainwave patterns and their subjective evaluations of flight control levels, offering insights into the cognitive underpinnings of pilot skill efficiency and development.

Method: EEG microstate analysis was employed to examine the brainwave dynamics of pilot trainees while they performed aircraft control tasks under a flight simulator-based pilot training process. Trainees' control performance was evaluated by experienced instructors across five dimensions and their EEG data were analyzed to investigate the associations between the parameters of specific microstates with successful aircraft control.

Results: The experimental results revealed significant associations between aircraft control levels and the parameters of distinct EEG microstates. Notably, these associations varied across control dimensions, highlighting the multifaceted nature of control proficiency. Noteworthy correlations included positive correlations between microstate class E and class G with aircraft control, emphasizing the role of attentional processes, perceptual integration, working memory, cognitive flexibility, decision-making, and executive control in aviation expertise. Conversely, negative correlations between microstate class C and class F with aircraft control indicated links between pilot trainees' cognitive control and their control performance on flight tasks.

Conclusion: The findings underscore the multidimensional nature of aircraft control proficiency and emphasize the significance of attentional and cognitive processes in achieving aviation expertise. These neurophysiological markers offer a basis for designing targeted pilot training programs and interventions to enhance trainees' aircraft control skills.

Introduction: Recently, a link has been established between cognitive function and hand dexterity in older adults. Declines in cognitive function have been shown to impair performance in finger tapping movements. Research suggest that hand training can improve dexterity, executive function, and cognitive function over time. This underscores the need for effective methods to improve hand and finger dexterity.

Method: In this study, we introduced a new hand training system that provides real-time feedback on finger movements during tapping tasks. We examined the system's impact on the finger dexterity of 32 healthy young participants by using a magnetic sensor finger tapping device (UB-2). During the finger tapping task, the participants performed opening and closing movements either in-phase or anti-phase on both left and right hands for 15 s. They were instructed to tap as quickly as possible. The number of taps, left-right balance, and other relevant data were measured using the UB-2 device.

Results: In terms of the number of tapping, a significant difference was found between 64.4 without feedback and 68.1 with feedback for the simultaneous opening and closing movements in the dominant hand. In the alternating open-close movement, the significant difference was 50.3 without feedback and 53.4 with feedback. The results showed that the system significantly improved the number and frequency of taps for both hands.

Conclusion: The improved tapping performance with feedback suggests that this system can improve hand dexterity.

Introduction: Enhancing medical robot training traditionally relies on explicit feedback from physicians to identify optimal and suboptimal robotic actions during surgery. Passive brain-computer interfaces (BCIs) offer an emerging alternative by enabling implicit brain-based performance evaluations. However, effectively decoding these evaluations of robot performance requires a comprehensive understanding of the spatiotemporal brain dynamics identifying optimal and suboptimal robot actions within realistic settings.

Methods: We conducted an electroencephalographic study with 16 participants who mentally assessed the quality of robotic actions while observing simulated robot-assisted laparoscopic surgery scenarios designed to approximate real-world conditions. We aimed to identify key spatiotemporal dynamics using the surface Laplacian technique and two complementary data-driven methods: a mass-univariate permutation-based clustering and multivariate pattern analysis (MVPA)-based temporal decoding. A second goal was to identify the optimal time interval of evoked brain signatures for single-trial classification.

Results: Our analyses revealed three distinct spatiotemporal brain dynamics differentiating the quality assessment of optimal vs. suboptimal robotic actions during video-based laparoscopic training observations. Specifically, an enhanced left fronto-temporal current source, consistent with P300, LPP, and P600 components, indicated heightened attentional allocation and sustained evaluation processes during suboptimal robot actions. Additionally, amplified current sinks in right frontal and mid-occipito-parietal regions suggested prediction-based processing and conflict detection, consistent with the oERN and interaction-based ERN/N400. Both mass-univariate clustering and MVPA provided convergent evidence supporting these neural distinctions.

Discussion: The identified neural signatures propose that suboptimal robotic actions elicit enhanced, sustained brain dynamics linked to continuous attention allocation, action monitoring, conflict detection, and ongoing evaluative processing. The findings highlight the importance of prioritizing late evaluative brain signatures in BCIs to classify robotic actions reliably. These insights have significant implications for advancing machine-learning-based training paradigms.

The U.S. Army Aeromedical Research Laboratory (USAARL) Multi-Attribute Task Battery (MATB) represents a significant advancement in research platforms for human performance assessment and automation studies. The USAARL MATB builds upon the legacy of the traditional MATB, which has been refined over 30 years of use to include four primary aviation-like tasks. However, the USAARL MATB takes this foundation and enhances it to meet the demands of contemporary research, particularly in the areas of performance modeling, cognitive workload assessment, adaptive automation, and trust in automation. The USAARL MATB retains the four classic subtask types from its predecessors while introducing innovations such as subtask variations, dynamic demand transitions, and performance-driven adaptive automation handoffs. This paper introduces the USAARL MATB to the research community, highlighting its development history, key features, and potential applications.

Introduction: First responders play a pivotal role in ensuring the wellbeing of individuals during critical situations. The demanding nature of their work exposes them to prolonged shifts and unpredictable situations, leading to elevated fatigue levels. Modern countermeasures to fatigue do not provide the best results. This study evaluates the acceptance and ethical considerations of a novel fatigue countermeasure using transcranial Direct Current Stimulation (tDCS) for fire and emergency medical services (EMS) personnel.

Methods: To better understand first responders' perceptions and ethical concerns about this novel fatigue countermeasure in their work, we conducted semi-structured interviews with first responders (N = 20). Interviews were transcribed into text and analyzed using qualitative content analysis.

Results: Over half of responders (59%) were interested, but over a third had a cautionary stand. Half of the participants seemed to have positive views regarding acceptability; a few were more cautionary or hesitant. A main area of consideration was user control (75%), with the majority wanting to retain some control over when or whether to accept the stimulation. Just above half of the participants (64%) mentioned privacy concerns. Another relevant consideration, raised by 50% of participants, was safety and the potential impact of stimulation (e.g., side effects, long-term effects). Overall, participants thought they needed to understand the system better and agreed that more education and training would be required to make people more willing to use it.

Discussion: Our exploration into combating fatigue among first responders through tDCS has revealed promising initial reactions from the responder community. Findings from this study lay the groundwork for a promising solution, while still in a nascent design stage, to improve the effectiveness and resilience of first responders in fatiguing shifts and critical situations.