Frontiers in neuroergonomics最新文献

Mental fatigue is a psychophysiological state that adversely impacts performance in cognitive tasks, increasing risk of occupational hazards. Given its manifestation as a conscious sensation, it is often measured through subjective self-report. However, subjective measures are not always true measurements of objective fatigue. In this study, we investigated the relationship between objective and subjective fatigue measurements with the preventive AccessPoint fatigue assay in Chilean mine workers. Subjective fatigue was measured through the Samn-Perelli scale, objective fatigue through a neurocognitive reaction time task. We found that objective and subjective fatigue do not correlate (-0.03 correlation coefficient, p < 0.001). Moreover, severe fatigue cases often displayed absence of subjective fatigue coupled with worse cognitive performance, a phenomenon we denominated Perceptual Blindness to fatigue. These findings highlight the need for objective fatigue measurements, particularly in high-risk occupational settings such as mining. Our results open new avenues for researching mechanisms underlying fatigue perception and its implications for occupational health and safety.

The present study investigated whether trait self-control impacted operators' behavior and associated neural resource strategies during a temporally irregular vigilance task. Functional near-infrared spectroscopy (fNIRS) readings of oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbR) from 29 participants were recorded fromthe prefrontal and parietal cortices. Self-control was associated with better perceptual sensitivity (A') in the task with the irregular event schedule. A left-lateralized effect of HbO2 was found for temporal irregularity within the dorsomedial prefrontal cortex, in accordance with functional transcranial doppler (fTCD) studies. Self-control increased HbR (decreasing activation) at right superior parietal lobule (rSPL; supporting vigilance utilization) and right inferior parietal lobule (rIPL; supporting resource reallocation). However, only rSPL was associated with the vigilance decrement-where decreases in activation led to better perceptual sensitivity in the temporally irregular task. Additionally, short stress-state measures suggest decreases in task engagement in individuals with higher self-control in the irregular task. The authors suggest a trait-state-brain-behavior relationship for self-control during difficult vigilance tasks. Implications for the study include steps toward rectifying the resource utilization vs. allocation debate in vigilance-as well as validating HbO2 and HbR as effective constructs for predicting operators' mental resources through fNIRS.

Introduction: 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.

Methods: 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.

Results: 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.

Discussion: 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.

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.