Cognitive Response of Underground Car Driver Observed by Brain EEG Signals DOI Creative Commons
Yizhe Zhang,

Lunfeng Guo,

Xiusong You

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7763 - 7763

Published: Dec. 4, 2024

In auxiliary transportation within mines, accurately assessing the cognitive and response states of drivers is vital for ensuring safety operational efficiency. This study investigates effects various vehicle interaction stimuli on electroencephalography (EEG) signals mine transport drivers, analyzing under different conditions to evaluate their impact performance. Through experimental design, we simulate multiple scenarios encountered in real operations, including interactions with dynamic static vehicles, personnel, warning signs. EEG technology records brain during these scenarios, data analysis reveals changes responses stimuli. The results indicate significant variations involving signs, reflecting shifts drivers. Additionally, examines overall objects environments. detailed sheds light perception, attention, related which critical advancing sustainability mining operations.

Language: Английский

Understanding the Unexplored: A Review on the Gap in Human Factors Characterization for Industry 5.0 DOI Creative Commons

Alessia Ricci,

Vincenzo Ronca, Rossella Capotorto

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 1822 - 1822

Published: Feb. 11, 2025

The integration of neurophysiological techniques into Industry 5.0 represents a transformative approach to assessing human factors in real-world operational settings. This study presents systematic review existing literature evaluate the application methods cognitive and emotional states, such as workload, stress, attention, trust, within industrial environments. A total X peer-reviewed articles published between 2018 2024 were analyzed following structured methodology. findings reveal that EEG (45%), eye-tracking (30%), EDA (20%), ECG (15%) are most frequently adopted for monitoring responses. Additionally, 60% studies focused on stress workload assessment, while only 25% examined trust collaboration human–robot interaction, highlighting gap comprehensive teamwork analysis. Furthermore, 35% validated their approaches settings, emphasizing significant limitation ecological validity. also identifies multimodal remains underexplored, with just 15% combining multiple signals more holistic assessment. These results indicate growing but still fragmented research landscape, clear opportunities expanding applications, improving methodological standardization, fostering interdisciplinary collaboration. Future should prioritize validation dynamic, real-life work environments explore synergistic potential enhance human-centred systems.

Language: Английский

Citations

1
Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network DOI Creative Commons
Geqi Qi, Rui Liu, Wei Guan

et al.

Cyborg and Bionic Systems, Journal Year: 2024, Volume and Issue: 5

Published: Jan. 1, 2024

In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types distractions during driving. Driver distractions, such as cognitive processing and visual disruptions driving, lead to distinct alterations in electroencephalogram (EEG) signals extracted networks. We designed conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear nonlinear synchronization measures, were chosen construct network. By computing strengths topological features, explored potential relationship between configurations states driver distraction. Statistical analysis features indicates substantial differences normal states, suggesting reconfiguration under conditions. Different their combinations are fed into varied machine learning classifiers recognize states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other across all selected features. For individual networks, constructed using likelihood (SL) achieved highest accuracy distinguishing optimal feature set from 3 achieves 95.1% binary classification 88.3% ternary normal, cognitively distracted, visually proposed could accomplish may serve valuable tool further optimizing assistance systems with distraction control strategies, well reference future research on brain–computer interface autonomous

Language: Английский

Citations

6
How Immersed Are You? State of the Art of the Neurophysiological Characterization of Embodiment in Mixed Reality for Out-of-the-Lab Applications DOI Creative Commons
Vincenzo Ronca,

Alessia Ricci,

Rossella Capotorto

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(18), P. 8192 - 8192

Published: Sept. 12, 2024

Mixed Reality (MR) environments hold immense potential for inducing a sense of embodiment, where users feel like their bodies are present within the virtual space. This subjective experience has been traditionally assessed using reports and behavioral measures. However, neurophysiological approaches offer unique advantages in objectively characterizing embodiment. review article explores current state art utilizing techniques, particularly Electroencephalography (EEG), Photoplethysmography (PPG), Electrodermal activity (EDA), to investigate neural autonomic correlates embodiment MR out-of-the-lab applications. More specifically, it was investigated how EEG, with its high temporal resolution, PPG, EDA, can capture transient brain associated specific aspects such as visuomotor synchrony, visual feedback body, manipulations body parts. The signals differentiate between experiences discussed, particular regard identify markers early formation during exposure real settings. Finally, strengths limitations approach context research were order achieve more comprehensive understanding this multifaceted phenomenon.

Language: Английский

Citations

3
Cognitive load and task switching in drivers: Implications for road safety in semi-autonomous vehicles DOI Creative Commons
Jinhui Xu, Mohammad Fard, Neng Zhang

et al.

Transportation Research Part F Traffic Psychology and Behaviour, Journal Year: 2024, Volume and Issue: 107, P. 1175 - 1197

Published: Nov. 1, 2024

Language: Английский

Citations

3
Optimizing EEG Signal Integrity: A Comprehensive Guide to Ocular Artifact Correction DOI Creative Commons
Vincenzo Ronca, Rossella Capotorto, Gianluca Di Flumeri

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(10), P. 1018 - 1018

Published: Oct. 12, 2024

Ocular artifacts, including blinks and saccades, pose significant challenges in the analysis of electroencephalographic (EEG) data, often obscuring crucial neural signals. This tutorial provides a comprehensive guide to most effective methods for correcting these with focus on algorithms designed both laboratory real-world settings. We review traditional approaches, such as regression-based techniques Independent Component Analysis (ICA), alongside more advanced like Artifact Subspace Reconstruction (ASR) deep learning-based algorithms. Through detailed step-by-step instructions comparative analysis, this equips researchers tools necessary maintain integrity EEG ensuring accurate reliable results neurophysiological studies. The strategies discussed are particularly relevant wearable systems real-time applications, reflecting growing demand robust adaptable solutions applied neuroscience.

Language: Английский

Citations

2
o-CLEAN: a novel multi-stage algorithm for the ocular artifacts’ correction from EEG data in out-of-the-lab applications DOI Creative Commons
Vincenzo Ronca, Gianluca Di Flumeri, Andrea Giorgi

et al.

Journal of Neural Engineering, Journal Year: 2024, Volume and Issue: 21(5), P. 056023 - 056023

Published: Sept. 19, 2024

In the context of electroencephalographic (EEG) signal processing, artifacts generated by ocular movements, such as blinks, are significant confounding factors. These overwhelm informative EEG features and may occur too frequently to simply remove affected epochs without losing valuable data. Correcting these remains a challenge, particularly in out-of-lab online applications using wearable systems (i.e. with low number channels, any additional channels track EOG).

Language: Английский

Citations

1
Characterization of Cochlear Implant Artifact and Removal Based on Multi-Channel Wiener Filter in Unilateral Child Patients DOI Creative Commons
D. Rossi, Giulia Cartocci, Bianca Maria Serena Inguscio

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(8), P. 753 - 753

Published: July 24, 2024

Cochlear implants (CI) allow deaf patients to improve language perception and improving their emotional valence assessment. Electroencephalographic (EEG) measures were employed so far CI programming reliability evaluate listening effort in auditory tasks, which are particularly useful conditions when subjective evaluations scarcely appliable or reliable. Unfortunately, the presence of on scalp introduces an electrical artifact coupled EEG signals that masks physiological features recorded by electrodes close site implant. Currently, methods for removal have been developed very specific montages protocols, while others require many electrodes. In this study, we propose a method based Multi-channel Wiener filter (MWF) overcome those shortcomings. Nine children with unilateral nine age-matched normal hearing (control) participated study. data acquired relatively low number (n = 16) during resting condition task. The obtained results allowed characterize affected electrode significantly reduce, if not remove it through MWF filtering. Moreover, indicate, comparing two sample populations, loss is minimal users after filtering, maintain characteristics.

Language: Английский

Citations

0
Cognitive Response of Underground Car Driver Observed by Brain EEG Signals DOI Creative Commons
Yizhe Zhang,

Lunfeng Guo,

Xiusong You

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7763 - 7763

Published: Dec. 4, 2024

In auxiliary transportation within mines, accurately assessing the cognitive and response states of drivers is vital for ensuring safety operational efficiency. This study investigates effects various vehicle interaction stimuli on electroencephalography (EEG) signals mine transport drivers, analyzing under different conditions to evaluate their impact performance. Through experimental design, we simulate multiple scenarios encountered in real operations, including interactions with dynamic static vehicles, personnel, warning signs. EEG technology records brain during these scenarios, data analysis reveals changes responses stimuli. The results indicate significant variations involving signs, reflecting shifts drivers. Additionally, examines overall objects environments. detailed sheds light perception, attention, related which critical advancing sustainability mining operations.

Language: Английский

Citations

0