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Giannakopoulou O, Kakkos I, Dimitrakopoulos GN, Tarousi M, Sun Y, Bezerianos A, Koutsouris DD, Matsopoulos GK. Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics. SENSORS (BASEL, SWITZERLAND) 2024; 24:3894. [PMID: 38931678 PMCID: PMC11207888 DOI: 10.3390/s24123894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 06/05/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024]
Abstract
Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.
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Affiliation(s)
- Olympia Giannakopoulou
- Biomedical Engineering Laboratory, National Technical University of Athens, 15772 Athens, Greece; (O.G.); (D.D.K.); (G.K.M.)
| | - Ioannis Kakkos
- Biomedical Engineering Laboratory, National Technical University of Athens, 15772 Athens, Greece; (O.G.); (D.D.K.); (G.K.M.)
- Department of Biomedical Engineering, University of West Attica, 12243 Athens, Greece
| | | | - Marilena Tarousi
- Biomedical Engineering Laboratory, National Technical University of Athens, 15772 Athens, Greece; (O.G.); (D.D.K.); (G.K.M.)
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Anastasios Bezerianos
- Brain Dynamics Laboratory, Barrow Neurological Institute (BNI), St. Joseph’s Hospital and Medical Center, Phoenix, AZ 85013, USA;
| | - Dimitrios D. Koutsouris
- Biomedical Engineering Laboratory, National Technical University of Athens, 15772 Athens, Greece; (O.G.); (D.D.K.); (G.K.M.)
| | - George K. Matsopoulos
- Biomedical Engineering Laboratory, National Technical University of Athens, 15772 Athens, Greece; (O.G.); (D.D.K.); (G.K.M.)
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Huang KC, Tseng CY, Lin CT. EEG Information Transfer Changes in Different Daily Fatigue Levels During Drowsy Driving. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:180-190. [PMID: 38606398 PMCID: PMC11008798 DOI: 10.1109/ojemb.2024.3367496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/19/2024] [Accepted: 02/11/2024] [Indexed: 04/13/2024] Open
Abstract
A significant issue for traffic safety has been drowsy driving for decades. A number of studies have investigated the effects of acute fatigue on spectral power; and recent research has revealed that drowsy driving is associated with a variety of brain connections in a specific cortico-cortical pathway. In spite of this, it is still unclear how different brain regions are connected in drowsy driving at different levels of daily fatigue. This study identified the brain connectivity-behavior relationship among three different daily fatigue levels (low-, median- and high-fatigue) with the EEG data transfer entropy. According to the results, only low- and medium-fatigue groups demonstrated an inverted U-shaped change in connectivity from high performance to poor behavioral performance. In addition, from low- to high-fatigue groups, connectivity magnitude decreased in the frontal region and increased in the occipital region. These study results suggest that brain connectivity and driving behavior would be affected by different levels of daily fatigue.
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Affiliation(s)
- Kuan-Chih Huang
- Brain Science and Technology Center, Department of Electrical and Computer EngineeringNational Yang Ming Chiao Tung UniversityHsinchu300Taiwan
| | - Chun-Ying Tseng
- Brain Science and Technology CenterNational Yang Ming Chiao Tung UniversityHsinchu300Taiwan
| | - Chin-Teng Lin
- Australian Artificial Intelligence Institute, Faculty of Engineering and ITUniversity of Technology SydneySydneyNSW2007Australia
- Brain Science and Technology Center, Department of Electrical and Computer EngineeringNational Yang Ming Chiao Tung UniversityHsinchu300Taiwan
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Vigué-Guix I, Soto-Faraco S. Using occipital ⍺-bursts to modulate behavior in real-time. Cereb Cortex 2023; 33:9465-9477. [PMID: 37365814 DOI: 10.1093/cercor/bhad217] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/26/2023] [Accepted: 05/27/2023] [Indexed: 06/28/2023] Open
Abstract
Pre-stimulus endogenous neural activity can influence the processing of upcoming sensory input and subsequent behavioral reactions. Despite it is known that spontaneous oscillatory activity mostly appears in stochastic bursts, typical approaches based on trial averaging fail to capture this. We aimed at relating spontaneous oscillatory bursts in the alpha band (8-13 Hz) to visual detection behavior, via an electroencephalography-based brain-computer interface (BCI) that allowed for burst-triggered stimulus presentation in real-time. According to alpha theories, we hypothesized that visual targets presented during alpha-bursts should lead to slower responses and higher miss rates, whereas targets presented in the absence of bursts (low alpha activity) should lead to faster responses and higher false alarm rates. Our findings support the role of bursts of alpha oscillations in visual perception and exemplify how real-time BCI systems can be used as a test bench for brain-behavioral theories.
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Affiliation(s)
- Irene Vigué-Guix
- Center for Brain and Cognition, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona 08005, Spain
| | - Salvador Soto-Faraco
- Center for Brain and Cognition, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona 08005, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
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Chang YC, Wang YK, Pal NR, Lin CT. Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2464-2473. [PMID: 34748496 DOI: 10.1109/tnsre.2021.3126264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals. Then, an unsupervised clustering is conducted on the extracted feature space to identify the brain (external and covert) states that respond to different cognitive demands. To understand the human state change, a state transition diagram is introduced, allowing visualization of connectivity patterns between every pair of states. We compute the transition probability between every pair of states to represent the relationships between the states. This state transition diagram is named as the Fuzzy Covert State Transition Diagram (FCOSTD), which helps the understanding of human states and human performance. We then apply FCOSTD on distracted driving experiments. FCOSTD successfully discovers the external and covert states, faithfully reveals the transition of the brain between states, and the route of the state change when humans are distracted during a driving task. The experimental results demonstrate that different subjects have similar states and inter-state transition behaviour (establishing the consistency of the system) but different ways to allocate brain resources as different actions are being taken.
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Lin CT, Chuang CH, Hung YC, Fang CN, Wu D, Wang YK. A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:4959-4967. [PMID: 32816684 DOI: 10.1109/tcyb.2020.3010805] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant theta and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications.
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Freedman-Weiss MR, Heller DR, White EM, Chiu AS, Jean RA, Yoo PS. Driving Safety Among Surgical Residents in the Era of Duty Hour Restrictions. JOURNAL OF SURGICAL EDUCATION 2021; 78:770-776. [PMID: 32948507 DOI: 10.1016/j.jsurg.2020.08.041] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 08/18/2020] [Accepted: 08/23/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVE Fatigued driving is a known contributor to adverse motor vehicle events (AMVEs), defined as crashes and near misses. Surgical trainees work long and irregular hours; the safety of work-related driving since the introduction of work hour regulations has not yet been studied in this population. We aimed to assess the impact of fatigue on driving safety and explore perceptions of a funded rideshare program. DESIGN An electronic survey was delivered and inquired in retrospective fashion about fatigue and sleepiness while driving, occurrences of AMVEs, and projected use of a funded rideshare program as a potential solution to unsafe driving. Chi-square testing determined categorical differences between response choices. SETTING Yale University School of Medicine, Department of Surgery, New Haven, CT-a general surgery program with 4 urban clinical sites positioned along a roughly twenty mile stretch of interstate highway in Southeastern Connecticut. PARTICIPANTS General Surgery residents at the Yale University School of Medicine. RESULTS Of 58 respondents (81% response rate), 97% reported that fatigue compromised their safety while driving to or from work. Eighty-three percent reported falling nearly or completely asleep, and 22% reported AMVEs during work-related driving. Junior residents were more likely than Seniors to drive fatigued on a daily-to-weekly basis (69% vs 47%, p = 0.02) and twice as likely to fall asleep on a weekly-to-monthly basis (67% vs 33%, p = 0.02). Despite this, only 7% of residents had ever hired a ride service when fatigued, though 88%, would use a free rideshare service if provided. CONCLUSIONS Work-related fatigue impairs the driving safety of nearly all residents, contributing to frequent AMVEs. Currently, few residents hire rideshare services. Eliminating the cost barrier by funding a rideshare and encouraging its routine use may protect surgical trainees and other drivers.
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Affiliation(s)
| | - Danielle R Heller
- Department of Surgery, Yale University School of Medicine, New Haven, Connecticut
| | - Erin M White
- Department of Surgery, Yale University School of Medicine, New Haven, Connecticut
| | - Alexander S Chiu
- Department of Surgery, Yale University School of Medicine, New Haven, Connecticut
| | - Raymond A Jean
- Department of Surgery, Yale University School of Medicine, New Haven, Connecticut
| | - Peter S Yoo
- Department of Surgery, Yale University School of Medicine, New Haven, Connecticut.
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Huang KC, Chuang CH, Wang YK, Hsieh CY, King JT, Lin CT. The effects of different fatigue levels on brain-behavior relationships in driving. Brain Behav 2019; 9:e01379. [PMID: 31568699 PMCID: PMC6908862 DOI: 10.1002/brb3.1379] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 06/26/2019] [Accepted: 07/16/2019] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND In the past decade, fatigue has been regarded as one of the main factors impairing task performance and increasing behavioral lapses during driving, even leading to fatal car crashes. Although previous studies have explored the impact of acute fatigue through electroencephalography (EEG) signals, it is still unclear how different fatigue levels affect brain-behavior relationships. METHODS A longitudinal study was performed to investigate the brain dynamics and behavioral changes in individuals under different fatigue levels by a sustained attention task. This study used questionnaires in combination with actigraphy, a noninvasive means of monitoring human physiological activity cycles, to conduct longitudinal assessment and tracking of the objective and subjective fatigue levels of recruited participants. In this study, degrees of effectiveness score (fatigue rating) are divided into three levels (normal, reduced, and high risk) by the SAFTE fatigue model. RESULTS Results showed that those objective and subjective indicators were negatively correlated to behavioral performance. In addition, increased response times were accompanied by increased alpha and theta power in most brain regions, especially the posterior regions. In particular, the theta and alpha power dramatically increased in the high-fatigue (high-risk) group. Additionally, the alpha power of the occipital regions showed an inverted U-shaped change. CONCLUSION Our results help to explain the inconsistent findings among existing studies, which considered the effects of only acute fatigue on driving performance while ignoring different levels of resident fatigue, and potentially lead to practical and precise biomathematical models to better predict the performance of human operators.
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Affiliation(s)
- Kuan-Chih Huang
- Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan
| | - Chun-Hsiang Chuang
- Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan.,Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan
| | - Yu-Kai Wang
- CIBCI, Centre for Artificial Intelligence, FEIT, University of Technology Sydney, Sydney, NSW, Australia
| | - Chi-Yuan Hsieh
- Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan
| | - Jung-Tai King
- Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan
| | - Chin-Teng Lin
- Department of Electrical and Computer Engineering, National Chiao Tung University, Hsinchu, Taiwan.,Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan.,CIBCI, Centre for Artificial Intelligence, FEIT, University of Technology Sydney, Sydney, NSW, Australia
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