TY - GEN
T1 - Connectome pattern alterations with increment of mental fatigue in one-hour driving simulation
AU - Chua, Bing Liang
AU - Dai, Zhongxiang
AU - Thakor, Nitish
AU - Bezerianos, Anastasios
AU - Sun, Yu
N1 - Funding Information:
This work was supported by the National University of Singapore for Cognitive Engineering Group at Singapore Institute for Neurotechnology (SINAPSE) under Grant R-719-001-102-232. This work was also partially supported by the Ministry of Education of Singapore under the Grant MOE2014-T2-1-115. Asterisk indicates corresponding author.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/13
Y1 - 2017/9/13
N2 - The importance of understanding mental fatigue can be seen from many studies that started back in past decades. It is only until recent years has mental fatigue been explored through connectivity network analysis using graph theory. Although previous studies have revealed certain properties of the mental fatigue network via graph theory, some of these findings seemingly conflict with one another. The differences in findings could be due to mental fatigue being caused by various factors or being analyzed using different methods. So, in this study, to further understand the functional connectivity of driving fatigue, a weighted and undirected connectivity matrix would be constructed before applying graph theory to identify the biomarker from the network property. To obtain data for analysis, a 64-channel EEG cap was used to record the brain signals of subjects undergoing a one-hour driving simulation. Using the recorded EEG signal, a connectivity matrix was constructed using a synchronous method known as phase lag index (PLI) for the graph theory analysis. Results from this graph theory analysis showed that the synchronous network had increased clustering coefficient and decreased path length with the accumulation of mental fatigue. Furthermore, by calculating clustering coefficient regionally, its results revealed that the significant increase occurred mainly in the parietal and occipital regions of the brain.
AB - The importance of understanding mental fatigue can be seen from many studies that started back in past decades. It is only until recent years has mental fatigue been explored through connectivity network analysis using graph theory. Although previous studies have revealed certain properties of the mental fatigue network via graph theory, some of these findings seemingly conflict with one another. The differences in findings could be due to mental fatigue being caused by various factors or being analyzed using different methods. So, in this study, to further understand the functional connectivity of driving fatigue, a weighted and undirected connectivity matrix would be constructed before applying graph theory to identify the biomarker from the network property. To obtain data for analysis, a 64-channel EEG cap was used to record the brain signals of subjects undergoing a one-hour driving simulation. Using the recorded EEG signal, a connectivity matrix was constructed using a synchronous method known as phase lag index (PLI) for the graph theory analysis. Results from this graph theory analysis showed that the synchronous network had increased clustering coefficient and decreased path length with the accumulation of mental fatigue. Furthermore, by calculating clustering coefficient regionally, its results revealed that the significant increase occurred mainly in the parietal and occipital regions of the brain.
UR - http://www.scopus.com/inward/record.url?scp=85032196095&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032196095&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2017.8037820
DO - 10.1109/EMBC.2017.8037820
M3 - Conference contribution
C2 - 29060861
AN - SCOPUS:85032196095
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4355
EP - 4358
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -