Evaluation of Functional Brain Connectivity correlated with Power Spectrum Density during Mental Fatigue

• Mylena N. R. Ferreira SENAI Innovation Institute for Mineral Technologies
• Ana Carolina Q. Siravenha SENAI Innovation Institute for Mineral Technologies
• Iraquitan C. Filho Vale Institute of Technology
• Bruno D. Gomes Federal University of Para
• Schubert R. Carvalho Vale Institute of Technology

DOI: https://doi.org/10.5753/kdmile.2019.8787

Resumo

The progressive and gradual escalation of mental fatigue implies specific alterations in both power spectral values and brain connectivity. Detect these variations can improve the understanding of mental fatigue, and lead to identifying sensitive channels to mental state changes and also to identify brain connectivity profiles. This article aims to identify pairs of electrodes that could be used as indicators of mental fatigue based on highly correlated connectivity variations with power spectral values, and also high significance through both normal and fatigue states. In an effort to validate the selected pairs of channels, labeled and unlabeled datasets were subjected to statistical analysis. Our results indicate the concentration of channels in the anterior and posterior brain regions in the left hemisphere, and attenuation of brain connectivity.

Palavras-chave: Time Series Analysis, Brain Connectivity ,Mental Fatigue

Referências

Bastos, A. M. and Schoffelen, J. Tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Frontiers in Systems Neuroscience, 2016.

Borg, G. Psychophysical scaling with applications in physical work and the perception of exertion. Scand J Work Environ Health 16 (Suppl 1): 55–58, 1990.

Cajochen, C., Brunner, D. P., Krauchi, K., Graw, P., and Wirz-Justice, A. Power density in theta/alpha frequencies of the waking eeg progressively increases during sustained wakefulness. Sleep 18 (10): 890–894, 1995.

Charbonnier, S., Roy, R. N., Bonnet, S., and Campagne, A. Eeg index for control operatorsâAZ mental fatigue monitoring using interactions between brain regions. Expert Systems with Applications vol. 52, pp. 91–98, 2016.

Chennu, S., Annen, J., Wannez, S., Thibaut, A., Chatelle, C., Cassol, H., Martens, G., Schnakers, C., Gosseries, O., Menon, D., and Laureys, S. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain 140 (8): 2120–2132, 06, 2017.

Cohen, M. X. Analyzing neural time series data: theory and practice. MIT press, 2014.

Cohen, P., West, S. G., and Aiken, L. S. Applied multiple regression/correlation analysis for the behavioral sciences. Psychology Press, 2014.

Dasari, D., Shou, G., and Ding, L. Ica-derived eeg correlates to mental fatigue, effort, and workload in a realistically simulated air traffic control task. Frontiers in Neuroscience vol. 11, pp. 297, 2017.

Dimitrakopoulos, G. N., Kakkos, I., Dai, Z., Wang, H., Sgarbas, K., Thakor, N., Bezerianos, A., and SUN, Y. Functional connectivity analysis of mental fatigue reveals different network topological alterations between driving and vigilance tasks. IEEE Transactions on Neural Systems and Rehabilitation Engineering 4320 (c): 1–10, 2018.

Eoh, H. J., Chung, M. K., and Kim, S.-H. Electroencephalographic study of drowsiness in simulated driving with sleep deprivation. International Journal of Industrial Ergonomics 35 (4): 307–320, 2005.

Holm, A., Lukander, K., Korpela, J., Sallinen, M., and Müller, K. M. Estimating brain load from the eeg. The Scientific World Journal vol. 9, pp. 639–651, 2009.

Jung, T.-P., Makeig, S., Humphries, C., Lee, T.-W., Mckeown, M. J., Iragui, V., and Sejnowski, T. J. Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37 (2): 163–178, 2000.

Lee, K. A., Hicks, G., and Nino-Murcia, G. Validity and reliability of a scale to assess fatigue. Psychiatry research 36 (3): 291–298, 1991.

Liu, J., Zhang, C., and Zheng, C. Eeg-based estimation of mental fatigue by using kpca–hmm and complexity parameters. Biomedical Signal Processing and Control 5 (2): 124–130, 2010.

Min, J., Wang, P., and Hu, J. The original eeg data for driver fatigue detection, 2017.

Mitra, P. Observed brain dynamics. Oxford University Press, 2007.

Odom, J., Bach, M., Brigell, M., Holder, G., McCulloch, D., Tormene, A., and Vaegan. Iscev standard for clinical visual evoked potentials (2009 update). Documenta Ophthalmologica 120 (1): 111–119, 2010.

Oostenveld, R., Fries, P., Maris, E., and Schoffelen, J.-M. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Computational Intelligence and Neuroscience vol. 2011, 2011.

Schier, M. A. Changes in eeg alpha power during simulated driving: a demonstration. International Journal of Psychophysiology 37 (2): 155–162, 2000.

Sporns, O. Discovering the human connectome. MIT press, 2012.

Stoica, P., Moses, R. L., et al. Spectral analysis of signals, 2005.

Strimbu, K. and Tavel, J. a. What are Biomarkers? Curr Opin HIV AIDS 5 (6): 463–466, 2011.

Vinck, M., Oostenveld, R., van Wingerden, M., Battaglia, F., and Pennartz, C. M. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. NeuroImage 55 (4): 1548 – 1565, 2011.

Wascher, E., Rasch, B., Sunger, J., Hoffmann, S., Schneider, D., Rinkenauer, G., Heuer, H., and Gutberlet, I. Frontal theta activity reflects distinct aspects of mental fatigue. Biological Psychology 96 (1): 57–65, 2014.

Xu, R., Zhang, C., He, F., Zhao, X., Qi, H., Zhou, P., Zhang, L., and Ming, D. How physical activities affect mental fatigue based on eeg energy, connectivity, and complexity. Frontiers in neurology vol. 9, 2018.