Evaluation of Phase Synchronization Approach using Phase Locking Value in Color Discrimination Task

Davoodi, Saeideh; Daliri, Mohammad Reza

doi:10.22041/ijbme.2018.78557.1312

Document Type : Full Research Paper

Authors

¹ MSc. Student, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

² Associate Professor, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

https://doi.org/10.22041/ijbme.2018.78557.1312

Abstract

Variety of brain region function represent that interactions between different frequency bands, employ general mechanisms of neural communications. Moreover, a method which recently used for information encoding in the brain is phase synchronization that is a process by which two or more cyclic signals tends to oscillate with a repeating sequence of relative phase angle. Some evidence demonstrated the important role of phase synchronization in cognitive tasks. In this paper we investigated the role of phase synchronization in a new visual discrimination task. For this purpose we collected electroencephalography signals from fifteen subjects during a color discrimination task. The machine learning algorithm, support vector machine (SVM), was used to find out whether this criterion can distinguish two different colors in the mentioned task. The results show that classification accuracy of 75% is achieved using phase synchronization feature. Also efficient frequency bands and contribution of effective electrodes were shown.

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

References

[1] Canolty, R.T. and R.T. Knight, The functional role of cross-frequency coupling. Trends in cognitive sciences, 2010. 14(11): p. 506-515.

[2] Behroozi, M., M.R. Daliri, and B. Shekarchi, EEG phase patterns reflect the representation of semantic categories of objects. Medical & biological engineering & computing, 2016. 54(1): p. 205-221.

[3] Sauseng, P., et al., Cross-frequency phase synchronization: a brain mechanism of memory matching and attention. Neuroimage, 2008. 40(1): p. 308-317.

[4] Rosenblum, M.G., A.S. Pikovsky, and J. Kurths, Phase synchronization of chaotic oscillators. Physical review letters, 1996. 76(11): p. 1804.

[5] Carrasco, M., Visual attention: The past 25 years. Vision research, 2011. 51(13): p. 1484-1525.

[6] Doesburg, S.M., et al., Large-scale gamma-band phase synchronization and selective attention. Cerebral cortex, 2007. 18(2): p. 386-396.

[7] Klimesch, W., et al., A short review of slow phase synchronization and memory: evidence for control processes in different memory systems? Brain research, 2008. 1235: p. 31-44.

[8] Fell, J. and N. Axmacher, The role of phase synchronization in memory processes. Nature reviews neuroscience, 2011. 12(2): p. 105.

[9] Fries, P., et al., Modulation of oscillatory neuronal synchronization by selective visual attention. Science, 2001. 291(5508): p. 1560-1563.

[10] Ward, L.M., Synchronous neural oscillations and cognitive processes. Trends in cognitive sciences, 2003. 7(12): p. 553-559.

[11] Lachaux, J.-P., et al., Measuring phase synchrony in brain signals. Human brain mapping, 1999. 8(4): p. 194-208.

[12] Voytek, B. and R.T. Knight, Prefrontal cortex and basal ganglia contributions to visual working memory. Proceedings of the National Academy of Sciences, 2010. 107(42): p. 18167-18172.

[13] Repovs, G. Dealing with noise in EEG recording and data analysis. in Informatica Medica Slovenica. 2010.

[14] Jung, T.-P., et al. Removing electroencephalographic artifacts: comparison between ICA and PCA. in Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop. 1998. IEEE.

[15] Guyon, I. and A. Elisseeff, An introduction to feature extraction. Feature extraction, 2006: p. 1-25.

[16] Jafakesh, S., F.Z. Jahromy, and M.R. Daliri, Decoding of object categories from brain signals using cross frequency coupling methods. Biomedical Signal Processing and Control, 2016. 27: p. 60-67.

[17] Jirsa, V. and V. Müller, Cross-frequency coupling in real and virtual brain networks. Frontiers in computational neuroscience, 2013. 7.

[18] Penny, W., et al., Testing for nested oscillation. Journal of neuroscience methods, 2008. 174(1): p. 50-61.

[19] Papoulis, A., Random Variables and Stochastic Processes. 1985, McGraw-Hill, New York.

[20] Bermingham, M.L., et al., Application of high-dimensional feature selection: evaluation for genomic prediction in man. Scientific reports, 2015. 5.

[21] Guyon, I. and A. Elisseeff, An introduction to variable and feature selection. Journal of machine learning research, 2003. 3(Mar): p. 1157-1182.

[22] James, G., et al., An introduction to statistical learning. Vol. 112. 2013: Springer.

[23] Cortes, C. and V. Vapnik, Support-vector networks. Machine learning, 1995. 20(3): p. 273-297.

Iranian Journal of Biomedical Engineering

Evaluation of Phase Synchronization Approach using Phase Locking Value in Color Discrimination Task

References

References

Volume 11, Issue 3
September 2017
Pages 265-273

Evaluation of Phase Synchronization Approach using Phase Locking Value in Color Discrimination Task

References

References

Volume 11, Issue 3September 2017Pages 265-273

Volume 11, Issue 3
September 2017
Pages 265-273