EEG Signal Processing Methods to Differentiate Mental States

Authors: Sun H. Hwang, Young E. Lee, Yunhan Ga, Gilwon Yoon

Abstract:

EEG is a very complex signal with noises and other bio-potential interferences. EOG is the most distinct interfering signal when EEG signals are measured and analyzed. It is very important how to process raw EEG signals in order to obtain useful information. In this study, the EEG signal processing techniques such as EOG filtering and outlier removal were examined to minimize unwanted EOG signals and other noises. The two different mental states of resting and focusing were examined through EEG analysis. A focused state was induced by letting subjects to watch a red dot on the white screen. EEG data for 32 healthy subjects were measured. EEG data after 60-Hz notch filtering were processed by a commercially available EOG filtering and our presented algorithm based on the removal of outliers. The ratio of beta wave to theta wave was used as a parameter for determining the degree of focusing. The results show that our algorithm was more appropriate than the existing EOG filtering.

Keywords: EEG, focus, mental state, outlier, signal processing.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339744

References:

[1] M. Tudor, L. Tudor, K.I. Tudor, “Hans Berger (1873-1941)-the history of electroencephalography”, Act Med Coratica, Vol. 59, No. 4, 307-13, 2005.
[2] Y.H.C. Yau, M.J. Crowley, L.C. Mayes, and M-.N. Potenza, “Are Internet use and video-game-playing addictive behaviors? Biological, clinical and public health implications for youths and adults”, Minerva Psichiatr, Vol.53, No.3, pp. 153–170, 2012.
[3] J. Lee, J. Hwang, S. Park, H. Jung, S. Choi, D. Kim, J. Lee, J. Choi, “Differential resting-state EEG patterns associated with comorbid depression in Internet addiction”, Progress in Neuro-Psychopharmacology & Biological Psychiatry, Vol 50, pp 21–26, 2014.
[4] J. Choi, S. Park, J. Lee, J. Hwang, H. Jung, S. Choi, D. Kim, S. Oh, J. Lee, “Resting-state beta and gamma activity in Internet addiction”, Int.J. of Psychophysiology, Vol 89, No 3, pp 328–333, 2013.
[5] W. Klimesch*, M. Doppelmayr, H. Russegger, T. Pachinger, J. Schwaiger, “Induced alpha band power changes in the human EEG and attention”, Neuroscience Letters, vol. 244, pp. 73–76, 1998.
[6] Y. Lee, S. Hwang, Y. Ga, G. Yoon, ‘Study on EEG response during computer game”, Vol 27, No. 1, pp. 101, The Korean Sensor Society Conference, Nov. 11 -12, Kyungbuk National University, 2016.
[7] L. I. Aftanas, S. A. Golocheikine, “Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention”, Neuroscience Letters, Vol. 310, Issue. 1, pp. 7–60, 2001.
[8] R. K. Feige, C. Fritsch, J. Timmer, C. H. Lucking, “Effects of attention and precision of exerted force on beta range EEG-EMG synchronization during a maintained motor contraction task”, Clinical Neurophysiology, vol. 113, pp. 124–131, 2002.
[9] J. Sisman, D. Campbell, and L. Brion, "Amplitude-Integrated EEG in Preterm Infants: Maturation of Background Pattern and Amplitude Voltage with Postmenstrual Age and Gestational Age", J.of Perinatology, vol. 25, pp. 391-396, 2005.
[10] P. Zarkowski, C. J. Shin, T. Dang, J. Russo, and D. Avery, "EEG and the Variance of Motor Evoked Potential Amplitude", Clinical EEG and Neuroscience 02006 vol.37, no. 3, pp. 247-251, 2006.
[11] N. A. Busch, and R. V. Rullen, “Spontaneous EEG oscillations reveal periodic sampling of visual attention”, Proceedings of the National Academy of Sciences of the United States of America, Vol. 107, No. 37, pp. 16048-16053, 2010
[12] J. Mast, J. D. Victor, "Fluctuations of steady-state VEPs: interaction of driven evoked potentials and the EEG", Electroencephalography and clinical Neurophysiology, vol.78, pp. 389-401, 1991.
[13] M. E. Brandt, B. H. Jansen, and J. P. Carbonari, "Pre-stimulus spectral EEG patterns and the visual evoked response", Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, Vol.80, Issue 1, pp. 16–20, 1991.
[14] T. C. Ferreea, P. Luua, G. S. Russella, and D. M. Tuckera, "Scalp electrode impedance, infection risk, and EEG data quality", Clinical Neurophysiology, vol. 112, no. 3, pp. 536–544, 2001.
[15] C.E.M. van Beijsterveldt, and D.I.Boomsma, "Genetics of the human electroencephalogram (EEG) and event-related brain potentials (ERPs): a review, " Human Genetics, Vol.94, Issue 4, pp. 319-330. 1994.
[16] T. Choi, J. Kim, S. Jin, G. Yoon, "Determination of the Concentrated State Using Multiple EEG Channels", Int.J of Computer, Information, Systems and Control Engineering, Vol. 8, No. 8, pp. 1215-1218, 2014.
[17] M. V. Marrufo, E. Vaquero, M. J. Cardoso, and C. M. Gomez, "Temporal evolution of α and β bands during visual spatial attention", Cognitive Brain Research, Vol. 12, Issue 2, pp. 315–320, 2001.
[18] T. Harmony, T. FemGndez, J. Silva, J. Bemal, L. D. Comas, A. Reyes, E. Marosi, M. Rodriguez, M. Rodriguez, “EEG delta activity an indicator of attention to internal processing”, Int. J. of Psychophysiology, Vol. 24, Issues 1–2, pp. 161–171, 1996.
[19] P. Sauseng, W. Klimesch, W. Stadler, M. Schabus, M. Doppelmayr, S. Hanslmayr, W. R. Gruber and N. Birbaumer, “A shift of visual spatialattention is selectively associated with human EEG alpha activity”, European J.of Neuroscience, vol. 22, Issue 11, pp. 2917–2926, 2005.
[20] Talairach, J, Tournoux, P, Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system: an approach to cerebral imaging, europsychologia, http://dx.doi.org/10.1111/mono.12083, 1988.