EEG ANALYSIS OF REAL AND IMAGINARY ARM MOVEMENTS BY SPECTRAL COHERENCE

Year 2021, Volume: 26 Issue: 1, 109 - 126, 30.04.2021

Nurhan Gürsel Özmen

https://doi.org/10.17482/uumfd.793775

Abstract

Revealing the information between similar patterns of brain for a real motor task and its imaginary equivalent can be means to clarify movement intentions and help to improve Brain Computer Interfaces (BCI)s. This paper uses spectral coherence to assess the functional interactions between neural regions engaged in a real and an imagined arm movement task. Magnitude squared coherence values were calculated for two specific bands of Electroencephalogram (EEG) that are 8–12 Hz alpha band and 13–20 Hz beta for 48 channels from selected regions of interest (ROIs). The coherence values are transferred into surface maps. We try to explain how motor cognition in these regions are relevant with the literature. The maximum coherence is observed between the channels in the same hemisphere and surrounding closest channels located vertically and horizontally based on the 10-20 electrode placement. Our results that the supplementary motor area, the premotor, prefrontal, primary motor cortex and the parietal cortex play a role in facilitating real and imaginary motor movements, are in good accordance with the previous studies. Further research can be put on spectral coherence patterns which would be a possible means for prosthetic-interactive BCI systems, interactive multimedia applications, and emerging EEGbased biometric recognition areas.

Keywords

EEG, Spectral coherence, BCI, Motor task classification, Real and imaginary arm movements

Project Number

-

Thanks

-

Gerçek ve Hayali Kol Hareketlerine ait EEG verilerinin Spektral Koherens Yöntemiyle Analizi

Abstract

Gerçek bir motor görev ve onun hayal edilmesi arasındaki benzer aktivasyon örüntülerinin tanımlanması, beyindeki hareket niyet noktalarının tespit edilmesine ve Beyin Bilgisayar Arayüzlerinin (BBA) geliştirilmesine yardımcı olabilir. Bu çalışmada, gerçek ve hayali bir kol hareketi görevi yapan nöral bölgeler arasındaki fonksiyonel etkileşimler, spektral koherens yöntemi ile değerlendirilmiştir. Çift kat güçlendirilmiş koherens değerleri, tipik alfa (8-12 Hz) ve beta (13-20 Hz) frekans bant aralığında seçilen ilişkili bölgedeki (İB) 48 farklı Elektroensefalogram (EEG) kanalı için hesaplanmıştır. Farklı görevler için hesaplanan koherens değerleri, yüzey haritalarına dönüştürülmüştür. Bu bölgelerdeki motor biliş anlayışımızın sağ ve sol kol ile ilgili literatürden elde edilen bulgularla nasıl ilişkili olduğu açıklanmaya çalışılmıştır. Aynı yarımküredeki kanallar ile 10-20 elektrot yerleşimi temelinde dikey ve yatay olarak yerleştirilmiş en yakın kanallar arasında maksimum tutarlılığın gözlendiği gösterilmiştir. Çalışmamızın sonuçları, literatürde yer alan tamamlayıcı motor alanının, premotor, prefrontal ve primer motor kortekslerinin ve parietal korteksin gerçek ve hayali motor hareketlerini kolaylaştırmada rol oynadığı bulguları ile uyumludur. Çalışmanın sonuçları, spektral tutarlılık modellerinin protez-etkileşimli BBA sistemleri, etkileşimli multimedya uygulamaları ve ortaya çıkan EEG tabanlı biyometrik tanıma alanları için olası bir araç olabileceğini göstermektedir. 

Keywords

EEG, Spektral koherens, BBA, Motor görev sınıflandırma, Gerçek ve hayali kol hareketleri

Project Number

-

References

• Akin, M. and Kiymik, M.K. (2000) Application of periodogram and AR spectral analysis to EEG signals. Journal of Medical Systems, 24,247–256. doi: 10.1023/a:1005553931564.
• Ames, K. C., and Churchland, M. M. (2019). Motor cortex signals for each arm are mixed across hemispheres and neurons yet partitioned within the population response. Elife, 8, e46159. doi:10.7554/eLife.46159
• Baccala, L.A. and Sameshima, K. (2001) Partial directed coherence: a new concept in neural structure determination. Biol. Cybern, 84, 463-474. doi: 10.1007/PL00007990
• Başar, E., Başar-Eroglu, C., Parnefjord, R., Rahn, E., & Schürmann, M. (1992). Evoked potentials: ensembles of brain induced rhythmicities in the alpha, theta and gamma ranges. In Induced rhythms in the brain Birkhäuser, Boston, MA. (155-181).
• Bezerianos, A., Stavrinou, M., Cimponeriu, L., & Moraru, L. (2007). Inferring brain connectivity subserving real and imagined movements from synchronization analysis. Int J Bioelectromagnet, 9(4), 205-13.
• Binkofski, F. et al. (2002). Neural activity in human primary motor cortex areas 4a and 4p is modulated differentially by attention to action. J Neurophysiol, 88, 514–519, doi: 10.1152/jn.00947.2001
• Birbaumer, N., Hinterberger, T., Kübler A., Neumann N. (2003) The thought-translation device (TTD): Neurobehavioral mechanisms and clinical outcome. IEEE Trans. Neural Syst. Rehabil. Eng., 11, 120–123. doi: 10.1109/TNSRE.2003.814439.
• Bundy D.T. et al., (2018) Unilateral, three-dimensional arm movement kinematics are encoded in ipsilateral human cortex,, The Journal of Neuroscience, 21,38(47),10042-10056 doi.org/ 10.1523/JNEUROSCI.0015-18.2018.
• Cheyne, D., Bakhtazad, L. & Gaetz, W. Spatiotemporal mapping of cortical activity accompanying voluntary movements using an event-related beamforming approach. Hum. Brain Mapp.,27, 213–229, doi: 10.1002/hbm.20178 (2006).
• Daly, I., Nasuto, S. J., Warwick, K., (2012) Brain computer interface control via functional connectivity dynamics, Pattern Recognition, 45, 2123–2136. doi.org/10.1016/j.patcog.2011.04.034.
• Dauwels J, Vialatte F, Musha T, Cichocki A (2010b) A comparative study of synchrony measures for the early diagnosis of Alzheimer’s disease based on EEG. Neuroimage, 49, 668–693. doi: 10.1016/j.neuroimage.2009.06.056
• Fu, R., Wang, H., Bao, T., & Han, M. (2020). EEG intentions recognition in dynamic complex object control task by functional brain networks and regularized discriminant analysis. Biomedical Signal Processing and Control, 61, 101998. doi: 10.1016/j.bspc.2020.101998
• Galambos, R., Makeig, S., Talmachoff, P. (1981), A 40 Hz auditory potential recorded from the human scalp, Proc Natl Acad Sci USA, 78 2643-2647. doi: 10.1073/pnas.78.4.2643
• Gao, Q., Duan, X., & Chen, H. (2011). Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality. Neuroimage, 54(2), 1280-1288. doi: 10.1016/j.neuroimage.2010.08.071.
• Gentili, R. Cahouet, V. Ballay, Y. Papaxanthis, C. (2004) Inertial properties of the arm are accurately predicted during motor imagery, Behav Brain Res, 155, 231-239. doi: 10.1016/j.bbr.2004.04.027
• Georgopoulos, A. P., Lurito, J. T., Petrides, M., Schwartz, A. B. & Massey, J. T. (1989). Mental rotation of the neuronal population vector. Science, 243, 234–236, doi: 10.1126/science.2911737.
• Güntekin, B., Başar, E. (2010). A new interpretation of P300 responses upon analysis of coherences. Cognitive neurodynamics, 4(2), 107-118. https://doi.org/10.1007/s11571-010-9106-0.
• Güntekin, B., Femir, B., Gölbaşı, B. T., Tülay, E., & Başar, E. (2017). Affective pictures processing is reflected by an increased long-distance EEG connectivity. Cognitive neurodynamics, 11(4), 355-367. https://dx.doi.org/10.1007/s11571-017-9439-z.
• Halliday DM, Rosenberg JR, Amjad AM, Breeze P, Conway BA, Farmer SF (1995) A framework for the analysis of mixed time series/point process data—theory and application to the study of physiological tremor, single motor unit discharges and electromyograms. Prog Biophys Mol Biol 64(2),237–278. doi:10.1016/s0079-6107(96)00009-0
• Holsheimer, J., & Feenstra, B. W. A. (1977). Volume conduction and EEG measurements within the brain: A quantitative approach to the influence of electrical spread on the linear relationship of activity measured at different locations. Electroencephalography and clinical neurophysiology, 43(1), 52-58. doi:10.1016/0013-4694(77)90194-8.
• Kilner, J. M., Paulignan, Y., Boussaoud, D. (2004). Functional connectivity during real vs imagined visuomotor tasks: an EEG study. Neuroreport, 15(4), 637-642. doi: 10.1097/00001756-200403220-00013
• La Rocca,D., Campisi, P., Vegso, B., Cserti, P., Kozmann, G., Babiloni, F., Fallani, F.D. (2014) Human brain distinctiveness based on EEG spectral coherence connectivity. IEEE transactions on Biomedical Engineering, 61(9), 2406-12. doi:10.1109/TBME.2014.2317881.
• Li, P., Liu, H., Si, Y., Li, C., Li, F., Zhu, X., ... & Xu, P. (2019). EEG based emotion recognition by combining functional connectivity network and local activations. IEEE Transactions on Biomedical Engineering, 66(10), 2869-2881. doi: 10.1109/tbme.2019.2897651.
• Lima, C. F., Krishnan, S., & Scott, S. K. (2016). Roles of supplementary motor areas in auditory processing and auditory imagery. Trends in neurosciences, 39(8), 527-542. doi:10.1016/j.tins.2016.06.003.
• Lopes da Silva, F. (2013). EEG and MEG: relevance to neuroscience. Neuron, 80, 1112–1128. doi: 10.1016/j.neuron.2013.10.017.
• Mamashli, F., Hämäläinen, M., Ahveninen, J. et al. (2019) Permutation Statistics for Connectivity Analysis between Regions of Interest in EEG and MEG Data. Sci Rep 9, 7942. https://doi.org/10.1038/s41598-019-44403-z
• Manganotti, P., Gerloff, C., Toro, C., Katsuta, H., Sadato, N., Zhuang, P. A., ... & Hallett, M. (1998). Task-related coherence and task-related spectral power changes during sequential finger movements. Electroencephalography and Clinical Neurophysiology / Electromyography and Motor Control, 109(1), 50-62.
• Matyas, F. et al. (2010). Motor control by sensory cortex. Science 330, 1240–1243, doi: 10.1126/science.1195797.
• Miller, K. J. et al. (2010). Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proc. Natl. Acad. Sci., 107, 4430–4435, doi: 10.1073/pnas.0913697107.
• Mühl C, Allison B, Nijholt A, Chanel G (2014) A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges, Brain-Computer Interfaces, 1(2), 66-84, doi: 10.1080/2326263X.2014.912881.
• Neuper, C., Müller-Putz, G.R., Scherer, R., Pfurtscheller, G. (2006) Motor imagery and EEG-based control of spelling devices and neuroprostheses. Prog. Brain Res. 159, 393–409. doi: 10.1016/S0079-6123.
• Nicolelis, M. A. (2003). Brain-machine interfaces to restore motor function and probe neural circuits. Nat. Rev. Neurosci., 4, 417–422, doi:10.1038/nrn1105.
• Nunez, P.L, Wingeier, B.M, Silberstein, R.B. (2001) Spatial-temporal structures of human alpha rhythms: theory, microcurrent sources, multiscale measurements, and global binding of local networks. Hum Brain Mapp, 13,125–164. doi: 10.1002/hbm.1030.
• Nunez, P.L, Srinivasan, R., Westdorp, A.F., Wijesinghe, R.S., Tucker, D.M., Silberstein, R.B, Cadusch, P.J. (1997) EEG coherency: I: statistics, reference electrode, volume conduction, laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalogr Clin Neurophysiol, 103(5),499–515. doi:10.1016/S0013-4694(97)00066-7.
• Ozel, P., Akan, A., & Yilmaz, B. (2019). Synchrosqueezing transform based feature extraction from EEG signals for emotional state prediction. Biomedical Signal Processing and Control, 52, 152-161. doi: 10.1016/j.bspc.2019.04.023.
• Ozmen, N.G, Gumusel, L. (2013) Classification of real and imaginary hand movements for a BCI design. 36th IEEE International Conference on Telecommunications and Signal Processing (TSP), Rome, (607-611). ISBN 978-1-4799-0402-0.
• Ozmen, N.G, Gumusel L, Yang Y. (2018) A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification. Computational and Mathematical Methods in Medicine. 2018, 1-10. doi:10.1155/2018/9890132
• Pereda, E., Quiroga, R.Q., Bhattacharya, J. (2005) Nonlinear multivariate analysis of neurophysiological signals. Prog Neurobiol ,77,1–37. doi: 10.1016/j.pneurobio.2005.10.003
• Personnier, P., Ballay, Y., Papaxanthis, C. (2010). Mentally represented motor actions in normal aging: III. Electromyographic features of imagined arm movements. Behavioural brain research, 206(2), 184-191. doi.org/10.1016/j.bbr.2009.09.011.
• Sannita, W. G., Lopez, L., Piras, C., & Di Bon, G. (1995). Scalp-recorded oscillatory potentials evoked by transient pattern-reversal visual stimulation in man. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, 96(3), 206-218. doi: 10.1016/0168-5597(94)00285-m.
• Sasai, S., Koike, T., Sugawara, S. K., Hamano, Y. H., Sumiya, M., Okazaki, S., ... & Sadato, N. (2020). Frequency-specific task modulation of human brain functional networks: A fast fMRI study. NeuroImage, 224, 117375. DOI: 10.1016/j.neuroimage.2020.117375.
• Seeber, M., Cantonas, L., Hoevels, M. et al. Subcortical electrophysiological activity is detectable with high-density EEG source imaging. Nat Commun, 10, 753 (2019). doi:10.1038/s41467-019-08725-w.
• Seleznov, I., Zyma, I., Kiyono, K., Tukaev, S., Popov, A., Chernykh, M, and Shpenkov, O. (2019) Detrended Fluctuation, Coherence, and Spectral Power Analysis of Activation Rearrangement in EEG Dynamics During Cognitive Workload. Front. Hum. Neurosci. 13, 270. doi: 10.3389/fnhum.2019.00270
• Sleight, J., Pillai, P., & Mohan, S. (2009). Classification of executed and imagined motor movement EEG signals. Ann Arbor, University of Michigan, 1-10.
• Sugata, H., Hirata, M., Yanagisawa, T., Matsushita, K., Yorifuji, S., & Yoshimine, T. (2016). Common neural correlates of real and imagined movements contributing to the performance of brain–machine interfaces. Scientific reports, 6, 24663. doi: 10.1038/srep24663.
• Tang, Z., Yu, H., Lu, C., Liu, P., & Jin, X. (2019). Single-Trial Classification of Different Movements on One Arm Based on ERD/ERS and Corticomuscular Coherence. IEEE Access, 7, 128185-128197.
• Tzelepi, A. Bezerianos, T. Bodis-Wollner, I. (2000) Functional properties of sub-bands of oscillatory brain waves to pattern visual stimulation in man., Clinical Neurophysiology, 111(2), 259-269. doi:10.1016/S1388-2457(99)00248-5.
• URL 1: https://brainconnection.brainhq.com/2013/03/05/the-anatomy-of-movement/ (Accessed in:17.07.2020)
• URL 2: http://www.soft-dynamics.com (Accessed in: 17.07.2020).
• Wang, X. W., Nie, D., & Lu, B. L. (2014). Emotional state classification from EEG data using machine learning approach. Neurocomputing, 129, 94-106. doi.org/10.1016/j.neucom.2013.06.046.
• Wang, R., Wang, J., Yu, H., Wei, X., Yang, C., & Deng, B. (2015). Power spectral density and coherence analysis of Alzheimer’s EEG. Cognitive neurodynamics, 9(3), 291-304. doi: 10.1007/s11571-014-9325-x.
• Wolpaw, J.R. Birbaumer N., Heetderks W.J., et al, ,(2000) Brain–computer interface technology: A review of the first international meeting. IEEE Trans. Rehab. Eng., 8(2),164-173. doi: 10.1109/tre.2000.847807.
• Yang, L., Lu, Y. (2018) EEG Neural Correlates of Self-Paced Left- and Right-Hand Movement Intention during a Reaching Task, Annual International Conference of the IEEE Engineering in Medicine and Biology Society Conference Proceedings. doi:10.1109/EMBC.2018.8512725.
• Yong X, Menon C., (2015) EEG Classification of Different Imaginary Movements within the Same Limb. PLoS ONE 10(4): e0121896. doi:10.1371/journal.pone.0121896
• Zhang, Y., and Zhao, Y. (2013). Real and imaginary modulation spectral subtraction for speech enhancement. Speech Communication, 55(4), 509-522. doi: 10.1016/j.specom.2012.09.005.