Abstract
The functional activities of the brain during any task like imaginary, motor, or cognitive are different in pattern as well as their area of activation in the brain is also different. This variation in pattern is also found in the brain’s electrical variations that can be measured from the scalp of the brain using an electroencephalogram (EEG). This work exclusively studied a group of subjects’ EEG data (available at: https://archive.physionet.org/physiobank/database/eegmat/) to unravel the activation pattern of the human brain during a mental arithmetic task. Since any cognitive task creates variations in EEG signal pattern, the relative changes in the signal power also occur which is also known as event-related desynchronization/synchronization (ERD/ERS). In this work, ERD/ERS have calculated the band-wise power spectral density (PSD) using Welch’s method from the EEG signals. Besides, the coherence analysis was also performed to verify the results of ERD/ERS analysis from several randomly chosen subjects’ EEG data. Here, subjects performing mental arithmetic tasks were grouped based on their performances: good (subtraction solved > 10 on average) and bad (subtraction solved ≤ 10 on average) to conduct group-specific ERD/ERS analysis regarding their performance in cognitive tasks. It was found that when the brain is on count condition, the variations found in the band power of theta and beta. The amounts of ERS in the left hemisphere are increased. When the task complexity increases, it contributes to an increase in relative ERD/ERS amounts and durations. The left and right hemispheres were asymmetrically distributed by both the pre-stimulus stages of the corresponding band power and relative ERD/ERS.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
In this work, the main analysis is done on the dataset Physiobank: https://archive.physionet.org/physiobank/database/eegmat/. The outcomes of our research work as Figures, Tables, and other graphs can be available upon request to the corresponding author.
References
Aftanas L, Golosheykin S (2005) Impact of regular meditation practice on EEG activity at rest and during evoked negative emotions. Int J Neurosci 115:893–909. https://doi.org/10.1080/00207450590897969
Asada H, Fukuda Y, Tsunoda S et al (1999) Frontal midline theta rhythms reflect alternative activation of prefrontal cortex and anterior cingulate cortex in humans. Neurosci Lett 274:29–32. https://doi.org/10.1016/S0304-3940(99)00679-5
Başar E (2011) Brain-body-mind in the nebulous cartesian system: A holistic approach by oscillations
Başar E, Başar-Eroglu C, Karakaş S, Schürmann M (2001) Gamma, alpha, delta, and theta oscillations govern cognitive processes. Int J Psychophysiol 39:241–248. https://doi.org/10.1016/S0167-8760(00)00145-8
Başar E, Schürmann M, Sakowitz O (2001) The selectively distributed theta system: functions. Inter J Psychophysiol 39:197–212. https://doi.org/10.1016/S0167-8760(00)00141-0
Bastiaansen M, Hagoort P (2003) Event-induced theta responses as a window on the dynamics of memory. Cortex 39:967–992. https://doi.org/10.1016/S0010-9452(08)70873-6
Bastiaansen MCM, Oostenveld R, Jensen O, Hagoort P (2008) I see what you mean: theta power increases are involved in the retrieval of lexical semantic information. Brain Lang 106:15–28. https://doi.org/10.1016/j.bandl.2007.10.006
Burbaud P, Degreze P, Lafon P et al (1995) Lateralization of prefrontal activation during internal mental calculation: a functional magnetic resonance imaging study. J Neurophysiol 74:2194–2200. https://doi.org/10.1152/jn.1995.74.5.2194
Cao Z, Yin Z, Zhang J (2021) Recognition of cognitive load with a stacking network ensemble of denoising autoencoders and abstracted neurophysiological features. Cogn Neurodyn. https://doi.org/10.1007/s11571-020-09642-1
Choi SI, Hwang HJ (2019) Effects of different re-referencing methods on spontaneously generated Ear-EEG. Front Neurosci. https://doi.org/10.3389/fnins.2019.00822
Clarke AR, Barry RJ, Karamacoska D, Johnstone SJ (2019) The EEG Theta/Beta Ratio: A marker of Arousal or Cognitive Processing Capacity? Appl Psychophysiol Biofeedback 44(2):123–129. https://doi.org/10.1007/s10484-018-09428-6
Das A, Menon V (2022) Causal dynamics and information flow in parietal-temporal-hippocampal circuits during mental arithmetic revealed by high-temporal resolution human intracranial EEG. Cortex (elsevier) 147:24–40. https://doi.org/10.1016/j.cortex.2021.11
De Smedt B, Grabner RH, Studer B (2009) Oscillatory EEG correlates of arithmetic strategy use in addition and subtraction. Exp Brain Res 195(4):635–642. https://doi.org/10.1007/s00221-009-1839-9
Defebvre L, Derambure P, Bourriez JL et al (1993) Spatiotemporal study of event-related desynchronization in idiopathic Parkinson’s disease. Adv Neurol 60:422–428
Defebvre L, Bourriez JL, Dujardin K et al (1994) Spatiotemporal study of bereitschaftspotential and Event-Related Desynchronization during voluntary movement in Parkinson’s disease. Brain Topogr 6:237–244. https://doi.org/10.1007/BF01187715
Defebvre L, Bourriez JL, Destee A, Guieu JD (1996) Movement related desynchronisation pattern preceding voluntary movement in untreated Parkinson’s disease. Neurosurg, Psych 60:307–312. https://doi.org/10.1136/jnnp.60.3.307
Demiralp T, Başar E (1992) Theta rhythmicities following expected visual and auditory targets. Int J Psychophysiol 13:147–160. https://doi.org/10.1016/0167-8760(92)90054-F
Derambure P, Bourriez JL, Defebvre L et al (1997) Abnormal cortical activation during planning of voluntary movement in patients with epilepsy with focal motor seizures: event-related desynchronization study of electroencephalographic mu rhythm. Epilepsia 38:655–662. https://doi.org/10.1111/j.1528-1157.1997.tb01234.x
Diez OB, Pichler-Zalaudek K et al (1999) Ereignisbezogene EEG-desynchronisation und synchronisation (ERD und ERS) bei idiopathischem Parkinson-syndrom. Klin Neurophysiol 30:15–21. https://doi.org/10.1055/s-2008-1060081
Dimitriadis SI, Sun Y, Thakor N V., Bezerianos A (2016) Causal interactions between frontalθ-parieto-occipitalα2 predict performance on a mental arithmetic task. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2016.00454
Duru AD, Assem M (2018) Investigating neural efficiency of elite karate athletes during a mental arithmetic task using EEG. Cogn Neurodyn 12:95–102. https://doi.org/10.1007/s11571-017-9464-y
Enriquez-Geppert S, Huster RJ, Figge C, Herrmann CS (2014) Self-regulation of frontal-midline theta facilitates memory updating and mental set shifting. Front Behav Neurosci 8:1–13. https://doi.org/10.3389/fnbeh.2014.00420
Ergün E, Aydemir O (2020) A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals. Cogni Neurodyn (springer) 14(5):609–617. https://doi.org/10.1007/s11571-020-09592-8
Fehr T, Milz P (2019) The individuality index: a measure to quantify the degree of inter-individual, spatial variability in intra-cerebral brain electric and metabolic activity. Cogn Neurodyn. https://doi.org/10.1007/s11571-019-09538-9
Feige B, Scheffler K, Esposito F et al (2005) Cortical and subcortical correlates of electroencephalographic alpha rhythm modulation. J Neurophysiol 93:2864–2872. https://doi.org/10.1152/jn.00721.2004
Finlay MC, Lambiase PD, Ben-Simon R, Taggart P (2016) Effect of mental stress on dynamic electrophysiological properties of the endocardium and epicardium in humans. Heart Rhythm 13:175–182. https://doi.org/10.1016/j.hrthm.2015.08.011
Gevins A, Smith ME (2000) Neurophysiological measures of working memory and individual differences in cognitive ability and cognitive style. Cereb Cortex 10:829–839. https://doi.org/10.1093/cercor/10.9.829
Goldberger AL, Amaral LA, Glass L, et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 10.1161/01.cir.101.23.e215
Gómez CM, Vázquez M, Vaquero E et al (1998) Frequency analysis of the EEG during spatial selective attention. Int J Neurosci 95:17–32. https://doi.org/10.3109/00207459809000646
González-Garrido AA, Gómez-Velázquez FR, Salido-Ruiz RA et al (2018) The analysis of EEG coherence reflects middle childhood differences in mathematical achievement. Brain Cogn 124:57–63. https://doi.org/10.1016/j.bandc.2018.04.006
Grabner RH, De Smedt B (2011) Neurophysiological evidence for the validity of verbal strategy reports in mental arithmetic. Biol Psychol 87:128–136. https://doi.org/10.1016/j.biopsycho.2011.02.019
Gundel A, Wilson GF (1992) Topographical changes in the ongoing EEG related to the difficulty of mental tasks. Brain Topogr 5:17–25. https://doi.org/10.1007/BF01129966
Hidasi Z, Czigler B, Salacz P et al (2007) Changes of EEG spectra and coherence following performance in a cognitive task in Alzheimer’s disease. Int J Psychophysiol 65:252–260. https://doi.org/10.1016/j.ijpsycho.2007.05.002
Hossain SA, Rahman MA, Chakrabarty A, Rashid MA, Kuwana A, Kobayashi H (2023) Emotional state classification from MUSIC-based features of multichannel EEG signals. Bioengineering (MDPI). https://doi.org/10.3390/bioengineering10010099
Inouye T, Shinosaki K, Iyama A, Matsumoto Y (1993) Localization of activated areas and directional EEG patterns during mental arithmetic. Electroencephalogr Clin Neurophysiol 86(4):224–230. https://doi.org/10.1016/0013-4694(93)90102-2
Jatoi NA, Kyvelou SM, Feely J (2014) The acute effects of mental arithmetic, cold pressor and maximal voluntary contraction on arterial stiffness in young healthy subjects. Artery Res 8:44–50. https://doi.org/10.1016/j.artres.2014.02.002
Kalcher J, Pfurtscheller G (1995) Discrimination between phase-locked and non-phase-locked event-related EEG activity. Electroencephalogr Clin Neurophysiol 94:381–384. https://doi.org/10.1016/0013-4694(95)00040-6
Katahira K, Yamazaki Y, Yamaoka C, Ozaki H, Nakagawa S, Nagata N (2018) EEG correlates of the flow state: a combination of increased frontal theta and moderate frontocentral alpha rhythm in the mental arithmetic task. Front Psychol 9(300):1–11. https://doi.org/10.3389/fpsyg.2018.00300
Khanam F, Hossain ABMA, Ahmad M (2023) Electroencephalogram-based cognitive load level classification using wavelet decomposition and support vector machine. Brain-Comput Interfaces (taylor & Francis Online) 10(1):1–15. https://doi.org/10.1080/2326263X.2022.2109855
Kissler J, Müller MM, Fehr T et al (2000) MEG gamma band activity in schizophrenia patients and healthy subjects in a mental arithmetic task and at rest. Clin Neurophysiol 111:2079–2087. https://doi.org/10.1016/S1388-2457(00)00425-9
Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29:169–195
Kortelainen J, Seppanen T (2013) EEG-based recognition of video-induced emotions: Selecting subject-independent feature set. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS, pp 4287–4290
Kortelainen J, Väyrynen E, Seppänen T (2015) High-frequency electroencephalographic activity in left temporal area is associated with pleasant emotion induced by video clips. hindawi.com. https://doi.org/10.1155/2015/762769
Kukleta M, Brázdil M, Roman R, Jurák P (2003) Identical event-related potentials to target and frequent stimuli of visual oddball task recorded by intracerebral electrodes. Clin Neurophysiol 114:1292–1297. https://doi.org/10.1016/S1388-2457(03)00108-1
Li M, Member BLS (2009) 05334139.Pdf.ieeexplore.ieee.org, pp 1323–1326
Lin YP, Wang CH, Jung TP et al (2010) EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng 57:1798–1806. https://doi.org/10.1109/TBME.2010.2048568
Magnani G, Cursi M, Leocani L et al (1998) Event-related desynchronization to contingent negative variation and self-paced movement paradigms in Parkinson’s disease. Mov Disord 13:653–660. https://doi.org/10.1002/mds.870130408
Mason MF, Norton MI, Van Horn JD et al (2007) Wandering minds: the default network and stimulus-independent thought. Science 315:393–395. https://doi.org/10.1126/science.1131295
Menon V, Rivera SM, White CD, et al (2000) Dissociating prefrontal and parietal cortex activation during arithmetic processing. riveralab.ucdavis.edu. https://doi.org/10.1006/nimg.2000.0613
Molnár M, Csuhaj R, Csikós D et al (2005) Psychophysiologic and clinical aspects of EEG synchronization related to cognitive processes. Ideggyogy Sz 58:393–401
Noto Y, Sato T, Kudo M et al (2005) The relationship between salivary biomarkers and state-trait anxiety inventory score under mental arithmetic stress: a pilot study. Anesth Analg 101:1873–1876. https://doi.org/10.1213/01.ANE.0000184196.60838.8D
Osipova D, Takashima A, Oostenveld R et al (2006) Theta and gamma oscillations predict encoding and retrieval of declarative memory. J Neurosci 26:7523–7531. https://doi.org/10.1523/JNEUROSCI.1948-06.2006
Özgören M, Başar-Eroǧlu C, Başar E (2005) Beta oscillations in face recognition. Int J Psychophysiol 55(1):51–59. https://doi.org/10.1016/j.ijpsycho.2004.06.005
Pfurtscheller G, Lopes Da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clin Neurophysiol 110:1842–1857
Pfurtscheller G, Sager W, Wege W (1981) Correlations between CT scan and sensorimotor EEG rhythms in patients with cerebrovascular disorders. Electroencephalogr Clin Neurophysiol 52:473–485. https://doi.org/10.1016/0013-4694(81)90032-8
Pulvermüller F, Birbaumer N, Lutzenberger W, Mohr B (1997) High-frequency brain activity: its possible role in attention, perception and language processing. Prog Neurobiol 52:427–445
Rahman MA, Hossain MF, Hossain M, Ahmmed R (2020) Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal. Egypt Informat J (elsevier) 21(1):23–35. https://doi.org/10.1016/j.eij.2019.10.002
Razumnikova O, Razoumnikova OM (2000) Functional organization of different brain areas during convergent and divergent thinking: an EEG investigation. Cogn Brain Res 10:11–18. https://doi.org/10.1016/S0926-6410(00)00017-3
Rodriguez R, Kallenbach U, Singer W, Munk MHJ (2004) Short- and long-term effects of cholinergic modulation on gamma oscillations and response synchronization in the visual cortex. J Neurosci 24:10369–10378. https://doi.org/10.1523/JNEUROSCI.1839-04.2004
Röhm D, Klimesch W, Haider H, Doppelmayr M (2001) The role of theta and alpha oscillations for language comprehension in the human electroencephalogram. Neurosci Lett 310:137–140. https://doi.org/10.1016/S0304-3940(01)02106-1
Sammer G, Blecker C, Gebhardt H, Bischoff M, Stark R, Morgen K, Vaitl D (2007) Relationship between regional hemodynamic activity and simultaneously recorded EEG-theta associated with mental arithmetic-induced workload. Hum Brain Mapp 28(8):793–803. https://doi.org/10.1002/hbm.20309
Schmidt H, Avitabile D, Montbrió E, Roxin A (2018) Network mechanisms underlying the role of oscillations in cognitive tasks. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1006430
Schomer DL, da Silva FHL (2012) Niedermeyer’s electroencephalography: basic principles, clinical applications, and related fields, 6th edn. Wolters Kluwer Health Adis (ESP)
Seleznov I, Zyma I, Kiyono K, et al (2019) Detrended fluctuation, coherence, and spectral power analysis of activation rearrangement in EEG dynamics during cognitive workload. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2019.00270
Soltanlou M, Artemenko C, Dresler T, Fallgatter AJ, Nuerk HC, Ehlis AC (2019) Oscillatory EEG changes during arithmetic learning in children. Dev Neuropsychol 44(3):325–338. https://doi.org/10.1080/87565641.2019.1586906
Steriade M (2000) Corticothalamic resonance, states of vigilance and mentation. Neuroscience 101:243–276. https://doi.org/10.1016/S0306-4522(00)00353-5
Tatum IV WO (2014) Handbook of EEG interpretation
Weiss S, Mueller HM (2003) The contribution of EEG coherence to the investigation of language. Brain Lang 85:325–343
Wróbel A (2000) Beta activity: a carrier for visual attention. Acta Neurobiol Exp 60:247–260
Zyma I, Tukaev S, Seleznov I et al (2019) Electroencephalograms during Mental Arithmetic Task Performance. Data 4:14. https://doi.org/10.3390/data4010014
Acknowledgements
The authors would like to acknowledge the contribution of Md. Shahriarzaman, Department of Biomedical Engineering of KUET for some of his help during data analysis and also would like to thank Prof. Dr. Sheikh Md. Rabiul Islam, Department of Electronics and Communication Engineering of KUET for his exceptionally valuable guidelines to complete this work. We also thank the anonymous reviewers for their valuable comments leading to the more clear and more definite manuscript.
Funding
This work is done without any financial support.
Author information
Authors and Affiliations
Contributions
Md. Rayahan Sarker Bipul developed the idea, prepared MATLAB codes for the used algorithms, and wrote the paper along with Md. Asadur Rahman. Md. Foisal Hossain and Md. Asadur Rahman supervised the work. All authors reviewed the language and grammatical structures of the article and gave consent for publication.
Corresponding author
Ethics declarations
Conflict of interest
The authors don’t have any competing interests with the others regarding this research work.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Bipul, M.R.S., Rahman, M.A. & Hossain, M.F. Study on different brain activation rearrangement during cognitive workload from ERD/ERS and coherence analysis. Cogn Neurodyn (2023). https://doi.org/10.1007/s11571-023-10032-6
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11571-023-10032-6