Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions
Tóm tắt
While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus, this study aims at classifying existing raw EEG meditation data on single and multiple sessions to come up with meaningful inferences which will be highly beneficial when developing algorithms that can support meditation practices. In this analysis, data have been collected on Pre-Resting (before-meditation), Post-Resting (after-meditation), LKM-Self and LKM-Others for 32 participants and hence allowing us to conduct six pairwise comparisons for the four mind tasks. Common Spatial Patterns (CSP) is a feature extraction method widely used in motor imaginary brain computer interface (BCI), but not in meditation EEG data. Therefore, using CSP in extracting features from meditation EEG data and classifying meditation/non-meditation instances, particularly for multiple sessions will create a new path in future meditation EEG research. The classification was done using Linear Discriminant Analysis (LDA) where both meditation techniques (LKM-Self and LKM-Others) were compared with Pre-Resting and Post-Resting instances. The results show that for a single session of 32 participants, around 99.5% accuracy was obtained for classifying meditation/Pre-Resting instances. For the 15 participants when using five sessions of EEG data, around 83.6% accuracy was obtained for classifying meditation/Pre-Resting instances. The results demonstrate the ability to classify meditation/Pre-Resting data. Most importantly, this classification is possible for multiple session data as well. In addition to this, when comparing the classification accuracies of the six mind task pairs; LKM-Self, LKM-Others and Post-Resting produced relatively lower accuracies among them than the accuracies obtained for classifying Pre-Resting with the other three. This indicates that Pre-Resting has some features giving a better classification indicating that it is different from the other three mind tasks.
Tài liệu tham khảo
Thomas JW, Cohen M (2014) A methodological review of meditation research. Front Psychiatry. https://doi.org/10.3389/fpsyt.2014.00074
Grossman P, Niemann L, Schmidt S, Walach H (2004) Mindfulness-based stress reduction and health benefits: a meta-analysis. J Psychosom Res 57:35–43. https://doi.org/10.1016/S0022-3999(03)00573-7
Lau MA, Bishop SR, Segal ZV (2010) The Toronto Mindfulness Scale: development and validation. J Clin Psychol 66:430–441. https://doi.org/10.1002/jclp
Davis KM, Lau MA, Cairns DR (2009) Development and preliminary validation of a trait version of the Toronto mindfulness scale. J Cogn Psychother 23:185–195. https://doi.org/10.1891/0889-8391.23.3.185
Nasrolahzadeh M, Mohammadpoory Z, Haddadnia J (2019) Analysis of heart rate signals during meditation using visibility graph complexity. Cogn Neurodyn 13:45–52. https://doi.org/10.1007/s11571-018-9501-5
Delgado-Pastor LC, Perakakis P, Subramanya P, Telles S, Vila J (2013) Mindfulness (Vipassana) meditation: effects on P3b event-related potential and heart rate variability. Int J Psychophysiol 90:207–214. https://doi.org/10.1016/j.ijpsycho.2013.07.006
Dziembowska I, Izdebski P, Rasmus A, Brudny J, Grzelczak M, Cysewski P (2016) Effects of heart rate variability biofeedback on EEG alpha asymmetry and anxiety symptoms in male athletes: a pilot study. Appl Psychophysiol Biofeedback 41:141–150. https://doi.org/10.1007/s10484-015-9319-4
West MA (1980) Meditation and the EEG. Psychol Med 10:369–375. https://doi.org/10.1017/S0033291700044147
Ahani A, Wahbeh H, Nezamfar H, Miller M, Erdogmus D, Oken B (2014) Quantitative change of EEG and respiration signals during mindfulness meditation. J Neuroeng Rehabil 11:87
Bostanov V, Ohlrogge L, Britz R, Hautzinger M, Kotchoubey B (2018) Measuring mindfulness: a psychophysiological approach. Front Hum Neurosci 12:1–22. https://doi.org/10.3389/fnhum.2018.00249
Singh A, Lal S, Guesgen HW (2019) Reduce calibration time in motor imagery using spatially regularized symmetric positives-definite matrices based classification. Sensors (Switzerland). https://doi.org/10.3390/s19020379
Cahn BR, Delorme A, Polich J (2010) Occipital gamma activation during Vipassana meditation. Cogn Process 11:39–56. https://doi.org/10.1007/s10339-009-0352-1
Dunn BR, Hartigan JA, Mikulas WL (1999) Concentration and mindfulness meditations: unique forms of consciousness? Appl Psychophysiol Biofeedback 24:147–165. https://doi.org/10.1023/A:1023498629385
Banquet JP (1973) Spectral analysis of the EEG in meditation. Electroencephalogr Clin Neurophysiol 35:143–151. https://doi.org/10.1016/0013-4694(73)90170-3
Jurcak V, Tsuzuki D, Dan I (2007) 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage 34:1600–1611. https://doi.org/10.1016/j.neuroimage.2006.09.024
Tang YY, Tang R, Rothbart MK, Posner MI (2019) Frontal theta activity and white matter plasticity following mindfulness meditation. Curr Opin Psychol 28:294–297. https://doi.org/10.1016/j.copsyc.2019.04.004
Scheeringa R, Bastiaansen MCM, Petersson KM, Oostenveld R, Norris DG, Hagoort P (2008) Frontal theta EEG activity correlates negatively with the default mode network in resting state. Int J Psychophysiol 67:242–251. https://doi.org/10.1016/j.ijpsycho.2007.05.017
Ferrarelli F, Smith R, Dentico D, Riedner BA, Zennig C, Benca RM, Lutz A, Davidson RJ, Tononi G (2013) Experienced mindfulness meditators exhibit higher parietal-occipital EEG gamma activity during NREM sleep. PLoS ONE. https://doi.org/10.1371/journal.pone.0073417
Özerdem A, Güntekin B, Atagün I, Turp B, Başar E (2011) Reduced long distance gamma (28–48 Hz) coherence in euthymic patients with bipolar disorder. J Affect Disord 132:325–332. https://doi.org/10.1016/j.jad.2011.02.028
Cahn BR, Polich J (2006) Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychol Bull 132:180–211. https://doi.org/10.1037/0033-2909.132.2.180
NEW actiCAP and actiCHamp caps now with an improved and unified layout. 28th July 2016. https://pressrelease.brainproducts.com/uol/. Accessed 13 Mar 2023
Singh A, Hussain AA, Lal S, Guesgen HW (2021) A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface. Sensors 21:1–35. https://doi.org/10.3390/s21062173
Devipriya A, Nagarajan N (2018) A novel method of segmentation and classification for meditation in health care systems. J Med Syst. https://doi.org/10.1007/s10916-018-1062-y
Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F (2018) A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng. https://doi.org/10.1088/1741-2552/aab2f2
Ingle R, Oimbe S, Kehri V, Awale RN (2018) Classification of EEG signals during meditation and controlled state using PCA, ICA, LDA and support vector machines. Int J Pure Appl Math 118:3179–3190
Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37:8659–8666. https://doi.org/10.1016/j.eswa.2010.06.065
Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009
Urigüen JA, Garcia-Zapirain B (2015) EEG artifact removal: state-of-the-art and guidelines. J Neural Eng. https://doi.org/10.1088/1741-2560/12/3/031001
Duda AT, Clarke AR, De Blasio FM, Rout TW, Barry RJ (2023) The effects of concentrative meditation on the electroencephalogram in novice meditators. Clin EEG Neurosci 54:130–140. https://doi.org/10.1177/15500594211065897
Widmann A, Schröger E, Maess B (2015) Digital filter design for electrophysiological data—a practical approach. J Neurosci Methods 250:34–46. https://doi.org/10.1016/j.jneumeth.2014.08.002
Barry RJ, Clarke AR, Johnstone SJ, Magee CA, Rushby JA (2007) EEG differences between eyes-closed and eyes-open resting conditions. Clin Neurophysiol 118:2765–2773. https://doi.org/10.1016/j.clinph.2007.07.028
Thomas KP, Lau CT, Vinod AP, Guan C, Ang KK (2009) A new discriminative common spatial pattern method for motor imagery brain–computer interfaces. IEEE Trans Biomed Eng 56:2730–2733. https://doi.org/10.1109/TBME.2009.2026181
Li M, Zhang C, Jia S, Sun Y (2018) Classification of motor imagery tasks in source domain. In: Li M (ed) International conference on mechatronics and automation. IEEE, Changchun
Ang KK, Chin ZY, Wang C, Guan C, Zhang H (2012) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci. https://doi.org/10.3389/fnins.2012.00039
Lotte F, Guan C (2011) Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng 58:355–362. https://doi.org/10.1109/TBME.2010.2082539
Schlögl A, Lee F, Bischof H, Pfurtscheller G (2005) Characterization of four-class motor imagery EEG data for the BCI-competition 2005. J Neural Eng. https://doi.org/10.1088/1741-2560/2/4/L02
Tariq M, Trivailo PM, Simic M (2019) Classification of left and right knee extension motor imagery using common spatial pattern for BCI applications. Proc Comput Sci 159:2598–2606
Deolindo CS, Ribeiro MW, Aratanha MA, Afonso RF, Irrmischer M, Kozasa EH (2020) A critical analysis on characterizing the meditation experience through the electroencephalogram. Front Syst Neurosci 14:1–29. https://doi.org/10.3389/fnsys.2020.00053
Medvedev ON, Krägeloh CU, Narayanan A, Siegert RJ (2017) Measuring mindfulness: applying generalizability theory to distinguish between state and trait. Mindfulness (N Y) 8:1036–1046. https://doi.org/10.1007/s12671-017-0679-0
Fell J, Axmacher N, Haupt S (2010) From alpha to gamma: Electrophysiological correlates of meditation-related states of consciousness. Med Hypotheses 75:218–224. https://doi.org/10.1016/j.mehy.2010.02.025
Aftanas LI, Golosheikin SA (2003) Changes in cortical activity during altered states of consciousness: study of meditation by high resolution EEG. Fiziol Cheloveka 29:18–27
Fingelkurts AA, Fingelkurts AA, Kallio-Tamminen T (2016) Trait lasting alteration of the brain default mode network in experienced meditators and the experiential selfhood. Self Identity 15:381–393. https://doi.org/10.1080/15298868.2015.1136351
Brandmeyer T, Delorme A, Wahbeh H (2019) The neuroscience of meditation: classification, phenomenology, correlates, and mechanisms. Prog Brain Res 244:1–29. https://doi.org/10.1016/bs.pbr.2018.10.020
Zeng X, Li M, Zhang B, Liu X (2014) Revision of the Philadelphia mindfulness scale for measuring awareness and equanimity in Goenka’s Vipassana meditation with Chinese Buddhists. J Relig Health 54:623–637. https://doi.org/10.1007/s10943-014-9870-y
Lukseng T, Siripornpanich V, Chutabhakdikul N (2020) Long-term Vipassana meditation enhances executive function in adult meditators. Siriraj Med J 72:352–360
Murata T, Koshino Y, Omori M, Murata I, Nishio M, Sakamoto K, Horie T, Isaki K (1994) Quantitative EEG study on Zen meditation (Zazen). Psychiatry Clin Neurosci 48:881–890. https://doi.org/10.1111/j.1440-1819.1994.tb03090.x
Yu X, Fumoto M, Nakatani Y, Sekiyama T, Kikuchi H, Seki Y, Sato-Suzuki I, Arita H (2011) Activation of the anterior prefrontal cortex and serotonergic system is associated with improvements in mood and EEG changes induced by Zen meditation practice in novices. Int J Psychophysiol 80:103–111. https://doi.org/10.1016/j.ijpsycho.2011.02.004
Kato T, Kawano K (2017) Physiological investigation on EEG coherency during yoga meditation and qigong exercise. J Int Soc Life Inform Sci 35:99. https://doi.org/10.18936/islis.35.2_99
Sharma H, Raj R, Juneja M (2019) EEG signal based classification before and after combined Yoga and Sudarshan Kriya. Neurosci Lett 707:134300. https://doi.org/10.1016/j.neulet.2019.134300
Wong G, Sun R, Adler J, Yeung KW, Yu S, Gao J (2022) Loving-kindness meditation (LKM) modulates brain-heart connection: an EEG case study. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2022.891377
Rana N (2015) Mindfulness and loving-kindness meditation: a potential tool for mental health and subjective well-being. Indian J Posit Psychol 6:189. https://doi.org/10.15614/ijpp/2015/v6i2/73847
Travis F, Haaga DAF, Hagelin J, Tanner M, Arenander A, Nidich S, Gaylord-King C, Grosswald S, Rainforth M, Schneider RH (2010) A self-referential default brain state: patterns of coherence, power, and eLORETA sources during eyes-closed rest and transcendental meditation practice. Cogn Process 11:21–30. https://doi.org/10.1007/s10339-009-0343-2
Travis F (2001) Autonomic and EEG patterns distinguish transcending from other experiences during Transcendental Meditation practice. Int J Psychophysiol 42:1–9. https://doi.org/10.1016/S0167-8760(01)00143-X
Wahbeh H, Sagher A, Back W, Pundhir P, Travis F (2018) A systematic review of transcendent states across meditation and contemplative traditions. Explore 14:19–35. https://doi.org/10.1016/j.explore.2017.07.007
Travis F, Shear J (2010) Focused attention, open monitoring and automatic self-transcending: categories to organize meditations from Vedic, Buddhist and Chinese traditions. Conscious Cogn 19:1110–1118. https://doi.org/10.1016/j.concog.2010.01.007
Fingelkurts AA, Fingelkurts AA, Kallio-Tamminen T (2015) EEG-guided meditation: a personalized approach. J Physiol Paris 109:180–190. https://doi.org/10.1016/j.jphysparis.2015.03.001
Davis DM, Hayes JA (2011) What are the benefits of mindfulness? A practice review of psychotherapy-related research. Psychotherapy 48:198–208. https://doi.org/10.1037/a0022062
Hankey A, Shetkar R (2016) Self-transcending meditation is good for mental health: why this should be the case. Int Rev Psychiatry 28:236–240. https://doi.org/10.1080/09540261.2016.1191449
Brandmeyer T, Delorme A (2013) Meditation and neurofeedback. Front Psychol 4:3–5. https://doi.org/10.3389/fpsyg.2013.00688
Flett JAM, Hayne H, Riordan BC, Thompson LM, Conner TS (2019) Mobile mindfulness meditation: a randomised controlled trial of the effect of two popular apps on mental health. Mindfulness (N Y) 10:863–876. https://doi.org/10.1007/s12671-018-1050-9
Qu C, Sas C, Daudén Roquet C, Doherty G (2020) Functionality of top-rated mobile apps for depression: systematic search and evaluation. JMIR Ment Health. 7:e15321. https://doi.org/10.2196/15321
Bostock S, Crosswell AD, Prather AA, Steptoe A (2019) Mindfulness on-the-go: effects of a mindfulness meditation app on work stress and well-being. J Occup Health Psychol 24:127–138. https://doi.org/10.1037/ocp0000118
Huberty J, Green J, Glissmann C, Larkey L, Puzia M, Lee C (2019) Efficacy of the mindfulness meditation mobile app “calm” to reduce stress among college students: randomized controlled trial. J Med Internet Res. https://doi.org/10.2196/14273
Crivelli D, Fronda G, Venturella I, Balconi M (2019) Supporting mindfulness practices with brain-sensing devices. Cognitive and electrophysiological evidences. Mindfulness (NY) 10:301–311. https://doi.org/10.1007/s12671-018-0975-3
Roquet CD, Sas C (2018) Evaluating mindfulness meditation apps. In: Conference on human factors in computing systems—proceedings, 2018-April. https://doi.org/10.1145/3170427.3188616
Tee JL, Phang SK, Chew WJ, Phang SW, Mun HK (2020) Classification of meditation states through EEG: a method using discrete wavelet transform. AIP Conf Proc. https://doi.org/10.1063/5.0001375