| Abstract|| |
Background: Heartfulness meditation (HM) has been shown to have positive impacts on cognition and well-being, which makes it important to look into the neurophysiological mechanisms underlying the phenomenon. Aim: A cross-sectional study was conducted on HM meditators and nonmeditators to assess frontal electrical activities of the brain and self-reported anxiety and mindfulness. Settings and Design: The present study employed a cross-sectional design. Methods: Sixty-one participants were recruited, 28 heartfulness meditators (average age male: 31.54 ± 4.2 years and female: 30.04 ± 7.1 years) and 33 nonmeditators (average age male: 25 ± 8.5 years and female: 23.45 ± 6.5 years). An electroencephalogram (EEG) was employed to assess brain activity during baseline (5 min), meditation (10 min), transmission (10 min) and post (5 min). Self-reported mindfulness and anxiety were also collected in the present study. The EEG power spectral density (PSD) and coherence were processed using MATLAB. The statistical analysis was performed using an independent sample t-test for trait mindfulness and anxiety, repeated measures analysis of variance (ANOVA) for state mindfulness and anxiety, and Two-way multivariate ANOVA for EEG spectral frequency and coherence. Results: The results showed higher state and trait mindfulness, P < 0.05 and P < 0.01, respectively, and lower state and trait anxiety, P < 0.05 and P < 0.05, respectively. The PSD outcomes showed higher theta (P < 0.001) and alpha (P < 0.01); lower beta (P < 0.001) and delta (P < 0.05) power in HM meditators compared to nonmeditators. Similarly, higher coherence was found in the theta (P < 0.01), alpha (P < 0.05), and beta (P < 0.01) bands in HM meditators. Conclusions: These findings suggest that HM practice may result in wakeful relaxation and internalized attention that can influence cognition and behavior.
Keywords: Anxiety, autonomic activity, coherence, electroencephalogram, heartfulness meditation
|How to cite this article:|
Krishna D, Prasanna K, Angadi B, Singh BK, Anurag S, Deepeshwar S. Heartfulness meditation alters electroencephalogram oscillations: An electroencephalogram study. Int J Yoga 2022;15:205-14
|How to cite this URL:|
Krishna D, Prasanna K, Angadi B, Singh BK, Anurag S, Deepeshwar S. Heartfulness meditation alters electroencephalogram oscillations: An electroencephalogram study. Int J Yoga [serial online] 2022 [cited 2023 Jan 31];15:205-14. Available from: https://www.ijoy.org.in/text.asp?2022/15/3/205/367784
| Introduction|| |
Meditation is a self-regulated mental process that improves behavior, emotions, and cognition., It enhances one's state of mind and self-regulated attention that induce self-awareness, relaxation, and well-being. In addition, meditation practice is considered an essential factor affecting mental states and traits of personality. Studies have been carried out to explore meditation-related changes in the electroencephalogram (EEG) that measures spontaneous electrical activities in specific regions of the brain with temporal resolution in milliseconds. The spectral power and coherence of the brain are influenced by meditative practices. Previous meditation studies reported changes in the brain, including the frontal cortex.,, Moreover, a recent report suggests that regular practice of mindfulness meditation enhances frontal-midline theta activity that induces white matter plasticity.
Heartfulness meditation (HM) is a combination of relaxation, meditation, cleaning, and prayer. Relaxation is recommended to relax all parts of the body and prepare one for meditation. Subtle suggestions complemented by the energy flow from Mother Earth are used to remove heaviness and bring lightness to our system. Meditation is done on the source of light present in the heart (preferably before sunrise). Cleaning is done in the evening after the day's work is over to remove the complexities and impurities created by our day-to-day activities and rejuvenate the mind. Prayer is silently offered before going to bed, connecting ourselves with our inner self to remind the purpose of our existence. Moreover, transmission enhances the experience of meditation. Certified heartfulness trainers are trained to channel the process of transmission, of subtle yogic energy, into the heart of the meditating aspirants.
In the present study, we have assessed EEG during HM meditation and transmission. Few studies reported improvement in psychological and cognition parameters following HM practice. Recently, an EEG study reported enhanced gamma activity in the occipital region of the brain during HM meditation practice. No attempt has been made to investigate frontal brain oscillation and correlate it with mindfulness and anxiety in HM practitioners. Hence, the present study is intended to investigate the neural activities of the frontal brain regions and self-reported anxiety, and mindfulness in HM meditators and nonmeditators.
| Methods|| |
A cross-sectional study was conducted to compare the frontal EEG activities and self-reported anxiety and mindfulness measures in HM meditators (n = 28) and nonmeditators (n = 33) participants. All sixty-one participants (40 males) with an age range between 20 and 45 years were recruited for this study. The visual and auditory problems, neuropsychiatry problems, and history of drug and alcohol abuse were excluded from this study. The study was approved by the Institutional Ethics Committee (RES/IEC-SVYASA/164/1/2020), and written informed consent was obtained from each participant after explaining the design and assessment tools of the study.
The experiment was conducted in four sessions, i.e., baseline (BS) for 5 min, meditation (med) for 10 min, transmission (trans) for 10 min, and postresting (post) for 5 min, as shown in [Figure 1]. The sequence of the HM session was counterbalanced between participants to prevent the sequential effect.
The sociodemographic data were obtained for age, gender, education, and socioeconomic status. Participants were asked to provide meditation experience (in months) and frequency of meditation practices (duration of each meditation session in minutes and day per week). Further, self-reported scales were administered, followed by EEG signals acquisition on each participant. Data collection on female were acquired outside of the menstrual period., Due to bad signal quality of EEG data, seven participants (HM [n = 2; females = 2] and nonmeditators [n = 2; female = 1]) removed from the analysis. The characteristics of the remaining 54 participants are given in [Table 1].
Mindfulness Attention Awareness Scale
The Mindfulness Attention Awareness Scale (MAAS) was used to assess dispositional mindfulness. MAAS consists of two subscales, i.e., state mindfulness and trait mindfulness. State MAAS (S-MAAS) is a 5-item self-reported questionnaire that measures the immediate expression of basic features of mindfulness. Trait MAAS (T-MAAS) contains a 15-item self-reported single-factor scale to assess a unique quality of consciousness related to a variety of well-being constructs and is associated with psychological and physical health.
State and trait anxiety inventory
The state and trait anxiety of the participants were assessed using the State and trait anxiety inventory (STAI). It consists of two questionnaires of 20 items, each emphasizing the intensity of anxiety symptoms; State anxiety (S-STAI; how an individual feels at the moment); trait anxiety (T-STAI; how an individual generally feels). Scores range from 20 to 90, and the cut-off for high anxiety is 48.
Procedure of electroencephalogram data acquisition
All participants were asked to report by 7:00 am in the cognitive neuroscience laboratory. The consent form was obtained from each participant and asked to give sociodemographic information. The self-reported state anxiety and mindfulness questionnaires were obtained before and after the HM session, and trait anxiety and mindfulness were administered before the EEG recordings.
The EEG was recorded using 128 channels EGI system of Net Amps 300 amplifiers (Electrical Geodesics, Inc., Eugene, OR, U.S.A). Hydrocel Geodesic Sensor Net (GSN300) was put on the participant's scalp as per the stipulated instructions of the EGI manual. The reference channel was placed on the vertex sensor (Cz), and a notch filter was applied at 50 Hz. The impedance of each electrode was kept below 50 kΩ. The EEG signals were sampled at 250 kHz. Data were acquired in NetStation™ software (version 4.5.8). The EEG data processing sequence is presented schematically in [Figure 2].
|Figure 2: Flow diagram of EEG data processing. EEG: Electroencephalogram|
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A total of 30 min of raw EEG signals were segmented (.mff format) for baseline (5 min), meditation (10 min), transmission (10 min), and post (5 min) and preprocessed in MATLAB 2018a using the EEGLAB toolbox (version 2021). The 20 channels were placed over the frontal lobe, as show in [Figure 3]. The finite impulse response filter was applied for the low pass filter at 30 Hz and the high pass filter at 0.3 Hz. The artifacts (ocular, muscles, and head movement) were attenuated by conducting an independent component analysis on the individual recordings. EEG data were exported in.edf format, and custom scripts were written in MATLAB for further analysis.
|Figure 3: Highlighted electrodes of frontal lobe were used to process the EEG signals|
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Decomposition of electroencephalogram signals
Discrete wavelet transform (DWT) was used to decompose EEG signals into different frequency components to derive the EEG band information. To represent approximate signals, DWT employs a cluster of functions. With a sampling frequency of 250 Hz and five-level wavelet decomposition, it generates a detailed and approximate coefficient that can be used to approximate the original signal. Daubechies 2 wavelet (db2) as the db2 mother wavelet was used to analyze the EEG signals.
Power spectral density
The power spectral density (PSD) describes the distribution of power for a signal over a frequency. The spectral analysis of cleaned EEG signals was obtained by Welch's averaged periodogram method. The PSD was calculated using the pwelch command in MATLAB with a hamming window of 2 s and a sampling overlap of 75%. The normalized was used between the range 0 and 1.
PSD and coherence were calculated using Welch's method, a nonparametric method that is a modified approach of the Fast Fourier Transform algorithm, at four different frequency bands: delta (0.3–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), and beta (12–30 Hz).
Magnitude-squared coherence estimation
The magnitude-squared coherence was used to measure the changes in band activities between the right and left frontal regions of the brain. The frontal coherence averages included 10 coherence pairs between 10 frontal channels. Welch's method has been implemented to estimate PSD across electrodes at interest pairs. The coherence falls in the range of 0–1, whereby 0 represents that both signals are independent and 1 represents the maximum correlation between the signals.
Statistical analysis was performed using IBM SPSS version 18 (Chicago, USA) in Windows. The mindfulness and anxiety variables were checked for normality and showed normally distributed. Repeated measures analysis of variance (ANOVA) was used on S-MAAS and S-STAI to check the state and group differences. The independent sample t-test was used on T-MAAS and T-STAI to evaluate group differences. The two-way mixed multiple ANOVA (MANOVA) was used to evaluate the EEG variables between HM and nonmeditators, followed by univariate tests, and the Bonferroni correction was used to evaluate the comparative differences between significant univariate tests. Mauchly's test of sphericity suggested that the assumption of sphericity was violated (P < 0.01); therefore, Greenhouse–Geisser was used for EEG data. The PSD data were analyzed with three factors, i.e., Group (HM and nonmeditators) × State (BS, Med, Trans, and Post) × Hemisphere (R-right and L-left). Pearson's correlation was used to check the relationship between EEG and self-reported scales. Statistical significance was predetermined at P < 0.05, and all P values were two-sided.
| Results|| |
Between-group comparisons were conducted on all of the demographic variables, as listed in [Table 1]. No significant differences were observed in any demographic variables (P > 0.05). Repeated measures of ANOVA of the S-MAAS revealed significant changes in State (F(1,52) = 9.63, P < 0.01; η2 = 0.16) and Group (F (1,52) = 26.21, P < 0.001; η2 = 0.34). Whereas, S-STAI showed significant changes in State (F (1,52) = 6.25, P < 0.05; η2 = 0.12) and Group (F (1,52) = 36.1, P < 0.001; η2 = 0.41).
The independent sample t-test showed significant group differences in T-MAAS (P < 0.01) and T-STAI (P < 0.05) in HM meditators compared to nonmeditators, as shown in [Figure 4].
|Figure 4: Descriptive plot of (a) trait mindfulness and (b) trait anxiety for HM and non-meditators. HM: Heartfulness meditators, Non-med: Non-meditators, CI- Confidence Interval, T-MAAS- Trait-Mindfulness Attention Awareness Scale, T-STAI- Trait- State Trait Anxiety Inventory. *P<0.05 and **P<0.01|
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Two-way multivariate ANOVA (MANOVA) of between group showed significant differences in Group (F (4,49) = 4.845, P < 0.01; η2 = 0.28), State (F (12,41) = 2.39, P < 0.05; η2 = 0.41), and Hemisphere (F (4,45) = 15.41, P < 0.001; η2 = 0.73).
Further, the HM group showed significant univariate outcomes in State due to changes in alpha power (F (3,156) = 5.163, P < 0.01; η2 = 0.09) and hemispheric differences due to changes in delta and theta power, F (2,104) =38.99, P < 0.001; η2 = 0.43, and F (2,104) = 20.14, P < 0.001; η2 = 0.28, respectively.
State mindfulness and anxiety
In post hoc outcomes, the HM group showed significantly increased mindfulness and decreased anxiety within-group, P < 0.05 and P < 0.001 and between-group comparison, P < 0.05 and P < 0.001, respectively. Whereas, there was no change in mindfulness and anxiety in the non-meditators showed in [Figure 5]a and [Figure 5]b.
|Figure 5: Within and between groups outcomes of (a) state mindfulness and (b) state anxiety for participants. *P<0.05 and $$$P<0.001. *represents changes pre to post and $represents differences in groups|
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Power spectral density
The PSD outcomes of HM group revealed a significantly higher theta in the right and left frontal at baseline, P < 0.01 and P < 0.05, meditation, P < 0.01 and P < 0.05, transmission, P < 0.001 and P < 0.001, and post, P < 0.01 and P < 0.001, respectively, compared to nonmeditators. Moreover, a higher alpha was observed in the right and left frontal of the HM group in baseline, P < 0.01 and P < 0.05, mediation, P < 0.01 and P < 0.05, transmission, P < 0.01 and P < 0.05, respectively, and left frontal in the post (P < 0.05) compared to nonmeditators.
Interestingly, the beta band demonstrated lower bilateral power (P < 0.001) at all states in the HM group. The delta power was higher among nonmeditators in the post (P < 0.05), as shown in [Figure 6].
|Figure 6: Graphical representation of PSD for group differences in HM and nonmediators (values are in mean and SD). PSD: Power spectral density, HM: Heartfulness meditation, SD: Standard deviation. *P<0.05, **P < 0.01, ***P < 0.001, where *represents between-group factor. BS: Baseline, Med: Meditation, Trans: Transmission, R: Right hemisphere, L: Left hemisphere, HM: Heartfulness meditation, and NonMed: Nonmeditators, Changes within groups were observed only in the HM group|
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Within-group outcomes showed significantly higher theta power in the right frontal of the HM group during transmission (P < 0.01) than meditation and post (P < 0.01) compared to transmission. A significantly increased alpha power was found in the right hemisphere of the HM group during transmission, P < 0.01 and P < 0.05 compared to baseline and meditation, respectively. Moreover, reduced alpha power was observed in the right hemisphere during post, P < 0.01 and P < 0.001 compared to meditation and transmission, respectively, as shown in [Table 2].
|Table 2: Post-hoc outcomes of PSD for within group comparison between heartfulness meditators and non-meditators|
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The theta coherence was significantly higher in baseline (P < 0.05) and transmission (P < 0.01) in the HM group compared to nonmeditators. Similarly, the alpha coherence was found to be higher during baseline (P < 0.05) and beta coherence during meditation (P < 0.01) in the HM compared to nonmeditators, as shown in [Table 3].
|Table 3: Post-hoc outcomes of coherence for heartfulness meditation and nonmeditators|
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Pearson's correlation analysis showed a significant negative correlation between T-STAI and T-MAAS (r = −0.42; P < 0.05). The T-MAAS was positively correlated with right frontal theta during meditation (r = 0.48; P < 0.05), during post (r = 0.45; P < 0.05) and with right frontal alpha during meditation (r = 0.54; P < 0.01). The beta power of the right frontal was negatively correlated with T-MAAS during transmission (r = −0.47; P < 0.05). Moreover, T-STAI was negatively correlated with left frontal alpha during meditation (r = −0.51; P < 0.05) and positively correlated with left frontal beta during transmission (r = 0.57; P < 0.01). The results of Pearson's correlation are shown in [Figure 7].
|Figure 7: Pearson's correlation between trait anxiety, mindfulness, and EEG bands (PSD). EEG: Electroencephalogram, PSD: Power spectral density. *P < 0.05, **P < 0.01, T-MAAS: Trait mindfulness attention awareness scale, and T-STAI: Trait and state anxiety scale|
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| Discussion|| |
In the present study, the self-reported outcome measures showed HM meditators had higher state and trait mindfulness and lower state and trait anxiety than nonmeditators. These results align with previous findings that meditation practice improves the positive state of mind, attention, awareness, and mindfulness of the present moment.,,, The HM practice could be a useful therapeutic technique to ameliorate excessive anxiety, restlessness, and burnout.,,
The PSD of EEG analysis revealed higher frontal theta in the HM practitioners compared to nonmeditators. There is a shift to the higher power of theta and alpha frequencies in the right frontal during transmission and in post compared to baseline in the HM group. A previous study suggests that the enhanced power in the theta band reflects thalamic and cortical activities linked to the deep relaxation that frequently occurs in experienced meditators. The frontal theta activities reflect the involvement of attention and memory-related neural circuit, including the anterior cingulate, orbitofrontal, and association cortex.,, It could be the result of neural activation or oligodendrocyte activation to aid myelination, which improves the speed and specificity of brain network connections., The increased frontal theta coherence was observed during baseline and transmission in HM meditators. A similar outcome of a previous meditation study reported that enhanced theta coherence improves functional connectivity of the frontal cortex. It has been shown that frontal and parietal theta coherence correlates with executive function tasks, including working memory. The theta band of the right frontal showed a positive correlation with mindfulness during meditation and transmission in the HM practitioners. These shreds of evidence indicate that meditation may improve self-awareness, internalized attention, relaxation, and complex cognitive processes. However, regular HM practice may be associated with an enlarged repertoire of personal experiences other than ordinary conscious wakefulness.
The higher alpha power in the HM meditators reflects more “state-like” processes and is positively linked with basic cognitive functions such as internalized attention, memory, and behavioral inhibition.,, Moreover, the experienced HM meditators also showed higher coherence in the alpha band. The higher alpha coherence in meditators can be linked with synchronized neuronal oscillations and long-range cortical interconnectivity in the frontal lobe. The increased long-range cortical interconnectivity enables long-term changes “trait-like” in specific cognitive processes, relaxation, and self-control., The alpha activity in the right frontal showed a positive correlation with mindful attention and a negative correlation with anxiety. The alpha-band findings suggest that the right frontal region has a strong functional correlation with the other cortical and subcortical regions associated with internalized attention and lower anxiety., A magnetoencephalogram study showed higher alpha power in the frontal lobe corresponds to medial prefrontal and anterior cingulate cortices during transcendental meditation practice that suggests internalized attention with deep relaxation in meditators.
More interestingly, we have found lower beta in the HM meditators compared to nonmeditators suggesting lower sustained attention, relaxed state of mind, and minimal self-awareness., In contrast, a higher frontal beta was found during the motor process, alertness, and attentive state., We have observed higher frontal beta coherence in the HM meditators, which reflects higher executive function and cognitive control.
The HM group showed a lower frontal delta, indicating alignment with the results of mindfulness meditation, and Vipassana meditation., Low delta in the frontal region linked to nonanalyzing and nonjudgmental in HM meditators. Typically, delta waves occur during noneye movement sleep, and they are rationally accelerated in a few conditions, including brain tumors.,
Despite the strength of comparing frontal EEG activity and self-reported mental scales during HM meditators and nonmeditators, this study has a few limitations that should be taken into account. First, we tested a small sample of participants in each group, which limits the generalization of the present findings. Second, the nonmeditators group participants also had a single session practice of HM and cannot be considered nonmeditators. Third is the broad age range of the participants. Further, meditation in a closed room with minimal lighting and participants being connected with 128 channels EEG cap mounted on the head surface could also cause some discomfort to the meditators. There are a few other unknown factors of bias, such as heterogeneity in practice, personal life events, and clinical history, which were not taken into consideration. The broad age range was considered due to the limited number of meditators available in the meditation center. The duration of HM practice was self-reported by meditators, and not possible to rule out the exact duration of experience of HM practice.
Further studies can explore the other dimensions of functional activities of the frontal cortex, including cognition in HM practitioners, by including other neuroimaging techniques, such as functional magnetic resonance imaging or positron emission tomography to rule out the differences in structural or functional aspects of the brain.
| Conclusions|| |
To our knowledge, this is the first study that reported frontal cortical activities in HM meditators. Our results support that HM practice can influence frontal brain activities which are associated with higher-order brain functioning in practitioners. The outcome of the frontal EEG activities reflects internalized focused attention with deep relaxation. The HM practice may promote positive health and shift their mental state of being toward a deeper level of consciousness by reducing anxiety, enhancing mindfulness, and the functional connectivity of the brain. Although EEG activities of HM practice showed an alertness state with deep relaxation and high brain network connectivity in HM meditators, further studies are warranted.
Data availability statement
The data of this study are available with the corresponding author upon reasonable request.
The institutional ethics committee of Swami Vivekananda Yoga Anusandhana Samasthana (S-VYASA), Bengaluru, approved the study “RES/IEC-SVYASA/164/1/2020.”
We gratefully acknowledge that this study was partially funded by Heartfulness Institute and executed in the Cognitive Neuroscience Laboratory at Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bengaluru. The authors would like to thank Dr. J Krishnamurthy, Mr. Anil Jamadagani, Mr. Parthasarathy Patel, Mr. Krishna Chaitanya, and Mr. Budhi Bal Rana for supporting this study at different stages.
Financial support and sponsorship
This research was partially supported by Heartfulness Institute and Swami Vivekananda Yoga Anusandhana Samsthana (S-VYASA), Bengaluru, India.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Böckler A, Singer T. Longitudinal evidence for differential plasticity of cognitive functions: Mindfulness-based mental training enhances working memory, but not perceptual discrimination, response inhibition, and metacognition. J Exp Psychol Gen 2022;151:1573-90.
Murata T, Takahashi T, Hamada T, Omori M, Kosaka H, Yoshida H, et al.
Individual trait anxiety levels characterizing the properties of zen meditation. Neuropsychobiology 2004;50:189-94.
Burle B, Spieser L, Roger C, Casini L, Hasbroucq T, Vidal F. Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view. Int J Psychophysiol 2015;97:210-20.
Korponay C, Dentico D, Kral TR, Ly M, Kruis A, Davis K, et al.
The effect of mindfulness meditation on impulsivity and its neurobiological correlates in healthy adults. Sci Rep 2019;9:11963.
Lazar SW, Kerr CE, Wasserman RH, Gray JR, Greve DN, Treadway MT, et al.
Meditation experience is associated with increased cortical thickness. Neuroreport 2005;16:1893-7.
Deepeshwar S, Vinchurkar SA, Visweswaraiah NK, Nagendra HR. Hemodynamic responses on prefrontal cortex related to meditation and attentional task. Front Syst Neurosci 2014;8:252.
Tang YY, Tang R, Rothbart MK, Posner MI. Frontal theta activity and white matter plasticity following mindfulness meditation. Curr Opin Psychol 2019;28:294-7.
Patel KD, Pollock J, Soni V, Doty JR. The Heartfulness Way: Heart-Based Meditations for Spiritual Transformation. Oakland, CA, USA: New Harbinger Publications; 2018.
Arya NK, Singh K, Malik A, Mehrotra R. Effect of Heartfulness cleaning and meditation on heart rate variability. Indian Heart J 2018;70 Suppl 3:S50-5.
Sankar Sylapan B, Nair AK, Jayanna K, Mallipeddi S, Sathyanarayana S, Kutty BM. Meditation, well-being and cognition in heartfulness meditators – A pilot study. Conscious Cogn 2020;86:103032.
Ananthan VA. Modified Kuppuswamy scale for socioeconomic status of the Indian family – Update based on New CPI (IW) series from September 2020. J Family Med Prim Care 2021;10:2048-9. [Full text]
Haraguchi R, Hoshi H, Ichikawa S, Hanyu M, Nakamura K, Fukasawa K, et al.
The menstrual cycle alters resting-state cortical activity: A magnetoencephalography study. Front Hum Neurosci 2021;15:652789.
Brötzner CP, Klimesch W, Doppelmayr M, Zauner A, Kerschbaum HH. Resting state alpha frequency is associated with menstrual cycle phase, estradiol and use of oral contraceptives. Brain Res 2014;1577:36-44.
Brown KW, Ryan RM. The benefits of being present: Mindfulness and its role in psychological well-being. J Pers Soc Psychol 2003;84:822-48.
MacKillop J, Anderson EJ. Further psychometric validation of the Mindful Attention Awareness Scale (MAAS). J Psychopathol Behav Assess 2007;29:289-93.
Spielberger CD. State-trait anxiety inventory. In: The Corsini Encyclopedia of Psychology. Hoboken, NJ, USA: John Wiley & Sons, Inc.; 2010. p. 1.
Field T, Diego M, Delgado J, Medina L. Tai chi/yoga reduces prenatal depression, anxiety and sleep disturbances. Complement Ther Clin Pract 2013;19:6-10.
Bian Z, Li Q, Wang L, Lu C, Yin S, Li X. Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes. Front Aging Neurosci 2014;6:11.
Michel CM, Lantz G, Spinelli L, De Peralta RG, Landis T, Seeck M. 128-channel EEG source imaging in epilepsy: Clinical yield and localization precision. J Clin Neurophysiol 2004;21:71-83.
Delorme A, Makeig S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 2004;134:9-21.
Goodin P, Ciorciari J, Baker K, Carey AM, Harper M, Kaufman J. A high-density EEG investigation into steady state binaural beat stimulation. PLoS One 2012;7:e34789.
Ji N, Ma L, Dong H, Zhang X. EEG signals feature extraction based on DWT and EMD combined with approximate entropy. Brain Sci 2019;9:201.
Aliyu I, Lim CG. Selection of optimal wavelet features for epileptic EEG signal classification with LSTM. Neural Comput Appl 2021;1-21.
Unde SA, Shriram R. Coherence Analysis of EEG Signal Using Power Spectral Density. In: 2014 Fourth International Conference on Communication Systems and Network Technologies. IEEE; 2014. p. 871-4.
Goldstein MR, Turner AD, Dawson SC, Segal ZV, Shapiro SL, Wyatt JK, et al.
Increased high-frequency NREM EEG power associated with mindfulness-based interventions for chronic insomnia: Preliminary findings from spectral analysis. J Psychosom Res 2019;120:12-9.
Xiao R, Shida-Tokeshi J, Vanderbilt DL, Smith BA. Electroencephalography power and coherence changes with age and motor skill development across the first half year of life. PLoS One 2018;13:e0190276.
Zeidan F, Johnson SK, Diamond BJ, David Z, Goolkasian P. Mindfulness meditation improves cognition: Evidence of brief mental training. Conscious Cogn 2010;19:597-605.
Marciniak R, Sheardova K, Cermáková P, Hudeček D, Sumec R, Hort J. Effect of meditation on cognitive functions in context of aging and neurodegenerative diseases. Front Behav Neurosci 2014;8:17.
Telles S, Singh D, Naveen KV, Pailoor S, Singh N, Pathak S. P300 and heart rate variability recorded simultaneously in meditation. Clin EEG Neurosci 2019;50:161-71.
Vinchurkar SA, Singh D, Visweswaraiah NK. Self-reported measures of mindfulness in meditators and non-meditators: A cross-sectional study. Int J Yoga 2014;7:142-6.
] [Full text]
Krisanaprakornkit T, Ngamjarus C, Witoonchart C, Piyavhatkul N. Meditation therapies for attention-deficit/hyperactivity disorder (ADHD). Cochrane Database Syst Rev 2010;2010:CD006507.
Krisanaprakornkit T, Krisanaprakornkit W, Piyavhatkul N, Laopaiboon M. Meditation therapy for anxiety disorders. Cochrane Database Syst Rev 2006;25:CD004998.
Thimmapuram J, Pargament R, Sibliss K, Grim R, Risques R, Toorens E. Effect of heartfulness meditation on burnout, emotional wellness, and telomere length in health care professionals. J Community Hosp Intern Med Perspect 2017;7:21-7.
Deolindo CS, Ribeiro MW, Aratanha MA, Afonso RF, Irrmischer M, Kozasa EH. A critical analysis on characterizing the meditation experience through the electroencephalogram. Front Syst Neurosci 2020;14:53.
Hinterberger T, Schmidt S, Kamei T, Walach H. Decreased electrophysiological activity represents the conscious state of emptiness in meditation. Front Psychol 2014;5:99.
Travis F, Haaga DA, Hagelin J, Tanner M, Arenander A, Nidich S, et al.
A self-referential default brain state: Patterns of coherence, power, and eLORETA sources during eyes-closed rest and Transcendental Meditation practice. Cogn Process 2010;11:21-30.
Kaur C, Singh P. EEG derived neuronal dynamics during meditation: Progress and challenges. Adv Prev Med 2015;2015:614723.
Fields RD. A new mechanism of nervous system plasticity: Activity-dependent myelination. Nat Rev Neurosci 2015;16:756-67.
Kral TR, Imhoff-Smith T, Dean DC, Grupe D, Adluru N, Patsenko E, et al.
Mindfulness-Based Stress Reduction-related changes in posterior cingulate resting brain connectivity. Soc Cogn Affect Neurosci 2019;14:777-87.
Sauseng P, Klimesch W, Schabus M, Doppelmayr M. Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. Int J Psychophysiol 2005;57:97-103.
Shackman AJ, McMenamin BW, Maxwell JS, Greischar LL, Davidson RJ. Right dorsolateral prefrontal cortical activity and behavioral inhibition. Psychol Sci 2009;20:1500-6.
Aftanas LI, Golocheikine SA. Human anterior and frontal midline theta and lower alpha reflect emotionally positive state and internalized attention: High-resolution EEG investigation of meditation. Neurosci Lett 2001;310:57-60.
Takahashi T, Murata T, Hamada T, Omori M, Kosaka H, Kikuchi M, et al.
Changes in EEG and autonomic nervous activity during meditation and their association with personality traits. Int J Psychophysiol 2005;55:199-207.
Kim DK, Rhee JH, Kang SW. Reorganization of the brain and heart rhythm during autogenic meditation. Front Integr Neurosci 2014;7:109.
Seitzman BA, Gratton C, Laumann TO, Gordon EM, Adeyemo B, Dworetsky A, et al.
Trait-like variants in human functional brain networks. Proc Natl Acad Sci U S A 2019;116:22851-61.
Parkinson TD, Kornelsen J, Smith SD. Trait mindfulness and functional connectivity in cognitive and attentional resting state networks. Front Hum Neurosci 2019;13:112.
Knyazev GG, Savostyanov AN, Levin EA. Alpha oscillations as a correlate of trait anxiety. Int J Psychophysiol 2004;53:147-60.
Gola M, Magnuski M, Szumska I, Wróbel A. EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects. Int J Psychophysiol 2013;89:334-41.
Dor-Ziderman Y, Berkovich-Ohana A, Glicksohn J, Goldstein A. Mindfulness-induced selflessness: A MEG neurophenomenological study. Front Hum Neurosci 2013;7:582.
Engel AK, Fries P. Beta-band oscillations-signalling the status quo? Curr Opin Neurobiol 2010;20:156-65.
Gola M, Kamiński J, Brzezicka A, Wróbel A. β band oscillations as a correlate of alertness-changes in aging. Int J Psychophysiol 2012;85:62-7.
Basharpoor S, Heidari F, Molavi P. EEG coherence in theta, alpha, and beta bands in frontal regions and executive functions. Appl Neuropsychol Adult 2021;28:310-7.
Matiz A, Crescentini C, Fabbro A, Budai R, Bergamasco M, Fabbro F. Spontaneous eye movements during focused-attention mindfulness meditation. PLoS One 2019;14:e0210862.
Cahn BR, Delorme A, Polich J. Occipital gamma activation during Vipassana meditation. Cogn Process 2010;11:39-56.
Tei S, Faber PL, Lehmann D, Tsujiuchi T, Kumano H, Pascual-Marqui RD, et al.
Meditators and non-meditators: EEG source imaging during resting. Brain Topogr 2009;22:158-65.
Gao J, Fan J, Wu BW, Zhang Z, Chang C, Hung YS, et al.
Entrainment of chaotic activities in brain and heart during MBSR mindfulness training. Neurosci Lett 2016;616:218-23.
Oshino S, Kato A, Wakayama A, Taniguchi M, Hirata M, Yoshimine T. Magnetoencephalographic analysis of cortical oscillatory activity in patients with brain tumors: Synthetic aperture magnetometry (SAM) functional imaging of delta band activity. Neuroimage 2007;34:957-64.
Division of Yoga and Life Sciences, Swami Vivekananda Yoga Anusandhana Samsthana, Bengaluru, Karnataka
Source of Support: None, Conflict of Interest: None
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
[Table 1], [Table 2], [Table 3]