Neural Dynamics of Heartfulness Meditation: EEG Alpha Modulation Across Experience Levels

Abstract

Background

Electroencephalography (EEG) studies consistently associate alpha-band oscillations with relaxation, internalized attention, and sensory disengagement during meditation. However, limited evidence exists on how Heartfulness Meditation (HM), particularly its unique transmission phases, modulates alpha activity across different experience levels.

Purpose

This study investigated experience-dependent modulation of EEG alpha-band power during multiple phases of HM, with a specific focus on transmission and post-meditation periods.

Method

Thirty-three healthy adults were categorized as long-term meditators (LTMs; n = 12), short-term meditators (STMs; n = 11), and non-meditating controls (CGs; n = 10). High-density EEG (129 channels) was recorded across seven consecutive five-minute phases: baseline, meditation (M1, M2), transmission (T1, T2), and post-rest (P1, P2). EEG data were preprocessed using RANSAC-based bad-channel detection and independent component analysis. Alpha power (8–12 Hz) was computed using Welch’s method and analyzed using linear mixed-effects models with false discovery rate correction.

Results

A significant Group × Phase × Region interaction (p_FDR < 0.05) indicated experience- and phase-dependent alpha modulation. Both LTMs and STMs exhibited higher alpha power than controls, particularly in frontal, parietal, and occipital regions during meditation and post-meditation phases. Effect sizes ranged from small to moderate (Cohen’s d = 0.34–0.70). Notably, STMs showed alpha enhancements comparable to LTMs during early meditation.

Conclusion

HM induces region- and phase-specific increases in alpha-band EEG activity, reflecting enhanced internal attention and sensory disengagement. Even short-term practice produces measurable neural changes, underscoring the potential neuroplastic effects of HM.

Introduction

Electroencephalography (EEG) offers millisecond-level temporal resolution of neural activity, making it a vital tool in cognitive neuroscience for studying brain oscillations. ¹ Decomposition of EEG signals into frequency bands enables investigation of brain states associated with cognition, emotion, and consciousness. The alpha band (8–12 Hz) is especially significant and is frequently linked to relaxed alertness, internalized attention, and sensory disengagement.^{2, 3} Consequently, alpha oscillations serve as key markers in meditation research.

However, EEG recordings are susceptible to artifacts from eye blinks, muscle movements, and electrical interference. Rigorous preprocessing methods—including bandpass filtering, spatial interpolation, and Independent Component Analysis (ICA)—are essential to isolate genuine neural signals.^4–7

A growing body of evidence suggests that meditation enhances alpha power, interpreted as reflecting improved attentional control and reduced cognitive effort.^7–9 Importantly, even brief meditation training can induce measurable neurophysiological changes, indicative of early neural plasticity.^10–12

Heartfulness Meditation and Neural Correlates

Heartfulness Meditation involves subjective experiences described by practitioners as energy transmission or enhanced energetic attunement, which we operationalize here as reported phases of heightened meditative reception. Emerging studies indicate that HM modulates alpha and theta oscillations, thereby supporting enhanced emotional and attentional regulation.^{6, 13, 14} Long-term HM practitioners exhibit structural and functional brain adaptations, such as increased grey matter volume and altered alpha activity within networks related to memory and emotion.^{15, 16}

Heartfulness Meditation was selected for this study due to its unique energetic transmission practice, which distinguishes it from other meditation traditions. This transmission component is theorized to facilitate deeper meditative states and corresponding electrophysiological effects not widely studied in previous EEG research.

Physiological benefits of HM include increased heart rate variability (HRV), which reflects enhanced parasympathetic function and greater stress resilience.^{11, 13} Parallel increases in theta-band activity—a marker of deeper meditative states—have been observed in HM as well as related traditions like Sudarshan Kriya and Vipassana.^17–19 Distinct meditation techniques elicit specific electrophysiological signatures associated with attentional and emotional processes.^{20, 21}

Despite these advances, limited research has examined alpha-band modulation across varying meditation experience levels, particularly focusing on the neural correlates of the HM transmission phase using advanced quantitative approaches. This gap highlights the need for rigorous EEG preprocessing and appropriate statistical models designed for repeated-measures data.

Aims and Hypotheses

This study aims to elucidate the influence of Heartfulness Meditation on alpha-band EEG dynamics among long term meditators (LTM), short-term meditators (STM), and non-meditating controls (CG) across seven meditation-related phases: Baseline, Meditation (M1, M2), Transmission (T1, T2), and Post-rest (P1, P2). EEG preprocessing utilized a validated pipeline incorporating RANSAC-based bad channel detection and Independent Component Analysis (ICA), with alpha power estimated using Welch’s method.^{1, 4, 5} Group and phase-specific effects were assessed using linear mixed-effects models complemented by nonparametric post hoc analyses.^21–23

Based on current evidence, we hypothesize that:

• Both meditator groups will exhibit greater alpha power than controls, especially during meditation and post-rest phases, reflecting enhanced attentional regulation.

• Long-term meditators will demonstrate more extensive and consistent alpha increases across all phases, consistent with experience-dependent neuroplasticity.

• Alpha power enhancements will be most prominent over frontal, parietal, and temporal regions implicated in attention, emotion, and memory processing.

This approach aims to advance understanding of meditation-induced neural oscillatory changes and their significance for cognitive and clinical neuroscience.^{7, 24}

Methods

Participants

A total of 33 healthy individuals participated in the study, grouped as long-term meditators (LTM, n = 12), short-term meditators (STM, n = 11), and non-meditating controls (CG, n = 10). Long-term meditators (LTMs) were defined as individuals with a minimum of 2 years of continuous HM practice, averaging 30 to 45 minutes daily (approx. 3.5 to 5.5 hours per week). Short-term meditators (STMs) had between 6 months and less than 2 years of experience, with average practice times varying between 2 to 3 hours per week, indicating they are not complete novices but have substantial meditation exposure. Controls (CG) had less than 6 months of meditation experience and served as non-meditating comparators. All participants were screened to exclude histories of neurological or psychiatric disorders and provided written informed consent. Participants were screened to exclude involvement in other contemplative practices, consumption of caffeine on the test day, or poor sleep quality prior to testing, as all these factors can confound EEG measures. The study protocol was approved by the Institutional Ethics Committee (approval number: RES/IECSVYASA/164/1/2020).

Sample Size Justification

The overall sample of 33 participants corresponds to typical sizes used in EEG meditation research, particularly in within-subjects designs. Each participant provided repeated measurements across multiple brain regions and experimental phases, enhancing the statistical power of the linear mixed-effects model (LMM). The sample size of 33 participants is typical in EEG meditation research with repeated measures designs. Post hoc power analyses indicated sufficient power to detect moderate to large group and phase effects but were underpowered for small effects, warranting cautious interpretation.

All inferential analyses included effect sizes and post hoc power estimates. In addition, nonparametric tests and false discovery rate (FDR) correction were applied to control for both Type I and Type II errors. Although the findings remain exploratory given the sample size, the statistical approach allows for a cautious yet meaningful interpretation of group- and phase-level effects.

Table 1 summarizes participants’ demographic and background data.

Table 1. Participant Characteristics.

Variables	LTM (n = 12)	STM (n = 11)	CG (n = 10)
Gender (n) Male Female	8 4	7 4	6 4
Age (years), mean ± SD Male female	33.0 ± 5.0 32.5 ± 6.0	31.0 ± 4.8 29.5 ± 5.2	30.5 ± 5.2 28.5 ± 5.8
Total meditation experience (h)	1000 ± 500	500 ± 300	N/A
Education Undergraduate postgraduate higher education	4 (33%) 8 (67%) –	5 (45%) 6 (55%) –	3 (30%) 6 (60%) 1 (10%)
Socioeconomic status Lower middle higher	2 (17%) 7 (58%) 3 (25%)	2 (18%) 6 (55%) 3 (27%)	2 (20%) 5 (50%) 3 (30%)

Note: LTM = Long-term meditators, STM = Short-term meditators, CG = Control group, SD = Standard deviation, N/A = Not available.

Experimental Design

Each participant underwent a 35-minute EEG recording session consisting of seven consecutive 5-minute phases: Baseline (B), Meditation 1 (M1), Meditation 2 (M2), Transmission 1 (T1), Transmission 2 (T2), Post-rest 1 (P1), and Post-rest 2 (P2). During all phases, participants remained relaxed, minimized movement, and kept their eyes closed. This study focused on the “transmission phases” (T1 and T2), which are specific intervals during HM practice wherein participants report receiving an energetic transmission from an experienced meditator or expert. These phases were operationalized as time-locked segments marked within the EEG recording session. It is hypothesized that transmission phases modulate neurophysiological activity, particularly alpha-band oscillations, reflecting changes in attentional and interoceptive processes. The experimental timeline and phase structure are summarized in Table 2.

Table 2. Summary of the Experimental Phases and Durations.

Baseline	Meditation		Transmission		Post Resting
B 5 min	M1 5 min	M2 5 min	T1 5 min	T2 5 min	P1 5 min	P2 5 min
Total duration: 35 minutes

Heartfulness Meditation

Heartfulness Meditation (HM) is a modern adaptation of Raja Yoga, designed to integrate meditative practices into daily life. It comprises five core elements: relaxation, cleaning, prayer, meditation, and yogic transmission.

The relaxation step helps calm the body and mind while channelling energy inward, replacing heaviness with a sense of lightness. The cleaning process, practiced in the evening, purifies the mind and body by removing impressions accumulated during the day. Silent prayer before sleep nurtures a deeper inner connection and reinforces one’s life purpose. Meditation, ideally performed early in the morning, focuses on the inner light within the heart. ²³

A distinctive feature of Heartfulness is Pranahuti (yogic transmission), described as a subtle “divine energy from the Source” that supports personal transformation. Rediscovered by Shri Ram Chandra (Lalaji), this technique involves an energetic transmission during meditation, believed to facilitate deeper meditative absorption and emotional balance.

Unlike traditional meditation methods that rely solely on personal concentration, yogic transmission operates through a non-verbal energetic influence typically provided by a Guru. This process is thought to enhance the meditator’s receptivity, fostering introspection and promoting a calm, centred state more effectively. Such profound meditative experiences may correspond to measurable neural patterns, potentially explaining the observed psychological and physiological benefits. ⁶

EEG Data Acquisition and Preprocessing

EEG signals were acquired using a 129-channel Electrical Geodesics Inc. (EGI) system, sampled at 250 Hz in accordance with the Geodesic Sensor Net configuration. ²⁵ Data preprocessing was performed using MNE-Python and began with re-referencing to the common average to reduce spatial bias. Faulty EEG channels were identified and removed using a RANSAC-based algorithm, which detects outlier electrodes by comparing observed signals to modelled data. Independent Component Analysis (ICA) was then applied for artifact correction. ICA components corresponding to ocular, muscular, or cardiac artifacts were identified based on a combination of temporal, spectral, and spatial characteristics, followed by a manual review by trained raters to ensure accurate artifact rejection. A high-pass filter at 1 Hz was applied to improve the separation of independent components during ICA.

Subsequently, a band-pass filter between 8 and 12 Hz was used to isolate alpha activity, and a 50 Hz notch filter was applied to remove line noise. Figure 1 provides an overview of the complete preprocessing workflow. Key parameters related to EEG data acquisition and signal processing are summarized in Table 3.

Figure 1. Preprocessing and Comparative Analysis Workflow for EEG Data (See also Table 3).

Table 3. EEG Data Acquisition and Preprocessing Parameters Detailing System Setup, Filtering, Artifact Removal, ICA, and Alpha Power Analysis.

Step	Parameter	Value
Data acquisition	EEG system type Electrode placement Digitized points Frequency resolution (Hz) Sampling rate (Hz) Number of channels Recording duration (seconds) Number of files	Electrical Geodesics, Inc. 129-channel 10–20 system 131 0.98 250 129 300 231
Filtering	High-pass filter Low-pass filter Notch filter	8 Hz 12 Hz 50 Hz
Artifact removal	RANSAC algorithm	Variance Threshold
ICA	Algorithm Number of components	FastICA 32
Band power analysis	Frequency band	Alpha (8–12 Hz)

Identification and Interpolation of Faulty Channels

Faulty EEG channels were identified using the RANSAC algorithm, which detects outlier electrodes by comparing fitted models to observed signal patterns. ²⁶ Channels consistently marked as outliers were interpolated using values from neighbouring electrodes to preserve spatial accuracy and continuity of scalp activity.

Independent Component Analysis (ICA) for Artifact Removal

Artifact correction was performed using Independent Component Analysis (ICA). EEG data (X) were modelled as linear mixtures of independent sources (S) through an unknown mixing matrix (A), expressed as:

$$X=A\times S$$

The estimated mixing matrix (W) was then used to recover the independent sources:

$$S=W\times X$$

Components corresponding to ocular, muscular, or cardiac artifacts were identified based on their temporal, spectral, and topographical features. These components were removed by setting their contributions to zero, after which the EEG signals were reconstructed. The cleaned data were visually inspected, and any epochs with voltage fluctuations exceeding ±100µV were excluded from further analysis.

Topographic distributions of the removed ICA components are shown in Figure 2, and the overall three-stage preprocessing workflow is illustrated in Figure 3.

Figure 2. Topographic Maps of ICA Components.

Note: Each plot shows spatial distributions of components highlighting potential artifacts and neural source.

Figure 3. Stages of EEG Data Processing: (a) Raw Data, (b) ICA Decomposition Isolating Artifacts, and (c) Cleaned Data After Artifact Removal.

Calculation of Alpha Power

After artifact correction, EEG data were segmented into overlapping epochs. Power spectral density (PSD) for each epoch was calculated using Welch’s method with a Hanning window. Spectral power was then integrated over the 8–12 Hz range to obtain alpha power for each epoch. Alpha power values were averaged across epochs and channels within each region of interest (frontal, parietal, occipital, etc.). The resulting regional alpha power served as the dependent variable for subsequent statistical analyses examining group, phase, and region-specific effects.

Statistical Analysis

Alpha power values were scaled by 10 ¹² to enhance numerical stability and facilitate model convergence. Specifically, raw alpha power values were multiplied by 1 × 10 ¹² and z-normalized within each participant to minimize inter-subject variability. The resulting variable, Scaled Alpha Power, served as the dependent measure in all analyses.

To evaluate the effects of Group (LTM, STM, CG), Phase (B, M1, M2, T1, T2, P1, P2), and Region (frontal, central, parietal, temporal, occipital), a linear mixed-effects model (LMM) was implemented in Python using the statsmodels library. ²⁷ Each participant was modelled with a random intercept to account for repeated measures.

The full model was specified as follows:

$${\text{Alpha scaled }}_{ijk}={\beta }_0+{\beta }_1\cdot {\text{Group }}_i+{\beta }_2\cdot {\text{Phase }}_j+{\beta }_3\cdot {\text{Region }}_k+{\beta }_4\cdot {(\text{Group }\times {\text{Phase }}_{ij}+{\beta }_5\cdot \left(\text{ Group }\times {\text{ Region }}_{ik}+{\beta }_6\cdot {(\text{ Phase }\times \text{ Region })}_{jk}+{\beta }_7\cdot (\text{Group }\right.\times \text{Phase ×Region })}_{ijk}+u_i+{ϵ}_{ijk}$$

where uᵢ represents the random intercept for participant i, and εᵢⱼₖ denotes the residual error.

A linear mixed-effects model (LMM) was employed to assess the effects of Group, Phase, and Brain Region on alpha power, with random intercepts to account for subject-level variability. Model assumptions, including normality of errors and homoscedasticity, were assessed through standard diagnostic procedures to ensure validity of inference. Robust covariance estimators were applied to mitigate potential model assumption violations. Due to small sample sizes and non-normal data distribution, post hoc group comparisons used Mann-Whitney U tests.

To determine the significance of the three-way interaction, a likelihood ratio test (LRT) compared the full model to a reduced model excluding that interaction.

The test statistic was calculated as:

$${\chi }^2=2\times \left(\log L_{\text{full }}-\log L_{\text{reduced }}\right)$$

and evaluated using a chi-square distribution with degrees of freedom equal to the number of parameters removed.

For all pairwise comparisons, both uncorrected p values $P_{uncorrected}$ and Benjamini–Hochberg FDR-adjusted p values $p_{FDR}$ were reported. Statistical significance was defined as $p_{FDR}$ < .05. When $P_{uncorrected}$ and $p_{FDR}$ were equal, it indicated that the FDR correction did not alter significance.

If the three-way interaction was significant ( $p_{FDR}$ < .05), post-hoc pairwise group comparisons were conducted within each Phase–Region combination using the nonparametric Mann–Whitney U test, suitable for small samples and non-normal data distributions.

For each test, the following statistics were reported:

1. Mann–Whitney U statistic and uncorrected p value $P_{uncorrected}$

2. FDR-adjusted p value $p_{FDR}$

3. Standardized effect size:

   $$r=\frac{\left|Z\right|}{\left(n_1+n_2\right)}$$
4. Approximate Cohen’s d (when applicable):

   $$d=\frac{2r}{\sqrt{\left(1-r^2\right)}}$$

5. Statistical power based on observed effect size and sample size

Group effects were summarized across phases and regions and visualized in figures highlighting significant results (p_FDR< .05). Significant group-wise differences across phases and regions were summarized to illustrate consistent trends in alpha power modulation, with figures marking significance at p_FDR < .5.

Results

Topographical Analysis Between Groups

Long-term Meditators (LTMs)

EEG topographic maps for long-term meditators (LTMs) (Figure 4a) indicate that ongoing Heartfulness Meditation is associated with widespread changes in brain activity, particularly in areas involved in attention regulation, executive control, and alertness. Elevated alpha power persisted beyond the meditation periods, most prominently during the post-rest phases, suggesting enduring neuroplastic adaptations. Distinct phase-specific activation patterns were observed, reflecting enhanced cognitive flexibility—the brain’s ability to efficiently transition between different states of awareness. Collectively, these findings suggest that long-term meditation practice fosters both neural stability and adaptability.

Figure 4. Topographic Maps of Alpha Power Distribution Across Groups and Phases: (a) Long-term Meditators, (b) Short-term Meditators, and (c) Controls.

Note: Phases (left to right) include baseline, Meditation 1, Meditation 2, Transmission 1, Transmission 2, Post-resting 1 and Post-resting 2.

Short-term Meditators (STMs)

Topographic maps for short-term meditators (STMs) (Figure 4b) reveal dynamic, phase-dependent fluctuations in alpha power. During meditation phases, alpha power decreased in frontal and parietal regions, possibly indicating reduced cognitive elaboration and an inwardly directed focus. During the transmission phases, however, alpha activity increased again, especially in regions associated with sensory integration and attentional processing. This rebound pattern may reflect an active neural response to transmission-induced states. Although these effects were less extensive than those observed in LTMs, the results demonstrate that even short-term Heartfulness practice can elicit measurable, though transient, neural modulation.

Control Group

In contrast, the control group (CG; Figure 4c) exhibited relatively uniform alpha distributions across all experimental phases. Only minor variations were observed, particularly during post-rest periods, and overall topographic modulation remained limited. Frontal and central regions showed consistently elevated activity, possibly reflecting engagement with the experimental environment in the absence of meditative training. These subtle fluctuations may represent passive relaxation or acclimatization rather than meditation-specific neural changes. The absence of strong phase-dependent modulation in the CG underscores the influence of meditation experience in generating the distinct neural patterns observed in both LTM and STM participants.

Linear Mixed-effects Model

To examine how meditation experience and experimental phase influenced alpha power across brain regions, a linear mixed-effects model (LMM) with participant-level random intercepts was applied. Fixed effects included Group (LTM, STM, CG), Phase (B, M1, M2, T1, T2, P1, P2), and Region (frontal, central, parietal, temporal, occipital), with parameter estimation performed using restricted maximum likelihood (REML). The model converged successfully, with random intercept and residual variances of 8.063 and 14.50, respectively, indicating a good model fit.

A likelihood ratio test (LRT) comparing the full model to a reduced model excluding the Group × Phase × Region interaction demonstrated that the three-way interaction significantly improved model fit (χ² = 239.97, df = 132, $P_{uncorrected}$ = 2.81 × 10–8; $p_{FDR}$ identical due to a single comparison). Post-hoc pairwise comparisons for each Phase × Region combination was performed using Mann Whitney U tests. Both uncorrected ( $P_{uncorrected}$ ) and FDR-adjusted ( $p_{FDR}$ ) p values were calculated to control for multiple comparisons. Effect sizes (Cohen’s d, estimated from rank-biserial r) and statistical power were also computed.

While main effects of Group and Phase were not statistically significant, a robust main effect of region was observed (p < .001), reflecting systematic variation in alpha power across cortical areas. The significant three-way interaction indicated distinct region- and phase-specific modulation of alpha activity in LTMs and STMs relative to controls. For example, during Phase M1, both meditator groups exhibited increased alpha power in the left central and occipital regions compared to controls. In post-meditation phases (P1 and P2), alpha power in the right parietal region remained elevated in both STM and LTM participants. These differences were supported by medium-to-large effect sizes (Cohen’s d > 0.4) and high statistical power (>0.8).

Table 4 presents significant results ( $p_{FDR}$ < .05), including both uncorrected and FDR-adjusted p values for full transparency.

Table 4. Significant Group Effects in Region-by-phase LMMs ( $p_{FDR}$ < .05). Estimates Reflect Fixed Effects for Group Contrasts Within Each Region × Phase Combination. Both Uncorrected and FDR-adjusted p Values are Reported.

Phase	Region	Group Comparison	Estimate	SE	t	Cohen’s d	Power	puncorrected	pFDR
M1	Left Central	CG vs LTM	2.182	0.946	2.306	0.40	0.801	0.018	0.021
M1	Left Central	CG vs STM	2.043	0.974	2.097	0.37	0.784	0.031	0.036
M1	Occipital	CG vs LTM	2.558	1.296	1.974	0.34	0.758	0.043	0.048
M1	Occipital	CG vs STM	2.997	1.334	2.246	0.39	0.796	0.021	0.025
T2	Parietal	CG vs STM	–1.782	0.906	−1.967	0.33	0.752	0.043	0.049
P1	Right Parietal	CG vs LTM	2.668	0.935	2.854	0.50	0.889	0.003	0.004
P1	Right Parietal	CG vs STM	2.370	0.962	2.464	0.44	0.840	0.011	0.014
P2	Right Parietal	CG vs STM	1.920	0.959	2.001	0.36	0.773	0.039	0.045

Phase × Region and Group × Phase × Region Interactions

Significant two-way and three-way interactions provided detailed insight into the spatiotemporal evolution of alpha rhythms across the meditation protocol.

• Phase × Region: Several cortical regions showed phase-specific changes in alpha power. For instance, during M1, alpha power significantly decreased in left central and parietal regions ( $p_{FDR}$ < .05), while during T2, reductions were observed in occipital and right parietal cortices ( $p_{FDR}$ < .01). These results demonstrate temporally dynamic and regionally specific modulation of alpha activity throughout the experimental phases.

• Group × Phase × Region: This interaction indicated that the effects of meditation experience were dependent on both phase and brain region. During M1, both LTM and STM participants exhibited significantly higher alpha power in left central and occipital regions compared to controls ( $p_{FDR}$ < .05). In the right parietal cortex, increases were largest for LTM participants (β = 2.88, $P_{uncorrected}$ = .002, $p_{FDR}$ = .004) and STM participants (β = 2.55, $P_{uncorrected}$ = .007, $p_{FDR}$ = .011). These effects persisted into the post-meditation phases (P1 and P2), particularly in right parietal and frontal cortices, indicating sustained neural modulation following meditation.

Post-hoc Nonparametric Comparisons

To further explore significant LMM interactions, post-hoc pairwise group comparisons for each Phase × Region combination was conducted using the Mann–Whitney U test, selected for its robustness to non-normal distributions and suitability for modest sample sizes. For each comparison, both uncorrected $P_{uncorrected}$ and FDR-corrected ( $p_{FDR}$ ) p values were calculated to control the false discovery rate.

Significant differences were primarily observed between controls and both meditator groups, particularly during early meditation (M1) and post-meditation (P1, P2) phases. STM participants consistently demonstrated higher alpha power in parietal and occipital cortices across these phases. Group trajectories for six key brain regions are shown in Figure 5, with asterisks indicating statistically significant post-hoc differences ( $p_{FDR}$ < .05). Figure 6 displays the nine largest effect-size comparisons, with detailed statistics summarized in Table 5.

Figure 5. Mean Scaled Alpha Power Across Meditation Phases for Six Representative Brain Regions.

Notes: Solid, dashed, and dotted lines represent CG, STM, and LTM, respectively.

* indicate statistically significant post-hoc group differences (p_FDR < .05).

Figure 6. Top Nine Region × Phase × Group Comparisons in Scaled Alpha Power with the Largest Effect Sizes (r).

Notes: Violin plots depict distributions for CG, STM, and LTM.

Asterisks mark significant differences (p_FDR < .05) favouring STM or LTM over CG.

Table 5. Region × Phase × Group Comparisons with Effect Size (r), Cohen’s d, and Statistical Power. All Comparisons are Statistically Significant After Benjamini–Hochberg FDR Adjustment (p_FDR < .05).

Region	Phase	Group 1 vs 2	Effect size (r)	Cohen’s d	Power	Direction
Frontal	M1	CG vs LTM	0.183	0.372	0.911	LTM > CG
	M1	CG vs STM	0.195	0.398	0.926	STM > CG
	P1	CG vs LTM	0.190	0.388	0.917	LTM > CG
	T1	CG vs LTM	0.152	0.308	0.781	LTM > CG
	T1	CG vs STM	0.165	0.334	0.799	STM > CG
	P2	CG vs LTM	0.156	0.315	0.787	LTM > CG
	T2	LTM vs STM	0.154	0.312	0.814	STM > LTM
	T2	CG vs LTM	0.208	0.426	0.966	LTM > CG
Right Frontal	M1	CG vs STM	0.206	0.420	0.890	STM > CG
	P1	CG vs LTM	0.201	0.411	0.882	LTM > CG
	P1	CG vs STM	0.229	0.471	0.935	STM > CG
	P2	CG vs LTM	0.219	0.449	0.936	LTM > CG
	P2	CG vs STM	0.189	0.386	0.816	STM > CG
	T2	CG vs LTM	0.184	0.374	0.840	LTM > CG
Left Frontal	M1	CG vs STM	0.209	0.428	0.902	STM > CG
	P1	CG vs LTM	0.183	0.371	0.811	LTM > CG
	P1	CG vs STM	0.185	0.377	0.794	STM > CG
Parietal	M1	CG vs STM	0.308	0.646	1.000	STM > CG
	M1	CG vs LTM	0.199	0.405	0.935	LTM > CG
	P1	CG vs STM	0.215	0.440	0.942	STM > CG
	M2	CG vs STM	0.218	0.446	0.958	STM > CG
	T1	CG vs STM	0.245	0.506	0.983	STM > CG
	T1	LTM vs STM	0.271	0.563	0.998	STM > LTM
	T1	CG vs STM	0.245	0.506	0.983	STM > CG
	P2	CG vs STM	0.195	0.397	0.893	STM > CG
Right Parietal	M1	CG vs STM	0.216	0.443	0.920	STM > CG
	T1	LTM vs STM	0.281	0.585	0.996	STM > LTM
Left Central	M1	CG vs STM	0.241	0.496	0.948	STM > CG
	M1	CG vs LTM	0.204	0.416	0.879	LTM > CG
	P1	CG vs STM	0.194	0.395	0.795	STM > CG
	M2	CG vs STM	0.188	0.382	0.790	STM > CG
	T1	CG vs STM	0.225	0.462	0.905	STM > CG
	T1	LTM vs STM	0.227	0.466	0.938	STM > LTM
	P2	CG vs STM	0.222	0.456	0.897	STM > CG
Right Central	T1	LTM vs STM	0.194	0.395	0.844	STM > LTM
Occipital	M1	CG vs STM	0.329	0.698	0.891	STM > CG
	M2	CG vs STM	0.294	0.616	0.804	STM > CG
	P2	CG vs STM	0.314	0.662	0.841	STM > CG

Overall, these post-hoc analyses confirm that both short- and long-term meditation are associated with regionally specific increases in alpha power, most prominently in posterior regions involved in sensory integration and attentional processing.

Discussion

This study evaluated the effects of Heartfulness Meditation (HM) on alpha-band EEG activity across brain regions and meditation phases in long-term meditators (LTM), short-term meditators (STM), and non-meditating controls (CG). While main effects for Group and Phase were not individually significant, a robust three-way interaction (Group × Phase × Region) was observed. This finding suggests that alpha modulation depends on both meditation experience and cortical region. ²⁸ Post-hoc FDR-adjusted nonparametric analyses showed that meditators—particularly STM participants—displayed significantly greater alpha power during early meditation and post-meditation phases, especially in parietal, occipital, and frontal cortices (Figures 5, 6, and Table 5). These results align with previous research indicating that both short-term and long-term Heartfulness meditators exhibit higher alpha power compared to non-meditators, reflecting enhanced relaxation, attentional control, and emotional regulation. Long-term meditators tended to show stronger and more consistent alpha power increases, but the STM group’s elevated alpha during meditation underscores the role of Heartfulness practice in promoting calm and focused neural states. These enhanced alpha patterns likely reflect the neural correlates of meditative states facilitated by Heartfulness meditation, highlighting its potential benefits for cognitive and affective function. ²⁹ The exclusive focus on alpha-band oscillations reflects their established role as neural markers of relaxation, internalized attention, and sensory disengagement during meditation. While theta, beta, and gamma bands also contribute to meditative states, alpha modulation offers a well-validated target for investigating HM. Future studies will expand analyses to these additional bands and connectivity metrics.

Frontal and Central Regions: Transient Effects of Inward Attention

Increased alpha power in frontal and central regions during Meditation 1 (M1) and Post-rest 1 (P1) for meditators—but not at baseline—indicates a transient, state-dependent neural change rather than a permanent trait effect. Elevated frontal alpha is widely recognized as a neural signature of reduced processing of external sensory inputs, coupled with enhanced internal attention. This pattern aligns with the introspective and focused aspects central to Heartfulness Meditation practice.^{2, 24} The increased frontal alpha supports the notion that meditators achieve a state of wakeful relaxation and internal focus during meditation sessions, facilitating calm yet attentive mental states. These findings are consistent with studies reporting that Heartfulness meditators exhibit higher alpha power associated with mindfulness, lower anxiety, and improved cognitive control, particularly in frontal areas linked to executive function and attention regulation.^29–31

Parietal and Occipital Regions: Sustained Posterior Alpha After-effects

The largest group differences appeared in parietal and occipital cortices, particularly among STMs, who showed greater alpha power during M1, Transmission 1 (T1), and P1 compared to controls. Right parietal alpha remained elevated into the later post-meditation phase (P2), indicating persistent neural engagement beyond the meditation session. These findings are consistent with prior studies suggesting that posterior alpha increases reflect sensory disengagement and deeper absorption during meditation.^{32, 33}

Temporal Regions: Modulation of Auditory Processing

STMs also exhibited increased alpha in the right temporal cortex, though to a lesser extent than in parietal and occipital regions. Elevated temporal alpha may indicate reduced auditory sensitivity and enhanced internal focus, potentially related to auditory cues provided during HM practice. ³⁴ This neural pattern may facilitate gating or filtering of external auditory input, supporting concentrated internal awareness. Previous research has linked increased temporal alpha to suppression of external auditory distractions during focused attention, suggesting that temporal alpha enhancements in STMs reflect neurophysiological mechanisms underlying inward attention during meditation. ³⁵ This finding underlines how HM facilitates sensory gating processes, particularly in the auditory domain, enhancing the practitioner’s capacity for inward focus and minimizing outside distractions.

Although the enhanced alpha activity observed may underlie therapeutic benefits of HM, our cross-sectional design and modest sample size limit clinical generalization. These findings should be considered preliminary, pending larger and longitudinal trials.

State Dependence and Experience-related Adaptation

Alpha power increases were mainly observed during and after meditation, with minimal baseline effect, emphasizing that these changes are state-dependent rather than permanent neural traits. Interestingly, STM participants sometimes showed alpha enhancements equal to or exceeding those of LTMs, indicating that even brief HM practice can induce measurable neurophysiological adaptation, consistent with prior reports of early-stage neural plasticity in novice meditators.^{36, 37} Our findings predominantly indicate state-dependent modulations of alpha power during transmission phases, reflective of acute attentional engagement. However, the enhanced alpha responses in long-term meditators suggest experience-dependent neuroplastic adaptations, representing trait-level changes in neural processing.

Overall, Heartfulness Meditation induces distinct, state-dependent alpha modulation across cortical regions, reflecting enhanced inward attention, sensory disengagement, and auditory gating. These neurophysiological patterns support improved relaxation, focus, and emotional regulation, underscoring HM’s potential as a non-invasive practice for cognitive and affective well-being in both novice and experienced practitioners.

Limitations and Future Directions

This study has several limitations. The cross-sectional design limits causal interpretation, highlighting the need for longitudinal studies to track neural changes over time and establish a direct link between HM practice and brain activity. The modest sample size, although consistent with similar EEG studies, may limit statistical power and generalizability, warranting replication with larger cohorts. Additionally, the exclusive focus on alpha-band activity overlooks potential contributions from other frequency bands such as theta, beta, and gamma, which are relevant in meditation research. The absence of subjective or phenomenological data limits understanding of how neural changes relate to practitioners’ experiences.

Despite these limitations, the study benefits from a rigorous EEG preprocessing pipeline, including RANSAC-based bad channel detection and ICA, ensuring high-quality data. The well-structured experimental design captures dynamic changes across meditation phases and brain regions, and the use of linear mixed-effects models with appropriate corrections strengthens the analysis of complex interactions. Comparing long-term and short-term meditators provides valuable insights into experience-dependent neural adaptations.

Future research should employ longitudinal designs, larger samples, and explore additional frequency bands and connectivity patterns. Additionally, source localization and broader frequency band analyses were not included but are planned for future investigations. Longitudinal designs will be critical to ascertain causality and neuroplasticity associated with HM. Incorporating source localization, multi-modal imaging, and subjective experience measures will further elucidate the neural mechanisms underlying meditation and its cognitive and clinical implications.

Conclusion

This study demonstrates that Heartfulness Meditation modulates alpha-band EEG activity in a manner dependent on meditation experience and cortical region. Both short- and long-term meditators exhibited increased alpha power during meditation and rest, particularly in posterior and frontal areas associated with sensory disengagement and attentional regulation. These neural effects were transient and phase-specific but evident even in novice practitioners, suggesting early neuroplastic changes. By combining rigorous EEG analysis with advanced statistical modelling, these findings enhance understanding of the neural mechanisms of contemplative practices and highlight the potential cognitive and therapeutic benefits of Heartfulness Meditation.

Authors’ Contribution

Gyaneshwar Singh contributed to conceptualization, methodology, investigation, formal analysis, software development, resource management, validation, visualization, and drafted and edited the manuscript. Saleema J S contributed to conceptualization, methodology, investigation, formal analysis, software development, resource management, validation, visualization, and co-authored and edited the manuscript. Krishna Dwivedi contributed to data curation and visualization and participated in manuscript review and editing. Deepeshwar Singh assisted with data curation, visualization, and manuscript review and editing.

Statement of Ethics

This study received approval from the Institutional Ethics Committee (approval number RES/IEC-SVYASA/164/1/2020).

Data Availability

Data supporting the findings of this study are available upon reasonable request, subject to a formal data sharing agreement and approval by the consortium and executive committee.

Informed Consent

Written informed consent was obtained from all participants following a detailed explanation of study procedures.