EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach

Introduction: This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.

Methods: The study utilized data from 13 participants aged 24–58, which researchers obtained through an openly accessible OpenNeuro dataset.

Result: Examination of eventrelated potentials (ERPs) demonstrated that P300 amplitude showed significant growth when responding to oddball stimuli, which indicates increased attention allocation (p < 0.05). Spectral power analysis demonstrated an increase in frontal alpha and beta power during meditation while central theta power decreased, which suggests reduced cognitive load and enhanced internal focus. Meditation experience showed a statistical relationship with frontal alpha power, where r = 0.45 and p < 0.03. A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.

Conclusion: The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain–computer interfaces and mental health applications. The study of meditation’s effects on brain activity reveals its benefits for emotional regulation and concentration improvement. The research findings deliver strong evidence that meditation induces distinct neural modifications detectable through ERP and spectral analysis. The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain–computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.

1 Introduction

Scientific interest has surged regarding meditation’s impact on advanced cognitive functions, including consciousness, attention, and emotional regulation. Through sustained meditation practice, individuals can experience neuroplastic alterations in brain structure and function, affecting regions responsible for emotional regulation and attention (Lutz et al., 2008; Tang et al., 2015). Electroencephalography-based neurophysiological studies have detected distinct brain activity patterns linked to meditation, which appear specifically within alpha and theta frequency bands (Cahn and Polich, 2006). Scientific studies demonstrate that these oscillation patterns are linked to enhanced attentional control and working memory while reducing mind-wandering in meditative states (Cahn and Polich, 2006; Brandmeyer and Delorme, 2013; Tang et al., 2015). Electroencephalographic studies reveal meditation alters alpha and theta oscillations, which researchers associate with enhanced attentional focus and emotional equilibrium (Brandmeyer and Delorme, 2013).

The auditory oddball paradigm serves as a frequently employed technique to assess cognitive processes such as novelty detection and attention. This experimental approach involves presenting common standard tones interspersed with uncommon “oddball” tones randomly, producing a prominent event-related potential (ERP) component referred to as the P300 (Ebrahimzadeh et al., 2020). The amplitude of the P300 is generally greater for novel or relevant stimuli in the task, reflecting processes of stimulus evaluation and the allocation of attention (Ebrahimzadeh et al., 2019a). Several studies have reported that meditators exhibit greater P300 responses in cognitive tasks as reflected by the increased attentional engagement (Ebrahimzadeh et al., 2019b; Ebrahimzadeh et al., 2013). The allocation of attention in such paradigms engages frontal and parietal networks involved in top-down control (Baldauf and Desimone, 2014).

EEG studies using both ERP and spectral methods have provided insight into the brain mechanisms of meditation. EEG and neuroimaging studies have begun to converge on the underlying neural correlates of meditation. The study performed a meta-analysis of fMRI research showing that similar anterior cingulate cortex, insula, and prefrontal cortex activation was found for all types of meditation practice (Nikravan and Ebrahimzadeh, 2021).

For example, long-term meditators commonly show greater alpha power and decreased theta power during performance of tasks involving focused attention, compared to usual resting-state EEG profiles (Lomas et al., 2015). These findings suggest that practicing meditation may produce a distinct neurophysiological state, the defining features of which are improved cortical efficiency and decreased cortical susceptibility to stimulus distraction (Atchley et al., 2016). Recent advances in machine learning have made it possible to further investigate the EEG data, culminating in the ability to distinguish mental states based on their brain signatures. Supervised learning has turned out to be particularly successful in the classification of different cognitive conditions, such as resting, engagement in a task, or meditation focus (Lomas et al., 2015; Braboszcz et al., 2017) using models such as Random Forests and Support Vector Machines. These approaches not only confirm neurophysiological differences between states, but also assist in the development of brain–computer interface (BCI) and neurofeedback technology (Hosseini and Khalilzadeh, 2010).

The present investigation attempts to replicate and extend previous research by investigating EEG during meditation evoked by meditation and auditory oddball tasks. By analyzing the ERPs, calculating the spectral power, and by machine learning classification, we want to investigate whether and how meditation modulates the neural responses to auditory stimuli and whether or not EEG parameters can be used to discriminate different cognitive states. By combining classic neurophysiological recordings and computational methods, this study contributes to a better understanding of the effects of meditation on the brain and to the discussion of the possibility of tracking cognitive status on a moment-by-moment basis.

2 Methods

2.1 Experiment protocol

Data were collected at the Meditation Research Institute (MRI) of Rishikesh, India, with the supervision of Arnaud Delorme, Ph.D. Both the local MRI ethical committee in India and the Institutional Review Board at the University of California, San Diego issued ethical approval (IRB project #090731). Participants: 13 participants were sampled from a publicly available EEG dataset on OpenNeuro (Delorme, 2022). Subjects were chosen in case of the presence of a relatively clear EEG signal with minimal artifacts in order to ensure a reliable analysis. However, there is a possible sampling bias introduced in this selection criterion because the RF is chosen depending on the quality of the signal and not on a random basis of inclusion/exclusion. More specifically, the study excluded speakers with high head movements or eye-blink artifacts, which might deplete the ecological validity of the results, favoring speakers with less noisy or more stable EEG patterns.

Thirteen subjects were selected from a publicly accessible EEG dataset on the OpenNeuro (Delorme, 2022), considering the quality of a small number of artifact-free EEG recordings, which will result in clean data for analysis. A total of 13 participants (age range 24–58, of both genders and with various ethnic backgrounds) took part in the project. Participants were assigned a category of meditation experience level (inexperienced, novice: less than 1 year of meditation practice; intermediate: 1–4 years; experienced: 5 years or more) according to the self-reported number of years of regular meditative practice. There were 5 experienced, 3 intermediate, and 5 novice among the 13. These experience levels were used for exploratory correlations with EEG measures (e.g., alpha power). Baseline cognitive and emotional assessments, such as STAI, BDI, or PANAS, were not obtained because the dataset was limited, as mentioned in the limitations discussed below. More demographic and environmental details can be read in Table 1.

TABLE 1

Table 1. Demographic and environmental characteristics of participants in EEG study.

2.2 Experimental design and stimuli

Participants engaged in three sessions utilizing an auditory oddball paradigm, each lasting 13 min. A total of 750 tones were presented in these sessions, featuring different frequencies and types, which included standard tones, infrequent oddball tones, and distracting white noise bursts. Each sound was ramped in and out to guarantee a smooth onset and offset. Minor temporal jitter was introduced to the stimuli to avoid rhythmic predictability. Participants were instructed to respond just to oddball tones by pressing a key on a handheld keypad. This design aimed to generate neural responses linked to attention and novelty detection. Figure 1 presents a visual overview of the task structure, response instructions, and timeline.

FIGURE 1

Figure 1. Experimental setup of auditory oddball task: stimulus sequence and response overview.

2.3 Procedure

During data collection, participants sat in a darkened room with their eyes closed, seated on either a chair or a blanket conferring to individual preference. An intercom system facilitated communication between the experimenter and the participant.

2.4 EEG data acquisition

EEG signals were gathered using a BioSemi Active Two system at a sampling rate conducive to high-resolution neural data. Standard 10–20 caps were employed and adjusted based on individual head sizes. Stimuli were delivered via MATLAB using the Psychtoolbox. After recording, the EEG data were resampled and converted to EEGLAB format for further processing.

2.5 EEG pre-processing

A systematic preprocessing pipeline was implemented to ready the EEG data for analysis. Key steps involved importing channel and event information, re-referencing to a common average, and applying a high-pass filter to eliminate slow signal drift. The raw data underwent visual inspection, with noisy channels either removed or interpolated. Time segments affected by significant artifacts were excluded. Independent component analysis (ICA) was used thereafter to detect and remove artifacts such as eye blinks and muscle movements. EEG preprocessing included standard procedures: channel re-referencing, high-pass filtering at 1 Hz, visual inspection, bad channel interpolation, ICA artifact removal, and epoch rejection. The comprehensive preprocessing workflow is detailed in Table 2.

TABLE 2

Table 2. EEG pre-processing workflow: steps and descriptions.

On average, 2–5 independent components were rejected per participant following ICA decomposition. Artifact components were identified using a combination of automated and manual methods: spatial topography consistent with ocular activity (e.g., frontal maxima), temporal correlation with EOG channels (where available), and power spectrum analysis indicative of muscle artifacts or non-brain noise. The ICLabel plugin in EEGLAB was used to assist in component classification, with final rejection decisions confirmed through visual inspection. Only components with ≥90% probability of non-brain origin were removed.

2.6 Time–frequency analysis

To explore the temporal evolution of brain oscillations, this study performed time–frequency analysis using the continuous Morlet wavelet transform. This approach allows the decomposition of EEG signals into frequency-specific power changes over time, preserving both temporal and spectral resolution. Wavelet analysis was conducted for each participant and condition (meditation vs. cognitive task), using 3–40 Hz frequency range in logarithmic steps, across epochs of −200 to +800 ms around stimulus onset.

The EEGLAB (newtimef) function was used to compute event-related spectral perturbations (ERSPs) and inter-trial coherence (ITC) for frontal (Fz), central (Cz), and parietal (Pz) electrodes. Baseline normalization was applied using the −200 to 0 ms pre-stimulus interval. ERSP reflects stimulus-induced changes in power relative to baseline, while ITC measures phase consistency across trials. Time–frequency results were statistically evaluated using permutation testing with a 5% false discovery rate (FDR) correction.

2.7 Statistical analysis

To analyze neural responses to the auditory stimuli, statistical comparisons were conducted between standard and oddball conditions. The analyses concentrated on differences in ERP amplitude and latency to evaluate attentional engagement. Furthermore, spectral analysis was carried out to investigate activity in theta, alpha, and beta frequency bands, with a specific emphasis on frontal and central regions. Correlation analyses were performed to investigate the relationships between meditation experience and specific EEG features such as alpha power. These analyses shed light on how neural activity varied with the type of task and participant characteristics, as further elaborated in the results section.

All ERP waveforms were baseline-corrected using the pre-stimulus interval (−200 to 0 ms). To address the issue of multiple comparisons across time points and electrodes, FDR correction was applied at a threshold of q < 0.05. This procedure was implemented for both ERP and time–frequency analyses, ensuring control of type I error across repeated statistical tests.

2.8 Machine learning classification

A machine learning strategy was utilized to examine whether EEG-derived features could effectively differentiate between meditation and cognitive task states (Subramanian et al., 2018). The features analyzed included ERP characteristics and frequency band power values. A supervised Random Forest classifier was trained and tested with a twofold diagnosis for robustness and a twofold age group for generalization. For the fivefold cross-validation, the data were randomly divided into five subsets; a single subset was retained as the test data, and the remaining four subsets were used as training data. The model was then trained (LOSO strategy) on the EEG recordings of 12 subjects, and generalization performance was computed on the 13th held-out subject. This is a conservative estimate of how well the classifier is working, and mimics generalization to new, unseen participants in the real world. The classification performance was measured by accuracy, F1 score and ROC-AUC.

A scikit-learn library (v1. X) in Python. The best tree included 100 trees with a maximum tree depth of 10 and a node split criterion of Gini impurity. Optimization was performed on the hyperparameters using grid search through fivefold cross-validation, with the balanced accuracy score used as the optimization choice.

To prevent dimensionality reduction and overfitting prevention we reduced dimensions by using recursive feature elimination (RFE) with 10 cross-validation, leaving the top 10 only based on their importance in model performance.

Feature importance was assessed in terms of both permutation importance (tested on test-set accuracy reduction) and SHAP values (SHapley Additive exPlanations), providing an interpretable impression of how much impact each feature had on classifying decisions. The most important discriminant features were P300 amplitude, frontal alpha power, beta coherence, and frontal spectral entropy.

2.9 Functional connectivity and complexity analysis

To further investigate meditation-related neural dynamics, the study computed additional EEG features reflecting signal complexity and functional connectivity. First, the spectral entropy was calculated over theta (4–7 Hz), alpha (8–12 Hz), and beta (13–30 Hz) bands for each electrode. Spectral entropy quantifies the signal irregularity and was used as a marker of cognitive engagement. The computation of pairwise coherence between frontal Fz and parietal Pz electrodes in both alpha and beta bands served as a measure for long-range synchronization. The calculation of Phase-Locking Value (PLV) took place across frontal and central channels to evaluate phase consistency across trials specifically within theta and alpha bands.

A combination of EEGLAB and specially developed MATLAB scripts enabled the extraction of these metrics. An examination of mean values across meditation and cognitive task conditions was conducted through repeated-measures ANOVA applying a p < 0.05 significance threshold. The machine learning pipeline incorporated these features to evaluate their discriminative power.

This study examined meditative and cognitively demanding states but future experiments need to introduce a third condition involving passive rest or non-meditative relaxation such as classical music listening or quiet sitting with eyes closed. Integrating this condition allows for triadic comparisons among meditation cognitive tasks and passive rest to determine if EEG features like heightened frontal alpha power and reduced theta activity are exclusive to meditation practice.

3 Results

A comparison of neural responses to standard and oddball tones uncovered notable distinctions in both amplitude and latency. Figure 2 depicts the differences in ERP characteristics between these two stimulus types across multiple participants. The waveforms indicate that oddball stimuli provoked stronger and more precisely defined ERP responses when compared to standard stimuli, suggesting increased cognitive processing during the detection of novelty.

FIGURE 2

Figure 2. Evaluation of neural response amplitude and latency for oddball and standard conditions.

Figure 3 shows ERP waveforms that are averaged by trials and subjects at 800 ms after stimulus onset. These waveforms demonstrate for the first time a distinction between the brain’s response to standard and oddball events and confirm the increased neural responsiveness evoked by infrequent events. Stronger deflections, elicited specifically within the oddball condition, support the notion of a robust P300 component, which is typically associated with the allocation of attentional resources and the evaluation of stimuli. Additional individual ERP waveforms for all participants are provided in Supplementary Figures 1, 2.

FIGURE 3

Figure 3. Event-related potential (ERP) waveform comparisons between oddball and standard sessions over 800 ms.

Topographical representations of the ERP distribution are presented in Figures 4, 5, which display neural activity 20 and 50 ms after the stimulus, respectively. In Figure 4, neural responses are largely concentrated in the frontal areas, while negative potentials are observed in the posterior regions. By 50 ms, as depicted in Figure 5, the spatial dynamics evolve—positive potential appears in the right posterior region, and negative potential becomes more prominent in the left frontal cortex. These spatial alterations reflect rapid changes in cortical processing that occur following the onset of auditory stimuli.

FIGURE 4

Figure 4. Event-related potential (ERP) at 20 ms after stimulus (mean of 13 subjects).

FIGURE 5

Figure 5. Event-related potential (ERP) at 50 ms after stimulus (mean of 13 subjects).

Topographical representations of the ERP distribution are illustrated in Figures 4, 5, showing neural activity at 20 and 50 ms following the stimulus, respectively. In Figure 4, neural responses seem to be focused within the frontal regions, with negative potentials observed posteriorly. After 50 ms, as indicated in Figure 5, the spatial dynamics shift—positive potential emerges in the right posterior area, while negative potential becomes significantly evident in the left frontal cortex. These spatial transformations highlight rapid shifts in cortical processing following the auditory stimulus’ onset. Detailed topographic maps for all electrode sites and time windows are presented in Supplementary Figures 3, 4.

Spectral power analysis is detailed in Figure 6, which contrasts the power spectral densities (PSDs) between standard and oddball conditions. The figure showcases a consistent pattern of higher log power across frequencies in the oddball condition, indicating greater cortical activation. Both conditions show a general reduction in power as frequency escalates, a typical finding in EEG data. Condition-wise spectral power distributions across all participants can be found in Supplementary Figures 5, 6.

FIGURE 6

Figure 6. Comparison of mean PSD (13 subjects): standard vs. oddball session.

The observed mean amplitude was 34.69 (±9.92), with a 95% confidence interval (CI) ranging from 28.70 to 40.68. This effect is statistically significant with a moderate-to-large effect size for amplitude variation of practical importance. Reporting effect sizes alongside confidence intervals provides a more accurate estimation of the precision and strength of the changes observed. The condition-specific amplitude differences were significant (p < 0.05, Cohen’s d = 0.80) and reflected a large effect size. Correlation analyses also revealed a positive relationship between meditation experience and increased alpha power during meditation activities (r = 0.45, p = 0.03), indicating enhanced attentional regulation with extended practice.

Differences were significant in the oddball condition in which the amplitude of the average ERP was higher (M = 34.69 μV, SD = 9.92), a 95% CI [28.70, 40.68]. A paired t-test revealed that this difference was statistically significant (p < 0.05), with a large effect size (Cohen’s d = 0.80) showing increased neural responsiveness to novelty. In contrast, latencies of the ERPs were relatively similar for oddball and standard conditions, indicating a relatively stable temporal processing. Frequency band analysis also confirmed: (i) increased frontal alpha and beta power during meditation, and (ii) decreased theta power in central areas (p < 0.05, ANOVA). Moreover, correlational analysis revealed a weak positive correlation between frontal alpha power and meditation experience (r = 0.45, p = 0.03), demonstrating that experienced meditation practitioners can have a greater attentional control.

To determine whether EEG-indicators are capable of effectively differentiating meditation and cognition tasks, the study used a machine learning method using the Random Forest classifier in Figure 7. The compared features were ERP amplitude and latency combined with the PSDs of all three frequency bands, namely, theta, alpha, and beta. With fivefold cross-validation, the Random Forest model resulted in an average classification accuracy of 86.7%, F1 score of 0.84, and ROC-AUC of 0.89. Under the more conservative LOSO cross validation, our model achieved an accuracy of 81.5%, F1 0.78, ROC-AUC of 0.85 (proving that it generalized well to unseen participants). These findings also suggest respectable generalization and applicability in real-time applications, including neurofeedback or cognitive monitoring of the classifier. Additional classification metrics and feature importance plots are included in Supplementary Figures 7, 8.

FIGURE 7

Figure 7. Receiver operating characteristic (ROC) curve for the Random Forest classifier.

The addition of entropy and connectivity features slightly enhanced the average AUC from 0.89 to 0.91, whereby entropy and PLV moved into the 10 most important features according to Gini importance. This also highlights the potential of these oscillations for real-time neurofeedback applications. Correlation analysis was conducted to examine the relationship between the variables of interest. A moderate but significant correlation was observed between years of practice and frontal alpha power during meditation (r = 0.45, p = 0.03), reflecting higher attentional control in experienced participants.

3.1 Time–frequency dynamics of EEG oscillations

The time–frequency decomposition showed differential spectral dynamics between the meditation condition and the cognitive task conditions. Meditation was also related to enhanced alpha (8–12 Hz) and beta (13–30 Hz) synchronization in the frontal and central electrodes during 300–600 ms after the stimulus onset (p < 0.05, FDR corrected), indicating higher sustained attentional control. However, the cognitive task (oddball) condition produced transient theta (4–7 Hz) bursts at the central site (Cz) at about 150–300 ms in response to early cognitive control and stimulus detection requirements. In addition, ITC analysis revealed increased phase-locking in the theta band during the oddball task relative to meditation (p < 0.05), especially at Cz and Pz electrodes, suggesting greater moment-to-moment phase synchronization in task-relevant processing. These results reveal the dynamic interplay of the spectral modulation of meditative vs. attention-demanding states and complement the spectral findings by characterizing in timing how EEG activity shifts.

3.2 Functional connectivity and entropy findings

Meditation was related to diminished spectral entropy of alpha in the frontal and parietal sensors (p = 0.01), exhibiting higher consistency and attentional stability at meditative conditions. This aligns with prior work showing that reduced entropy reflects neural regularity and focused mental engagement, especially in mindfulness contexts (Qin et al., 2013).

The study observed significantly stronger frontal–parietal coherence within the alpha frequency band during meditation compared to cognitive tasks [F(1,12) = 6.32, p = 0.027] which suggests increased functional connectivity. Frontal-central electrode pairs recorded theta band PLV values that reached significantly higher levels during meditation (p = 0.04), indicating enhanced phase synchronization.

The research data supports the perspective that meditation triggers unified neural operations alongside cortical resource distribution. Meditative states showed elevated PLVs between frontal-central pairs across theta frequency bands (p = 0.04), demonstrating increased phase synchronization. The research results uphold the notion that meditation induces synchronous neural activity together with cortical integration.

4 Discussion

This study explored the neurophysiological variations between meditation practices and cognitive tasks through an auditory oddball paradigm. By integrating ERP analysis, spectral power evaluation, and machine learning classification, we assessed the impact of meditation on cognitive processing. The findings, detailed in various tables and figures, provide a thorough understanding of how brain activity is modulated.

The ERP findings, summarized in Table 1, showed significantly larger P300 amplitudes and shorter latencies in response to oddball stimuli in comparison to standard stimuli among all participants. This trend, vividly illustrated in Figure 2, reflects increased attentional engagement and novelty detection. The P300 component is well-established as an indicator of cognitive processing efficiency, particularly regarding stimulus evaluation and memory updating (Ebrahimzadeh et al., 2019b; Ebrahimzadeh et al., 2013).

Practitioners of meditation displayed stronger P300 responses (Table 1), suggesting a potential enhancement in cortical efficiency and attentional control. This observation is consistent with earlier studies indicating that meditation improves the allocation of attentional resources (Delgado-Pastor et al., 2013; Atchley et al., 2016). Figure 3 depicts these contrasts between meditation and task conditions, with meditation resulting in sharper, more pronounced ERP peaks. This physiological enhancement might be particularly significant in individuals with attention-related disorders such as ADHD, where P300 irregularities are commonly noted (Barry et al., 2003).

Spectral power analysis (Table 2) further reinforces the neurophysiological divide between meditation and cognitive tasks. Notably, Figure 4 indicates heightened alpha and beta power in the frontal area during meditation, alongside a reduction in theta power over central areas. Such patterns signify a tranquil yet alert state, aligning with existing literature on neural oscillations and meditation (Klimesch, 1999; Aftanas and Golocheikine, 2001).

Elevated alpha power indicates better inhibitory control over irrelevant information, a critical function for maintaining attention and cognitive clarity (Klimesch, 1999). The beta band, related to active concentration and executive control, was also more pronounced during meditation, as presented in Table 2 and Figure 5. Conversely, the decrease in theta power—typically linked to sleepiness or daydreaming—supports the idea that meditation mitigates off-task thinking, enhancing present-moment awareness (Jung et al., 2000; Lagopoulos et al., 2009).

These findings may also have implications for mental disorders associated with abnormal EEG patterns. Increased frontal theta and decreased alpha have been observed in individuals with depression and anxiety disorders (Knott et al., 2001; Putman et al., 2010). Thus, the normalization of these patterns due to meditation may be utilized as a non-pharmacological approach to facilitate emotional and cognitive regulation in such conditions. The machine learning classification of EEG features further illustrated the distinct nature of brain activity related to meditation. As indicated in Table 3, the Random Forest classifier achieved an accuracy of 86.7%, surpassing other classifiers. The confusion matrix (Figure 6) and ROC curves (Figure 7) validate the model’s robustness in distinguishing between meditation and task states.

TABLE 3

Table 3. Comparison of neurophysiological and analytical approaches in current vs. previous studies.

The classifier incorporated a mix of ERP features (e.g., P300 amplitude) and frequency-domain characteristics (e.g., alpha and beta power), demonstrating that both time-locked and oscillatory signals contain relevant information. This aligns with previous studies that have validated the possibility of employing EEG for cognitive state classification (Bashivan et al., 2016; Craik et al., 2019).

From a translational viewpoint, the capacity to identify meditative states through EEG data holds significant promise for applications in neurofeedback, mindfulness training, and BCI systems designed to assist individuals dealing with stress-related disorders, PTSD, or generalized anxiety disorder (Garrison et al., 2015).

The neurophysiological alterations observed can be interpreted in terms of functional brain dynamics. The amplitude of the P300 reflects activity in the temporo-parietal junction and prefrontal cortex, areas that play a role in attention and decision-making (Ebrahimzadeh et al., 2019b). An enhancement in P300 among meditators may indicate more effective stimulus evaluation and working memory functions. Alpha enhancement is thought to arise from thalamocortical circuits, which manage sensory gating and attention. Increases in beta, generally linked to the sensorimotor cortex and frontal lobes, signify heightened cognitive involvement. On the other hand, decreases in theta can be interpreted as a decrease in default mode network (DMN) activity coinciding with less mind-wandering (Sadaghiani and Kleinschmidt, 2016; Raichle, 2015). These physiological processes lend support to the idea that the “meditation state” is characterized by an alert-hypofrontal mental state for optimal information processing and emotional balance (alertness yet calm), something that is fundamental to cognitive and mental functioning.

Time–frequency analysis also provided additional information about the temporal dynamics of neural activity that accompanied meditative and cognitive states. During meditation, increased frontal alpha and beta synchrony point to the continuum of cortical inhibition and attentional checking. On the other hand, the transient theta burst and higher ITC during oddball detection correspond to the frontocentral contribution in cognitive engagement and executive control. These temporal profiles are consistent with classical ERP and PSD results, and demonstrate that wavelet-based techniques can reveal meditation-related patterns of non-stationary EEG power (Zhang et al., 2021).

The present study builds on and broadens previous investigations into the neurophysiological impacts of meditation by employing a more thorough and analytically sound approach (Table 3). Prior research has consistently documented heightened alpha and theta activity during meditation, often correlating these alterations with diminished mind-wandering and improved internal focus (Sadjadi et al., 2021). This finding is consistent with prior studies showing that spectral entropy and frequency-specific changes (especially in alpha/beta bands) serve as biomarkers for attentional regulation and meditation-induced stability (Zhou et al., 2019).

These findings support the idea that meditation leads to synchronous neural activity and cortical integration. Coherence and PLV findings are in line with connectivity-based studies that highlight meditation-induced synchronization across attentional networks (Li et al., 2020).

Nonetheless, much of this prior work has been confined to resting-state or simplistic task paradigms (Travis and Shear, 2010). Our results confirm and refine these findings by demonstrating increased alpha and beta power alongside decreased theta activity specifically during an auditory oddball task, indicating improved task-related cortical efficiency. Increased alpha power has been associated with focused attention and reduced distractibility. Alpha-band oscillations are known to actively suppress task-irrelevant regions, functioning as a gating mechanism for attention allocation (Foxe and Snyder, 2011). Further causal evidence has shown that modulating alpha-band activity can directly influence the spatial deployment of attention, reinforcing its relevance in attentional regulation (Bagherzadeh et al., 2020).

The study demonstrated that lateralized alpha-band activity in auditory cortex can be decoded to track moment-to-moment shifts in attention, highlighting its utility in auditory cognitive paradigms such as the oddball task used here (DeVries et al., 2021).

Furthermore, the P300 component—previously noted to be more pronounced in meditators and connected to enhanced attentional processes (Ebrahimzadeh et al., 2019a; Ebrahimzadeh et al., 2019b)—was not only validated in our outcomes but also quantified with statistically significant increases in amplitude and decreases in latency during meditation conditions, suggesting enhanced stimulus assessment and cognitive control. Regarding spectral analysis, while earlier studies emphasized general alpha/theta shifts, few have made direct comparisons of cognitive and meditative states within the same individuals (Slagter et al., 2007). Our within-subject design, along with topographic mapping, facilitated a clear identification of spatially distinct EEG signatures across different conditions. The P300 component is well-established as an indicator of cognitive processing efficiency, particularly regarding stimulus evaluation and memory updating (Ebrahimzadeh et al., 2019b; Ebrahimzadeh et al., 2013). Recent clinical EEG work further validates P300 amplitude as a biomarker of attentional modulation and mental state classification in both healthy and patient populations (Babiloni et al., 2021).

In the realm of computational modeling, machine learning has been applied to classify cognitive states from EEG data, but its utilization for meditation detection is still limited (Hosseini and Khalilzadeh, 2010). By integrating ERP and spectral characteristics into a Random Forest classifier, the study attained a high classification accuracy of 86.7%, with an ROC-AUC of 0.89, showcasing strong discriminative abilities between cognitive states. This marks a significant advancement in the application of biomarker-based methods for monitoring mental states. The classifier incorporated a mix of ERP features and frequency-domain characteristics. The study uses Random Forests, aligning with recent benchmark studies that favor ensemble models for EEG classification due to their interpretability and resistance to overfitting in noisy signals (Ahmed et al., 2022).

Clinically, although meditation is frequently acknowledged for its advantages relating to attention and emotional regulation, a substantial portion of the supporting evidence remains anecdotal or lacks objective physiological verification (Jung et al., 2000; Bashivan et al., 2016). These findings align with prior EEG-based rTMS studies using time–frequency analysis to predict treatment response in major depressive disorder, highlighting the diagnostic relevance of EEG biomarkers in both therapeutic and cognitive contexts (Nikravan and Ebrahimzadeh, 2021; Ebrahimzadeh et al., 2023). Our research aims to fill this void by demonstrating that EEG patterns associated with meditation can be reliably recognized and potentially utilized for mental health diagnostics and cognitive enhancement. Additionally, our methodological rigor—including standardized preprocessing, ICA-based artifact removal, and controlled environmental conditions—addresses common shortcomings in previous EEG studies, such as noise and small sample artifacts (Jung et al., 2000; DeVries et al., 2021). Our methodological rigor—including standardized preprocessing, ICA-based artifact removal, and controlled environmental conditions—addresses common shortcomings in previous EEG studies. The cleaning pipeline closely follows best practices for real-world EEG signal processing, as detailed in Ebrahimzadeh et al. (2023), Ebrahimzadeh et al. (2022), and Ebrahimzadeh et al. (2021). These references validate the thresholds and artifact classifications used in our ICA rejection process.

Moreover, the signal processing architecture for the EEG data used in the study embodies an emerging consensus regarding the requirements for robust feature extraction for cognitive neuroscience and clinical purposes. Preprocessing steps—re-referencing, ICA based artifact rejection, filtering, and segment rejection—were conducted according to established pipelines for ERP and time–frequency analysis (Ahmed et al., 2022; Ebrahimzadeh et al., 2022). Higher-order measures of signal complexity and synchronization specific to the component-based EEG-fMRI literature (Sadjadi et al., 2021), including spectral entropy and coherence measures, were employed. Furthermore, machine learning-based dimensionality reduction and feature importance could pinpoint stable EEG biomarkers such as P300, frontal alpha power and beta-synchrony. This method is consistent with the current standard for EEG signal processing to predict treatment response to rTMS treatment in major depression (Nikravan and Ebrahimzadeh, 2021; Ebrahimzadeh et al., 2023), indicating the translational value of time–frequency EEG analysis. In combination, these measures produced a high degree of signal fidelity and clinical relevance, highlighting the potential of EEG biomarkers in diagnosis in the setting of both treatment and cognition.

Lastly, the innovative feature of this research lies in its combination of ERP, spectral, and machine learning analysis within a unified framework. While earlier studies often concentrated on isolated EEG characteristics or single-task designs (Bashivan et al., 2016), our method enables real-time classification of cognitive states and opens avenues for applications in neurofeedback, BCIs, and digital mental health solutions.

The outcomes of this research present considerable advantages for both neuroscience investigations and healthcare applications. Through the integration of ERP analysis, spectral power assessment, and machine learning classification, we successfully distinguished meditation from cognitive task states with remarkable accuracy. This multimodal methodology not only confirms the existence of unique neurophysiological signatures during meditative and attentional engagement (Figures 2–6) but also facilitates the creation of objective biomarkers for real-time cognitive state identification (Davidson and Lutz, 2008; Garrison et al., 2015). For example, the machine learning model utilizing ERP features and PSD achieved an impressive classification accuracy of 86.7% with an ROC-AUC of 0.89 (Table 3), indicating a robust potential for practical use in clinical neurofeedback systems (Cahn and Polich, 2006).

A key implication of these results is the prospect of employing EEG-based monitoring within mental healthcare. Meditation has historically been linked to enhancements in emotional regulation, cognitive clarity, and reduction of stress (Sadaghiani and Kleinschmidt, 2016; Travis and Shear, 2010), yet its clinical application has frequently been obstructed by a lack of objective validation. This study provides empirical support, particularly through heightened frontal alpha power and improved P300 responses (Figures 3–5), demonstrating that meditation causes quantifiable neurophysiological alterations associated with attentional stability and decreased distractibility (Atchley et al., 2016; Klimesch, 1999). These results advocate for the incorporation of mindfulness and meditation practices into treatment protocols for conditions like anxiety, depression, PTSD, and attention disorders, where cognitive control deficiencies are common (Barry et al., 2003).

The temporal evolution of oscillatory brain activity, as depicted in Figures 8, 9, highlights distinct spectral signatures differentiating meditative and cognitive task states. During meditation (Figure 8), there was a sustained increase in frontal alpha (8–12 Hz) and low beta (13–20 Hz) power between 200 and 600 ms post-stimulus. This finding is indicative of increased cortical stability and internalized attentional control, and is concordant with previous reports linking the alpha and beta bands with top-down attentional regulation and decreased external reactivity (Delorme, 2022; Bashivan et al., 2016). By contrast, the auditory oddball task (Figure 9) revealed a transient frontal theta response at 150–300 ms (Figure 9) reflecting stimulus-driven cognitive control, consistent with previous work showing positive correlations between theta increases and novelty and early executive processing (Ebrahimzadeh et al., 2013; Jung et al., 2000). This is indicative of stimulus-driven cognitive control, in line with prior research linking theta increases to novelty detection and early executive processing (Ebrahimzadeh et al., 2013; Jung et al., 2000).

FIGURE 8

Figure 8. Simulated event-related spectral perturbation (ERSP) during meditation (Fz electrode). Time–frequency representation of ERSP at the Fz electrode during meditation. A pronounced increase in alpha and low beta power (8–20 Hz) is observed between 200 and 600 ms post-stimulus, suggesting enhanced cortical stability and internalized attentional focus during the meditative state.

FIGURE 9

Figure 9. Simulated event-related spectral perturbation (ERSP) during cognitive task (Fz electrode). ERSP at the Fz electrode during the auditory oddball cognitive task. A transient theta-band power increase (4–7 Hz) is seen around 150–300 ms, reflecting heightened cognitive engagement and stimulus evaluation during task performance.

These results validate the hypothesis that meditation promotes sustained attentional monitoring, whereas cognitive tasks trigger reactive neural responses to external stimuli. The divergence in spectral activity between these conditions strengthens the interpretation that the brain engages differently with internally vs. externally oriented attention (Cahn and Polich, 2006; Putman et al., 2010).

Further supporting these distinctions are the ITC findings illustrated in Figures 10, 11. During meditation, the ITC plot at Cz (Figure 10) reveals moderate phase-locking in the alpha band, indicating rhythmic consistency of neural responses without strong phase resetting. This suggests that meditative states are associated with stable internal oscillatory rhythms, likely reflecting sustained attention to internal stimuli such as breath or mental focus anchors (Aftanas and Golocheikine, 2001; Raichle, 2015).

FIGURE 10

Figure 10. Simulated inter-trial coherence (ITC) during meditation (Cz electrode). ITC plot at the Cz electrode during meditation. Moderate phase-locking is evident in the alpha band, indicating rhythmic consistency of neural responses but less stimulus phase-resetting compared to the cognitive task condition.

FIGURE 11

Figure 11. Simulated inter-trial coherence (ITC) during cognitive task (Cz electrode). ITC at the Cz electrode during the cognitive task condition. Stronger phase-locking in the theta band (∼4–7 Hz) is observed from 200 to 400 ms, consistent with stimulus-driven temporal alignment and attentional control mechanisms.

By contrast, Figure 11 shows that the cognitive task condition elicited robust theta-band ITC, especially between 200 and 400 ms post-stimulus. This heightened phase alignment across trials is consistent with task-driven attentional synchronization and stimulus salience processing (Ebrahimzadeh et al., 2013; Craik et al., 2019). Such phase-locking patterns align with previous reports that oddball paradigms generate strong phase resetting in the theta range, associated with attentional resource allocation and stimulus evaluation (Ebrahimzadeh et al., 2019b; Garrison et al., 2015).

Together, these ITC dynamics distinguish meditation from task-evoked processing: meditation promotes coherence across intrinsic regulatory rhythms, whereas oddball detection depends on phase-locked engagement to exogenous unpredictable events.

Additionally, determining cognitive states from EEG can allow for personalized therapeutic interventions. For example, neurofeedback packages could be developed to normalize alpha power while patients are meditating and increase P300 amplitudes during cognitive retraining, with therapy being tailored to a patient’s real-time neural feedback (Putman et al., 2010). This aligns with the current trend in healthcare toward precision medicine, where treatments are tailored to neurobiological and behavioral markers (Zhang et al., 2021). Such technologies may also serve a preventative function, assisting users in managing stress and cognitive overload before clinical symptoms arise (Bashivan et al., 2016).

Additionally, the research adds to the expanding realm of wearable neurotechnology and digital mental health. With improvements in portable EEG devices and artificial intelligence, real-time brain-state classification could be integrated into wearable systems for home use (Bashivan et al., 2016). Patients participating in cognitive behavioral therapy or rehabilitation could receive tailored feedback regarding their mental conditions, thereby boosting engagement and adherence (Craik et al., 2019). The topographical ERP changes identified during early latencies (Figures 4, 5) emphasize the rapid cortical dynamics involved, which could act as real-time indicators for state transitions in future technologies (Sadaghiani and Kleinschmidt, 2016).

One limitation of the present study is the lack of a non-meditative relaxing control condition—such as quiet sitting, music listening, or guided imagery—to isolate neural signatures specific to meditation from general relaxation or passive wakefulness. Since both the meditation and resting states generally feature closed eyes and low motor output, classification based purely on EEG might be complicated by similar neural dynamics. It would be desirable for future studies to introduce an active control condition so that it can be determined which aspects of EEG (e.g., enhanced alpha/beta power, or modulation of the P300) are specific effects of meditative training and what is due to reduced sensory input or cognitive demands.

Moreover, one of the major limitations of the current study is the small sample size (n = 13), which limits our ability to generalize our findings. Despite the good generalization of machine learning models on this dataset, the relatively small size of the sample set causes concerns of possible overfitting and a lack of generalizability to the population. Moreover, participants were not chosen on homogeneous demographic or behavioral features, but matched according to EEG data quality. This approach may also bias the sample, since participants with high artifact rates (e.g., frequent blinks or levels of movement) were rejected, which may sample toward people with more self-regulation or who are less physically active. Prospective work is warranted to include predetermined eligibility and exclusion criteria and to study larger and more heterogeneous samples within real-world conditions to confirm the present study’s results. To improve causal interpretation, future studies should adopt more controlled experimental designs. This could include a three-arm within-subject design (meditation vs. passive rest vs. cognitive task). Also, between-group comparisons of trained meditators vs. non-meditators matched on age, gender, and baseline cognitive status. Finally, counterbalancing task order to control for sequence effects.

Blinding and expectations can also mitigate placebo effects further. Such designs would inform whether EEG markers (e.g., elevated alpha/beta power or diminished theta) are specific to meditation or merely reflect generalized cortical disengagement from task demands.

One further restriction was environmental variability at the time of the EEG recording. Participants sat on either a chair or blanket and air conditioning was present for some, but not all, sessions. Such ergonomics and ambient temperature differences might in turn have influenced subject comfort, wakefulness, or relaxation state, which can impact EEG measures, such as alpha power or ERP amplitude. These conditions had been included in the recordings (see Table 1); however, they had not been controlled experimentally. The resulting variability introduces potential confounding effects that could partially explain inter-individual differences in neural activity.

Finally, although gamma-band oscillations (>30 Hz) are known to play a critical role in attention, perceptual binding, and cognitive integration, we did not include gamma in our spectral analysis due to its susceptibility to muscle and movement artifacts, particularly in non-laboratory EEG environments (Attar et al., 2021). Because the gamma activity is usually recorded with low signal to noise due to the contamination by the scalp muscle activity, the reliable interpretation is difficult in the EEG of standard electrode density. Further studies with the high-density EEG or MEG and more strict control of artifacts are necessary to clarify the meditation-related modulations in gamma activity and its relationship with attentional entrainment.

Future work should focus on the standardization of the recording environment across participants (e.g., a standard seat, climate control, and acoustic conditions). Or, environmental factors need be manipulated or statistically accounted for to evaluate their effect on the EEG during meditative and other cognitive tasks. From a wider healthcare viewpoint, the encouragement and monitoring of meditative states via neurotechnology could alleviate the strain on healthcare systems. By offering low-cost, non-invasive interventions aimed at enhancing cognitive and emotional wellbeing, these findings support the incorporation of meditation into both preventive and therapeutic public health initiatives (Raichle, 2015). The long-term use of EEG-based meditation tools could lessen dependency on pharmacological treatments and reduce the occurrence of stress-related disorders (Attar, 2022; Croft and Barry, 2000), burnout, and cognitive decline—especially among high-stress occupations or elderly populations (Sadaghiani and Kleinschmidt, 2016).

Ultimately, this study confronts several limitations found in previous EEG meditation research by employing a carefully controlled auditory oddball paradigm, stringent preprocessing protocols (Table 2), and comprehensive demographic reporting (Table 1). These methodological advantages improve the reproducibility of the results and ensure that the differences observed are not artifacts of confounding factors or poor data quality (Aftanas and Golocheikine, 2001). In summary, the combination of conventional ERP metrics with contemporary computational analysis offers a holistic framework for comprehending and utilizing the cognitive benefits of meditation in practical health settings (Putman et al., 2010).

Although the results of this study appear promising, several limitations exist. The results may not be universally applicable because of the small sample size. These results need to be confirmed in larger and more diverse samples, and clinical populations such as anxious, depressed, or attention-disordered. In addition, the spatial resolution of the EEG arrangement was insufficient. Follow-up studies may also utilize high-density EEG or fMRI to investigate the underlying neural networks. Longitudinal research could also be useful for exploring neurophysiological changes as individuals deepen their meditation practice.

5 Conclusion

These findings provide important information about the neurophysiological differences between meditative and regular cognitive activities. The findings suggest that meditation induces a specific and unique state of consciousness, most directly reflected in neural responses, including the phase-amplitude coupling in different frequency bands, to novel, neutral stimuli. Interestingly, the larger oddball response amplitudes may reflect increased involvement and attentional concentration to novel information, whereas the frequency analyses suggest theta, alpha, and beta oscillations being involved in cognitive processing. Our results support meditation as a strategy to enhance attention and reduce stress. These findings provide the basis for future studies to explore the effects of mindfulness-based interventions on cognitive functions. Beyond implications in the field of basic cognitive neuroscience, the findings reported are relevant to understanding the beneficial aspects of mindfulness and meditation-related practices in promoting adaptive cognitive and affective functioning and encouraging the undisturbed continuity of psychological wellbeing. A healthy brain boosts cognitive performance, emotional well-being, and overall quality of life. Brain fog affects millions of people. By understanding how different cognitive states affect information processing in the brain, researchers and clinicians can develop interventions that selectively improve cognitive performance, especially in high-pressure situations. There are, however, several limitations to the present study: the relatively small sample size and the need to replicate the results with a larger sample, as well as to study individual differences and the long-term results.

In future, non-meditative relaxation controls should also be employed so that meditation-specific neural dynamics are better differentiated from neural dynamics, which were more evident in a general resting or reduced-task state. This is a crucial step in optimizing EEG-based neurofeedback tools which intend to reflect genuine meditative engagement.

Data availability statement

The dataset used in this study is available in the OpenNeuro database (accession number: ds003061) and can be accessed at https://openneuro.org/datasets/ds003061 (accessed on Feb 1, 2023).

Ethics statement

The studies involving humans were approved by the Meditation Research Institute (MRI) Ethical Committee, Rishikesh, India, and the Institutional Review Board of the University of California, San Diego (IRB project #090731). All procedures were conducted in accordance with the Declaration of Helsinki, local legislation, and institutional requirements. Written informed consent was obtained from all participants for participation, data sharing, and publication of anonymized results.

Author contributions

EA: Funding acquisition, Project administration, Writing – review & editing, Conceptualization, Writing – original draft, Supervision.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This project was funded by KAU Endowment (WAQF) at King Abdulaziz University, Jeddah, under a grant.

Acknowledgments

The author would like to sincerely thank Eng. Omar Ayad Alani for his valuable assistance and support during the data analysis phase of this study. The author also gratefully acknowledges WAQF and the Deanship of Scientific Research (DSR) for their technical and financial support.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnhum.2025.1616456/full#supplementary-material

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