EEG-based meditation decoding: tackling subject variability with spatial and temporal alignment.
Angeliki-Ilektra Karaiskou, Carolina Varon, Cem Ates Musluoglu, Kaat Alaerts, Maarten De Vos
Journal of Neural Engineering December 22, 2025 DOI: 10.1088/1741-2552/ae2b0f (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Experimental study Peer reviewed |
|---|---|
| Sample size | 53 |
| Population | Novice and expert meditators |
| Intervention | Meditation |
| Topics | Meditation |
| Keywords | Monge map Riemannian geometry Brain–computer interface Deep learning Domain adaptation Electroencephalography |
| Key findings | Combined RSDA and CMMN alignment improved cross-subject classification accuracy of meditation versus rest to 66.6% without retraining. |
Abstract
Objective. Meditation and mindfulness are increasingly recognized as important in improving mental well-being. However, electroencephalography (EEG)-based neurofeedback systems supporting these practices typically fail to generalize to unseen subjects. This study investigates the application of both spatial and spectral alignment to EEG to improve the classification of meditation and rest states for new subjects without any model retraining.Approach. Two unsupervised domain adaptation techniques are employed to reduce differences between subjects in their EEG recordings. The first, Riemannian Space Data Alignment (RSDA), adjusts and brings together patterns of brain activity across electrodes (spatial domain). The second, Convolutional Monge Mapping Normalization (CMMN), aligns the distribution of brain rhythms across frequencies (spectral domain). Each method is evaluated separately, in combination, and in interaction withz-score normalization. Classification between meditation and rest is performed on the aligned time series using EEGNet, a compact convolutional neural network architecture, with leave-one-subject-out (LOSO) cross-validation to assess generalization across subjects. All experiments are based on a publicly available dataset of meditation EEG recordings from 53 subjects, including both novice and expert meditators.Main results. The combined RSDA+CMMN approach significantly improved LOSO classification accuracy (66.6%) compared to non-aligned (55.7%) andz-score normalized (59.6%) baselines, even though it did not improve overall harmonization. Spectral analysis identified consistent classification contributions from the Theta (4-8 Hz), Alpha (8-14 Hz), and Beta (14-30 Hz) bands, while spatial analysis highlighted Frontopolar and Temporal regions as critical for distinguishing the mental states of meditation and rest.Significance. This work is the first to explore both spatial and spectral alignment in subject-independent meditation decoding for improved cross-subject generalization. Aligning EEG time series without retraining provides a practical solution for real-time neurofeedback, thereby reducing subject variability and paving the way toward calibration-free neurotechnology that supports mental well-being.
Comparable studies
Other experimental studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| The benefits of being present: Mindfulness and its role in psychological well-being. Cancer patients (clinical intervention study) | 2003 | Mixed methods: scale development, correlational, quasi-experimental, laboratory, experience-sampling, and clinical intervention study | |
| Loving-kindness meditation increases social connectedness. | 2008 | Controlled laboratory experiment | |
| Mindfulness and mind-wandering: Finding convergence through opposing constructs. | 2012 | Two studies: correlational study and experimental study | |
| Mental Training Enhances Attentional Stability: Neural and Behavioral Evidence | 2009 | Experimental study | |
| Mindfulness meditation and substance use in an incarcerated population. Incarcerated population | 2006 | Quasi-experimental with treatment-as-usual control condition |
Citations in the library
Cites 12
- The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology 2003
- Meditation Programs for Psychological Stress and Well-being JAMA Internal Medicine January 6, 2014
- Review of the Neural Oscillations Underlying Meditation Frontiers in Neuroscience March 26, 2018
- EEG Derived Neuronal Dynamics during Meditation: Progress and Challenges Advances in Preventive Medicine 2015
- Simple Neurofeedback via Machine Learning: Challenges in real time multivariate assessment of meditation state bioRxiv Preprint Server September 27, 2022
and 7 more in the library