Varying the High-pass-Cut Off Frequency Influences the Accuracy of the Model for Detection of Mind State Associated with Himalayan Yoga and Vipassana Meditation.
Annals of Neurosciences July 19, 2025 DOI: 10.1177/09727531251351067 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Experimental study Peer reviewed |
|---|---|
| Population | EEG recordings from individuals practicing Vipassana and Himalayan Yoga meditation |
| Topics | Meditation |
| Keywords | Deep learning dl Electroencephalogram EEG High-pass filter hpf Himalayan yoga meditation Vipassana meditation |
| Key findings | A 1 Hz high-pass filter setting produced the highest classification accuracy for both meditation types, with CNN models achieving up to 100% accuracy for Himalayan Yoga and 99.45% for Vipassana. |
Abstract
Meditation and Yoga practices are being adopted and gaining considerable interest as a tool that prevents the occurrence of numerous ailments. Meditation is well prescribed in several old religious manuscripts and has origins in past Indian practices that encourage emotional and personal well-being. Two different classification tasks were performed. One way to identify the mind state allied with Vipassana meditation and another was to identify the mind state allied with Himalayan Yoga meditation. The tasks were performed for classifying non-meditative and meditative states with varying cut-off frequencies to obtain the best results. This study is mainly focused on how the high-pass cut-off influences the single-trial accuracy of the model. The performance of the model depends on appropriate pre-processing. The results of High-pass Filter (HPF) at different settings were methodically assessed. Although there are many factors on which the accuracy of the model depends, like the HPF, Independent Components Analysis (ICA), model building and the hyperparameter tuning. One important preprocessing step is to effectively choose the filter to improve the classification results. Inception Convolutional Gated Recurrent Neural Network (IC-RNN) and Convolutional Neural Network (CNN) models were designed and compared to examine the varying effects of HPF. The highest accuracy of 86.19% was attained for IC-RNN, and 99.45% was achieved for CNN model with filter setting at 1 Hz for the Vipassana meditation classification task. The highest accuracy of 88.15% was attained for IC-RNN, and 100% was achieved for CNN model with the same filter setting at 1 Hz for the Himalayan Yoga meditation classification task. HPF at 1 Hz steadily produced good results. Based on the outcomes, the guidelines are suggested for filter settings to increase the performance of the model.
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
Cited by 2
- Machine learning techniques for detection of brain state associated with Himalayan Yoga meditation Cogent Engineering August 24, 2026
- High-Pass-Filtering Impacts the Accuracy of the Hybrid Model for Detection of Brain State Allied with Isha Shoonya Meditation Journal of Scientific and Technical Research 2025
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