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
Source: PubMed
Summary
The use of a 1 Hz high-pass filter significantly enhances meditation classification accuracy, achieving 100% for Himalayan Yoga and 99.45% for Vipassana using a Convolutional Neural Network (CNN). In this analysis, two classification tasks were performed on electroencephalogram (EEG) data from meditation practices, with sample sizes indicating robust results. The Inception Convolutional Gated Recurrent Neural Network (IC-RNN) also showed impressive results, reaching 86.19% for Vipassana and 88.15% for Himalayan Yoga. These findings highlight the importance of pre-processing techniques in deep learning applications for mental state identification.
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.