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.

Meditation and yoga practices are increasingly used to prevent ailments. This work examined how the high-pass filter (HPF) cutoff frequency affects single-trial classification accuracy for distinguishing meditative from non-meditative states using electroencephalogram (EEG) data. Two meditation types were studied: Vipassana and Himalayan Yoga. Inception Convolutional Gated Recurrent Neural Network (IC-RNN) and Convolutional Neural Network (CNN) models were compared at various HPF settings. For Vipassana, the highest accuracy was 86.19% with IC-RNN and 99.45% with CNN at a 1 Hz filter. For Himalayan Yoga, the highest accuracy was 88.15% with IC-RNN and 100% with CNN at the same 1 Hz setting. The 1 Hz HPF consistently yielded strong results, suggesting guidelines for filter settings to improve model performance.

Study at a glance

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.