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

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 <i>Vipassana</i> 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 <i>Himalayan Yoga</i> 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.