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.

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Article in Annals of neurosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 authors.

Ritu MunjalDepartment of Electrical Electronics and Communication Engineering, Sharda University, Greater Noida, Uttar Pradesh, India.ORCID https://orcid.org/0009-0002-1684-8498

Tarun VarshneyDepartment of Electrical Electronics and Communication Engineering, Sharda University, Greater Noida, Uttar Pradesh, India.ORCID https://orcid.org/0000-0002-4047-7351

Funding

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6 · The paper itself

Abstract

Background: Meditation and Purpose: 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. Methods: 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. Results and Conclusion: 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

Indexed as

Convolutional Neural Network (CNN)Deep Learning (DL)Electroencephalogram (EEG)High-pass Filter (HPF)Himalayan Yoga MeditationInception Convolutional Gated Recurrent Neural Network (IC-RNN)Vipassana Meditation

Identifiers

PMID40693246

DOI10.1177/09727531251351067

PMCPMC12276206

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.