scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

SVM classification of EEG signal to analyze the effect of OM Mantra meditation on the brain

TL;DR: Results show the significant changes in the delta band which represent the brain in deep sleep which gives the experience of deep sleep in Om mantra meditation.
Abstract: Meditation can significantly contribute to improving physical and mental health in modern stressful life. "OM" mantra is very easy to practice for meditation .This study is undertaken to classify the EEG band to observe abrupt changes in band as an effect of Om mantra meditation. Twenty-three naive meditators were experimented to chant OM mantra for 30 min and EEG signal recorded before and after meditation. The stationary wavelet transform is used to exact five bands from the EEG. The different statistical features were calculated. SVM classifier with Radial Basis Kernel is employed to classify the band. Results show the significant changes in the delta band which represent the brain in deep sleep. Thus OM meditation gives the experience of deep sleep. Thus study can be helpful to give new direction towards the meditation.
Citations
More filters
Proceedings ArticleDOI
18 Dec 2020
TL;DR: In this article, the changes in brain signal using C4-A1 channel of the Electroencephalogram (EEG) signal during recitation of the Maha Mrityunjaya hymn was found.
Abstract: Maha Mrityunjaya HYMN is Hindu religious belief to control the mind to dedicate Lord Shiva Many seers have been practicing it since ancient time The main aim of this paper is to find the changes in brain signal using C4-A1 channel of the Electroencephalogram (EEG) signal during recitation of this hymn This study was completed in some steps such as real time EEG recording, extraction of the C4-A1 channel, preprocessing, calculation of the power spectral density, and classification using Decision Tree (DT) and K-Nearest Neighbor (KNN) on five yoga practitioners The KNN classifier having 5-fold cross validation model achieved the highest recall (974%) and accuracy (974%) of the system We obtained that the average frequency of the EEG wave increased prominently This study will be helpful for the people to understand in the scientific way for the recitation of the religious hymn

8 citations

Book ChapterDOI
18 Nov 2022
TL;DR: In this article , the authors draw comparisons between the Hindu worldview and broadly Western-based positive psychology concepts like well-being and the self, and further explain the recent empirical evidence on Hindu constructs, including wellbeing enhancing strategies, in relation to several positive psychology constructs such as wellbeing and flourishing, and discuss some directions and recommendations for future research at the intersections of Hindu religious literature and positive psychology.
Abstract: Abstract Hinduism encompasses considerable heterogeneity within its many schools of thoughts and practice. However, the common thread that binds these multiple perspectives is this handbook’s main topic of inquiry—human well-being and happiness. Hindu thought has always deliberated on the process, nature, conditions, and practices that lead to a fulfilled life. In this chapter, we begin by explaining Hinduism’s core beliefs and tenets that guide daily living. Many extant psychological publications on Hinduism explicate psychological ideas embedded in tradition. We draw comparisons between the Hindu worldview and broadly Western-based positive psychology concepts like well-being and the self. The chapter also expands on select Hindu models of human flourishing. The chapter further explains the recent empirical evidence on Hindu constructs, including well-being enhancing strategies, in relation to several positive psychology constructs such as well-being and flourishing. Finally, in the light of the reviewed empirical evidence, we discuss some directions and recommendations for future research at the intersections of Hindu religious literature and positive psychology.
Proceedings ArticleDOI
20 Nov 2022
TL;DR: In this paper , several machine learning algorithms are applied to design a model which can predict the effect of a commute and the results obtained from the employed machine learning algorithm predict that heart rate difference before and after commute will correlate with EEG signals in participants who have selfreported to be stress after the commute.
Abstract: Stress can be described as an alteration in our body that can cause strain emotionally, physically, or psychologically. It is a reaction from our body to something that demands attention or exertion. It can be caused by various reasons depending on the physical or mental activity of the body. Commuting on a regular basis also acts as a source of stress. This research aims to explore the physiological effects of the commute with an application of a machine-learning algorithm. The data used in this research is collected from 45 healthy participants who commute to work on a regular basis. A multimodal dataset containing medical data like biosignals (heart rate, blood pressure, and EEG signal) plus responses obtained from the questionnaire PANAS. Evaluation is based on the performance metrics that include confusion matrix, ROC/AUC, and classification accuracy of the model. In this research, several machine learning algorithms are applied to design a model which can predict the effect of a commute. The results obtained from this research suggest that whether the interval of commute was small or large, there was a significant rise in stress levels including the bio-signals (electroencephalogram, blood pressure and heart rate) after the commute. The results obtained from the employed machine learning algorithms predict that heart rate difference before and after commute will correlate with EEG signals in participants who have self-reported to be stress after the commute. The random forest algorithm gave a very promising result with an accuracy of 91%, while the KNN and the SVM showed the accuracy of 78% and 80% respectively.
Proceedings ArticleDOI
20 Nov 2022
TL;DR: In this paper , several machine learning algorithms are applied to design a model which can predict the effect of a commute and the results obtained from the employed machine learning algorithm predict that heart rate difference before and after commute will correlate with EEG signals in participants who have selfreported to be stress after the commute.
Abstract: Stress can be described as an alteration in our body that can cause strain emotionally, physically, or psychologically. It is a reaction from our body to something that demands attention or exertion. It can be caused by various reasons depending on the physical or mental activity of the body. Commuting on a regular basis also acts as a source of stress. This research aims to explore the physiological effects of the commute with an application of a machine-learning algorithm. The data used in this research is collected from 45 healthy participants who commute to work on a regular basis. A multimodal dataset containing medical data like biosignals (heart rate, blood pressure, and EEG signal) plus responses obtained from the questionnaire PANAS. Evaluation is based on the performance metrics that include confusion matrix, ROC/AUC, and classification accuracy of the model. In this research, several machine learning algorithms are applied to design a model which can predict the effect of a commute. The results obtained from this research suggest that whether the interval of commute was small or large, there was a significant rise in stress levels including the bio-signals (electroencephalogram, blood pressure and heart rate) after the commute. The results obtained from the employed machine learning algorithms predict that heart rate difference before and after commute will correlate with EEG signals in participants who have self-reported to be stress after the commute. The random forest algorithm gave a very promising result with an accuracy of 91%, while the KNN and the SVM showed the accuracy of 78% and 80% respectively.
Journal ArticleDOI
21 Dec 2020
TL;DR: It is found by MATLAB simulations that the performance of proposed scheme coded Discrete wavelet transform based Orthogonal frequency division multiplexing outperforms than that of ½ rate convolution encoded Discrete Wavelet Transforms based Ortho- frequency divisionmultiplexing with 16-Pulse Amplitude Modulation.
Abstract: Wavelet Transforms is an Important Part of, Systems Theory and Signal Processing and finds numerous important applications in Science and Engineering. In this paper, we investigated the performance of proposed scheme coded Discrete wavelet transform based Orthogonal frequency division multiplexing scheme over Additive white Gaussian noise channel using Pulse Amplitude Modulation in terms of Energy bits per noise ratio values. The simulation has been done using MATLAB software and results are compared with ½ rate convolution coded Discrete wavelet transform based Orthogonal frequency division multiplexing system. It is found by MATLAB simulations that the performance of proposed scheme coded Discrete wavelet transform based Orthogonal frequency division multiplexing outperforms than that of ½ rate convolution encoded Discrete wavelet transform based Orthogonal frequency division multiplexing with 16-Pulse Amplitude Modulation. Along with this, different orders of reverse biorthogonal and biorthogonal wavelets are implemented to simulate the proposed system with 16-Pulse Amplitude Modulation scheme. The performance of proposed system is compared and it is found that proposed system performs better than conventional system under all different simulation conditions. This study finds important applications in Signal Processing.
References
More filters
Journal ArticleDOI
TL;DR: In this research, discrete Daubechies and harmonic wavelets are investigated for analysis of epileptic EEG records and the capability of this mathematical microscope to analyze different scales of neural rhythms is shown to be a powerful tool for investigating small-scale oscillations of the brain signals.

1,077 citations

Journal ArticleDOI
TL;DR: In this work, a versatile signal processing and analysis framework for Electroencephalogram (EEG) was proposed and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients.
Abstract: In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical operation.

1,010 citations

Journal ArticleDOI
TL;DR: It is suggested that exercise-immune interactions can be viewed as a subset of stress immunology and reflects the intensity, duration and chronicity of the exercise.

501 citations


"SVM classification of EEG signal to..." refers background in this paper

  • ...Stress becomes a part of everyday life that leads to different states such as anxiety, anger or fear [1] which may pose a major effect on heart and brain function [2]....

    [...]

Journal ArticleDOI
TL;DR: An artificial neural network technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals, which provides a potentially powerful technique for preprocessing EEG signals prior to classification.

291 citations

Journal ArticleDOI
TL;DR: Electroencephalography signals and its characterization with respect to various states of human body and experimental setup used in EEG analysis are focused on.

201 citations


"SVM classification of EEG signal to..." refers background in this paper

  • ...Delta and Theta band represent the brain in deep sleep and light sleep activity respectively [31-32]....

    [...]