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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
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Journal ArticleDOI
TL;DR: This study proposed a signal preprocessing and feature extraction method for EEG classification that consists of removing the artifacts by using Discrete Fourier Transform (DFT) as an ideal filter for specific frequencies.
Abstract: A crucial part of the brain-computer interface is a classification of electroencephalography (EEG) motor tasks. Artifacts such as eye and muscle movements corrupt EEG signal and reduce the classification performance. Many studies try to extract not redundant and discriminative features from EEG signals. Therefore, this study proposed a signal preprocessing and feature extraction method for EEG classification. It consists of removing the artifacts by using discrete fourier transform (DFT) as an ideal filter for specific frequencies. It also cross-correlates the EEG channels with the effective channels to emphases the EEG motor signals. Then the resultant from cross correlation are statistical calculated to extract feature for classifying a left and right finger movements using support vector machine (SVM). The genetic algorithm was applied to find the discriminative frequencies of DFT for the two EEG classes signal. The performance of the proposed method was determined by finger movement classification of 13 subjects and the experiments show that the average accuracy is above 93 percent.

24 citations

Proceedings ArticleDOI
TL;DR: There is no versatile device suitable for all purposes in the field of wellbeing support, and the WellAff system able to recognize affective states for wellbeing support is proposed.
Abstract: Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals in our everyday life. We propose the WellAff system able to recognize affective states for wellbeing support. It also includes health care scenarios, in particular patients with chronic kidney disease (CKD) suffering from bipolar disorders. For the need of a large-scale field study, we revised over 50 off-the-shelf devices in terms of usefulness for emotion, stress, meditation, sleep, and physical activity recognition and analysis. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Using Empatica E4 and Samsung Galaxy Watch, we have recorded physiological signals from 11 participants over many weeks. The gathered data enabled us to train a classifier that accurately recognizes strong affective states.

15 citations

Posted Content
30 Apr 2020
TL;DR: There is no versatile device suitable for all purposes in recognition and analysis of emotion, stress, meditation, sleep, and physical activity in off-the-shelf devices revised.
Abstract: Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals. In this study, we revised 55 off-the-shelf devices in recognition and analysis of emotion, stress, meditation, sleep, and physical activity, especially in field studies. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Empatica E4 and Microsoft Band 2 are good at emotion, stress, and together with Oura Ring at sleep research. Apple, Samsung, Garmin, and Fossil smart watches are proper in activity examination, while Muse and DREEM EEG headbands are suitable for meditation.

14 citations

Journal ArticleDOI
TL;DR: The Objected-Oriented Bayesian Network (OOBN) application for risk assessment of the MOB scenario is presented, and the OOBN model is developed to probabilistically capture the key accident influencing factors in fragmented structures.

12 citations

Proceedings ArticleDOI
07 Dec 2020
TL;DR: In this article, the WellAff system was proposed to recognize affective states for wellbeing support in patients suffering from bipolar disorder, in particular patients with chronic kidney disease and bipolar disorder.
Abstract: Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals in our everyday life. We propose the WellAff system able to recognize affective states for wellbeing support. It also includes health care scenarios, in particular patients with chronic kidney disease suffering from bipolar disorders. For the need of a large-scale field study, we revised over 50 off-the-shelf devices in terms of usefulness for emotion, stress, meditation, sleep, and physical activity recognition and analysis. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Using Empatica E4 and Samsung Galaxy Watch, we have recorded physiological signals from 11 participants over many weeks. The gathered data enabled us to train a classifier that accurately recognizes strong affective states.

8 citations

References
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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]....

    [...]