SVM classification of EEG signal to analyze the effect of OM Mantra meditation on the brain
01 Dec 2019-
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.
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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
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
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
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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TL;DR: Significant deactivation was observed bilaterally during ‘OM’ chanting in comparison to the resting brain state in bilateral orbitofrontal, anterior cingulate, parahippocampal gyri, thalami and hippocampi, and the right amygdala too demonstrated significant deactivation.
Abstract: Background: A sensation of vibration is experienced during audible 'OM' chanting. This has the potential for vagus nerve stimulation through its auricular branches and the effects on the brain thereof. The neurohemodynamic correlates of 'OM' chanting are yet to be explored. Materials and Methods: Using functional Magnetic Resonance Imaging (fMRI), the neurohemodynamic correlates of audible 'OM' chanting were examined in right-handed healthy volunteers (n=12; nine men). The 'OM' chanting condition was compared with pronunciation of ssss as well as a rest state. fMRI analysis was done using Statistical Parametric Mapping 5 (SPM5). Results: In this study, significant deactivation was observed bilaterally during 'OM' chanting in comparison to the resting brain state in bilateral orbitofrontal, anterior cingulate, parahippocampal gyri, thalami and hippocampi. The right amygdala too demonstrated significant deactivation. No significant activation was observed during 'OM' chanting. In contrast, neither activation nor deactivation occurred in these brain regions during the comparative task - namely the 'ssss' pronunciation condition. Conclusion: The neurohemodynamic correlates of 'OM' chanting indicate limbic deactivation. As similar observations have been recorded with vagus nerve stimulation treatment used in depression and epilepsy, the study findings argue for a potential role of this 'OM' chanting in clinical practice.
122 citations
"SVM classification of EEG signal to..." refers background in this paper
...Many researchers have found the effect of OM mantra meditation on human beings using an auditory middle latency evoked potentials [5-6], skin resistance level, heart rate, respiratory rate [7-8 ], Functional magnetic resonance imaging [9-12]....
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Journal Article•
TL;DR: The meditators showed a statistically significant reduction in heart rate during meditation compared to the control period (paired 't' test), and during both types of sessions there was a comparable increase in the cutaneous peripheral vascular resistance.
Abstract: The autonomic and respiratory variables were studied in seven experienced meditators (with experience ranging from 5 to 20 years). Each subject was studied in two types of sessions--meditation (with a period of mental chanting of "OM") and control (with a period of non-targetted thinking). The meditators showed a statistically significant reduction in heart rate during meditation compared to the control period (paired 't' test). During both types of sessions there was a comparable increase in the cutaneous peripheral vascular resistance. Keeping in mind similar results of other authors, this was interpreted as a sign of increased mental alertness, even while being physiologically relaxed (as shown by the reduced heart rate).
116 citations
"SVM classification of EEG signal to..." refers background in this paper
...Many researchers have found the effect of OM mantra meditation on human beings using an auditory middle latency evoked potentials [5-6], skin resistance level, heart rate, respiratory rate [7-8 ], Functional magnetic resonance imaging [9-12]....
[...]
Journal Article•
TL;DR: A statistical based system for human emotions classification by using electroencephalogram (EEG) and back-propagation neural network is applied for the classification of human emotions, achieving an overall classification rate as high as 95%.
Abstract: A statistical based system for human emotions classification by using electroencephalogram (EEG) is proposed in this paper. The data used in this study is acquired using EEG and the emotions are elicited from six human subjects under the effect of emotion stimuli. This paper also proposed an emotion stimulation experiment using visual stimuli. From the EEG data, a total of six statistical features are computed and back-propagation neural network is applied for the classification of human emotions. In the experiment of classifying five types of emotions: Anger, Sad, Surprise, Happy, and Neutral. As result the overall classification rate as high as 95% is achieved.
96 citations
TL;DR: The usage of statistics over the set of the features representing the electroencephalogram (EEG) signals confirmed that the proposed Multilayer perceptron neural network (MLPNN) has potential in detecting the Electroencephalographic changes.
86 citations
"SVM classification of EEG signal to..." refers background in this paper
...These parameters describe the behavior of the signal [23]....
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01 Jan 1988
TL;DR: This dissertation develops a nonlinear multiresolution transform which translates when the signal is translated called the dyadic wavelet transform and studies the application of this signal representation to data compression in image coding, texture discrimination and fractal analysis.
Abstract: Multiresolution representations are very effective for analyzing the information in images. In this dissertation we develop such a representation for general purpose low-level processing in computer vision. We first study the properties of the operator which approximates a signal at a finite resolution. We show that the difference of information between the approximation of a signal at the resolutions 2$\sp{j+1}$ and 2$\sp{j}$ can be extracted by decomposing this signal on a wavelet orthonormal basis of ${\bf L}({\bf R}\sp{n}$). In ${\bf L}\sp2({\bf R})$, a wavelet orthonormal basis is a family of functions $\left\lbrack\sqrt{2\sp{j}}\ \psi(2\sp{j}x+n)\right\rbrack\sb{(j,n)\in{\rm Z}\sp2}$, which is built by dilating and translating a unique function $\psi(x)$, called a wavelet. This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm of complexity n log(n). We study the application of this signal representation to data compression in image coding, texture discrimination and fractal analysis. The multiresolution approach to wavelets enables us to characterize the functions $\psi(x) \in {\bf L}\sp2({\bf R})$ which generate an orthonormal basis.
The inconvenience of a linear multiresolution decomposition is that it does not provide a signal representation which translates when the signal translates. It is therefore difficult to develop pattern recognition algorithms from such representations. In the second part of the dissertation we introduce a nonlinear multiscale transform which translates when the signal is translated. This representation is based upon the zero-crossings and local energies of a multiscale transform called the dyadic wavelet transform. We experimentally show that this representation is complete and that we can reconstruct the original signal with an iterative algorithm. We study the mathematical properties of this decomposition and show that it is well adapted to computer vision. To illustrate the efficiency of this Energy Zero-Crossings representation, we have developed a coarse to find matching algorithm on stereo epipolar scan lines. While we stress the applications towards computer vision, wavelets are useful to analyze other types of signal such as speech and seismic-waves.
77 citations
"SVM classification of EEG signal to..." refers background in this paper
...It has a lack of antialiasing capacity and is not shift invariant thereby to increase the describing ability for features [22]....
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