Multi-feature extraction, analysis, and classification for control and meditators’ electroencephalogram

Meditation has a metaphysical impact on human brain functioning. It is of utmost required to infer the cognitive effects of meditation using an electroencephalogram (EEG). In this novel work, the analyses of EEG signals’ features are extracted for cognitive effects on a human brain for meditation in...

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Published inSignal, image and video processing Vol. 16; no. 8; pp. 2259 - 2267
Main Authors Tibdewal, Manish N., Nagbhide, Dhanashri N., Mahadevappa, M., Ray, AjoyKumar, Dhoke, Ashok, Malokar, Monica
Format Journal Article
LanguageEnglish
Published London Springer London 01.11.2022
Springer Nature B.V
Subjects
Online AccessGet full text
ISSN1863-1703
1863-1711
DOI10.1007/s11760-022-02191-6

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Abstract Meditation has a metaphysical impact on human brain functioning. It is of utmost required to infer the cognitive effects of meditation using an electroencephalogram (EEG). In this novel work, the analyses of EEG signals’ features are extracted for cognitive effects on a human brain for meditation intervention of 25 subjects. To analyze the meditation effects, this study examines the feasibility of statistical, spatial, spectral, coherence features, and time–frequency analysis of EEG signals for control and meditator group. Based on the effective features the various classifiers are used to compare the accuracy and distinguish a subject as control or meditator. The results demonstrate that the Support Vector Machine (SVM) gives better accuracy than Artificial Neural Network (ANN) and k-Nearest Neighbors (KNN). The statistical analysis shows that the Variance and Sample Entropy decreased in meditators whereas, in spatial analysis, the Mahalanobis distance increased. The spectral analysis stated that theta power has increased 88% of subjects whereas the alpha power is increased for the entire subjects after meditation. The coherence observed in the pre-frontal lobes’ electrode pair is more in the meditators than in the control group. Eventually, meditation improves relaxation, cognitive functions, calmness, and mental concentration.
AbstractList Meditation has a metaphysical impact on human brain functioning. It is of utmost required to infer the cognitive effects of meditation using an electroencephalogram (EEG). In this novel work, the analyses of EEG signals’ features are extracted for cognitive effects on a human brain for meditation intervention of 25 subjects. To analyze the meditation effects, this study examines the feasibility of statistical, spatial, spectral, coherence features, and time–frequency analysis of EEG signals for control and meditator group. Based on the effective features the various classifiers are used to compare the accuracy and distinguish a subject as control or meditator. The results demonstrate that the Support Vector Machine (SVM) gives better accuracy than Artificial Neural Network (ANN) and k-Nearest Neighbors (KNN). The statistical analysis shows that the Variance and Sample Entropy decreased in meditators whereas, in spatial analysis, the Mahalanobis distance increased. The spectral analysis stated that theta power has increased 88% of subjects whereas the alpha power is increased for the entire subjects after meditation. The coherence observed in the pre-frontal lobes’ electrode pair is more in the meditators than in the control group. Eventually, meditation improves relaxation, cognitive functions, calmness, and mental concentration.
Author Dhoke, Ashok
Ray, AjoyKumar
Malokar, Monica
Nagbhide, Dhanashri N.
Mahadevappa, M.
Tibdewal, Manish N.
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value
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Snippet Meditation has a metaphysical impact on human brain functioning. It is of utmost required to infer the cognitive effects of meditation using an...
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SubjectTerms Artificial neural networks
Brain
Coherence
Computer Imaging
Computer Science
Electroencephalography
Feasibility studies
Feature extraction
Image Processing and Computer Vision
Meditation
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Spatial analysis
Spectrum analysis
Statistical analysis
Support vector machines
Time-frequency analysis
Vision
Title Multi-feature extraction, analysis, and classification for control and meditators’ electroencephalogram
URI https://link.springer.com/article/10.1007/s11760-022-02191-6
https://www.proquest.com/docview/2719732512
Volume 16
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