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 in | Signal, image and video processing Vol. 16; no. 8; pp. 2259 - 2267 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Springer London
01.11.2022
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 1863-1703 1863-1711 |
DOI | 10.1007/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. |
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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. |
Author_xml | – sequence: 1 givenname: Manish N. orcidid: 0000-0002-3591-4617 surname: Tibdewal fullname: Tibdewal, Manish N. email: mntibdewal@gmail.com, mntibdewal@ssgmce.ac.in organization: Electronics and Telecommunication Engineering Department, S.S.G. M. College of Engineering – sequence: 2 givenname: Dhanashri N. surname: Nagbhide fullname: Nagbhide, Dhanashri N. organization: Electronics and Telecommunication Engineering Department, S.S.G. M. College of Engineering – sequence: 3 givenname: M. surname: Mahadevappa fullname: Mahadevappa, M. organization: School of Medical Science and Technology, Indian Institute of Technology – sequence: 4 givenname: AjoyKumar surname: Ray fullname: Ray, AjoyKumar organization: School of Medical Science and Technology, Indian Institute of Technology – sequence: 5 givenname: Ashok surname: Dhoke fullname: Dhoke, Ashok – sequence: 6 givenname: Monica surname: Malokar fullname: Malokar, Monica organization: Malokar Hospital |
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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 |
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