@InProceedings{8081470,
  author = {L. Shaw and A. Routray},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Topographical pattern analysis using wavelet based coherence connectivity estimation in the distinction of meditation and non-meditation EEG},
  year = {2017},
  pages = {1554-1558},
  abstract = {Classification of EEG signal involved in a particular cognitive activity has found many application in brain-computer interface (BCI). In specific, use of classification algorithms to highly multivariate non-stationary recordings like EEG is a challenging and promising task. This study investigated two sub-stantial novelty of the topics, (1) Distinction between meditation (Kriya Yoga) and non-meditation state allied EEG, (2) Characterization of the underlying mechanism of cognitive process that is associated with meditation using topographical analysis. The topographic wavelet coherence based brain connectivity between two different groups is shown. Two groups of data, one with 23 meditators (meditator group) and other with ten non-meditators (controlled group) are analyzed. The spatial distribution between two groups can be well distinguished by the topographical approach. The quantification has been done by the colour intensity embedded in the topographical plots. The wavelet coherence is found to be a different parameter to represent the distinctiveness between two groups. The time-frequency quantification regarding wavelet coherence spectrum is shown the unique patterns among meditators and non-meditators. Thus time-frequency based wavelet coherence has found to be an unusual brain pattern in the distinction between meditators and non-meditators.},
  keywords = {bioelectric potentials;brain-computer interfaces;cognition;electroencephalography;medical signal processing;neurophysiology;signal classification;time-frequency analysis;wavelet transforms;topographical pattern analysis;brain-computer interface;EEG signal classification;multivariate nonstationary recordings;cognitive activity estimation;topographic wavelet coherence based brain connectivity estimation;time-frequency based wavelet coherence connectivity estimation;Coherence;Electroencephalography;Wavelet analysis;Time series analysis;Wavelet transforms;Time-frequency analysis;Estimation},
  doi = {10.23919/EUSIPCO.2017.8081470},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347702.pdf},
}

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In specific, use of classification algorithms to highly multivariate non-stationary recordings like EEG is a challenging and promising task. This study investigated two sub-stantial novelty of the topics, (1) Distinction between meditation (Kriya Yoga) and non-meditation state allied EEG, (2) Characterization of the underlying mechanism of cognitive process that is associated with meditation using topographical analysis. The topographic wavelet coherence based brain connectivity between two different groups is shown. Two groups of data, one with 23 meditators (meditator group) and other with ten non-meditators (controlled group) are analyzed. The spatial distribution between two groups can be well distinguished by the topographical approach. The quantification has been done by the colour intensity embedded in the topographical plots. The wavelet coherence is found to be a different parameter to represent the distinctiveness between two groups. The time-frequency quantification regarding wavelet coherence spectrum is shown the unique patterns among meditators and non-meditators. 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Routray},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Topographical pattern analysis using wavelet based coherence connectivity estimation in the distinction of meditation and non-meditation EEG},\n year = {2017},\n pages = {1554-1558},\n abstract = {Classification of EEG signal involved in a particular cognitive activity has found many application in brain-computer interface (BCI). In specific, use of classification algorithms to highly multivariate non-stationary recordings like EEG is a challenging and promising task. This study investigated two sub-stantial novelty of the topics, (1) Distinction between meditation (Kriya Yoga) and non-meditation state allied EEG, (2) Characterization of the underlying mechanism of cognitive process that is associated with meditation using topographical analysis. The topographic wavelet coherence based brain connectivity between two different groups is shown. Two groups of data, one with 23 meditators (meditator group) and other with ten non-meditators (controlled group) are analyzed. The spatial distribution between two groups can be well distinguished by the topographical approach. The quantification has been done by the colour intensity embedded in the topographical plots. The wavelet coherence is found to be a different parameter to represent the distinctiveness between two groups. The time-frequency quantification regarding wavelet coherence spectrum is shown the unique patterns among meditators and non-meditators. 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