Classification of Electroencephalographic Changes in Meditation and Rest: using Correlation Dimension and Wavelet Coefficients
Atefeh Goshvarpour, Ateke Goshvarpour
International Journal of Information Technology and Computer Science March 27, 2012 DOI: 10.5815/ijitcs.2012.03.04 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Sample size | 25 |
| Population | Healthy women |
| Intervention | Meditation |
| Topics | Meditation |
| Keywords | Wavelet Dimension graph theory Rest music Correlation dimension Pattern recognition psychology Electroencephalography Artificial intelligence Mathematical analysis Pure mathematics Acoustics Theology Fractal dimension |
| Citations | 10 |
| Key findings | The Fisher discriminant and Parzen classifier using correlation dimensions achieved 92.37% accuracy in classifying rest and meditation from EEG signals, outperforming wavelet-based features. |
Abstract
Meditation is a practice of concentrated focus upon the breath in order to still the mind. In this paper we have investigated an algorithm to classify rest and meditation, by processing of electroencephalogram (EEG) signals through the Wavelet and nonlinear methods. For this purpose, two types of EEG time series (before, and during meditation) of 25 healthy women are collected in the meditation clinic in Mashhad. Correlation dimension and Wavelet coefficients at the forth decomposition level of EEG signals in Fz, Cz and Pz are extracted and used as an input of different classifiers. In order to evaluate performance of the classifiers, the classification accuracies and mean square error (MSE) of the classifiers were examined. The results show that the Fisher discriminant and Parzen classifier trained on both composite features obtain higher accuracy than that of the others. The total classification accuracy of the Fisher discriminant and Parzen classifier applying Wavelet coefficients was 85.02% and 84.75%, respectively which is raised to 92.37% in both classifiers using Correlation dimensions.