NITAAI-Veda.nyf > Soul Science God Philosophy > Science and Spiritual Quest > Section 4 Towards a New Biology > MATHEMATICAL TECHNIQUES FOR STUDY OF EEG > Abstract |

**MATHEMATICAL TECHNIQUES FOR STUDY OF EEG DATA RECORDED DURING
MEDITATION**

D. Narayana Dutt

Dept. of Electrical Communication Engineering, Indian Institute of
Science, Bangalore 560012, India

**Abstract**

Meditation is considered to be an altered state of consciousness
associated with heightened cognitive functions and transcendental experiences.
The neural dynamics in meditative states needs to be explored and an objective
analysis of such states is required. Here, we have investigated the dimensional
complexity of electroencephalogram (EEG) signals from the brain of subjects in
yogic meditation. Several channels of EEG have been analyzed in terms of
compressed spectral array (CSA), running fractal dimension and running
attractor dimension during the process of meditation. The CSA yields some
interesting features. The running fractal plots show low average fractal
dimension values during pre-meditative and post-meditative periods. During
meditation there is an increase in the average fractal dimension value. The
attractor dimension values also show changes. As the meditation progresses the
average attractor dimension rises to a value which is more than that for the
premeditative period. It shows a decline during some stages of meditation. The
results indicate that the attractor dimension estimation is more effective in
depicting the dynamics of the brain in a highly complex state. The
investigation reveals that chaotic dynamics provides a mechanism for low dimensional
control of neuronal oscillations in meditation.

Next, we have studied the efficacy of neural network approach in
differentiating various levels of consciousness using EEG signals. We
considered 60 segments of premeditation data, 140 segments of meditation data,
140 segments of deep meditation data and 60 segments of post meditation data.
We have chosen 8 features as input to the neural networks. The features we have
chosen are mean, variance, fractal dimension, complexity measure, and powers in
alpha, beta, theta and delta waves. Four classification algorithms were
compared viz., k-neighbors, RBF networks, Support vector machines (SVM) and
back propagation networks for different set of features. The obtained
accuracies for the back propagation network, RBF network units and support
vector machine with RBF kernel are higher than for k-neighbors. We are able to
attain optimum accuracy of 99.6% with SVM in case of two-category problem
(Meditation and Pre-meditation), 99.6% with SVM in case of three-category
problem (PreŽmeditation, Meditation and Post meditation) and 99.38% with SVM in
case of four-category problem (Pre-meditation, Meditation, Deep meditation and
Post meditation). Thus this work has shown the feasibility of the use of neural
networks in the classification of EEG meditation data.

In summary, this work has demonstrated the efficacy of
mathematical techniques in establishing that meditative state is indeed a well
defined and distinct state of consciousness where clear changes are observed in
EEG during meditation. The fact that an automated method like neural network
approach can differentiate such states from other states clearly shows that
there is no human bias involved in such studies.