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NITAAI-Veda.nyf > Soul Science God Philosophy > Science and Spiritual Quest > Section 4 Towards a New Biology > MATHEMATICAL TECHNIQUES FOR STUDY OF EEG > 1. Introduction

1. Introduction


Electroencephalogram (EEG) is used for understanding gross disturbances of consciousness encountered in various physiological and pathological states. The macroscopic brain activity seen in EEG has been subjected to a variety of analytic methods in the past. Frequency information of EEG signals is of importance in various applications. The meditative process has also been investigated by using the spectral estimates of EEG. Stigsby et al. (1981) have provided evidence of alterations in EEG, during meditation to establish that meditation can be distinguished from other states of consciousness [1]. We have approached the analysis of EEG during meditation from the view point of detecting the change in its structural pattern by fractal dimension and quantifying its dynamical parameter of dimensional complexity through attractor dimension. The frequency changes have also been presented in a visually meaningful way by overlapping spectral plots to get Compressed Spectral Array [2]Neural networks have been successfully employed to process EEG signals. The neural networks have been employed successfully for EEG artifact processing. Studies involving neural networks for EEG data compression have been performed. Some of the various ANN-based systems developed were aimed at detecting specific graphoelements: K-complex waves, identification of EEG spikes or sleep spindles [3], or recognition of topographic patterns of EEG spectra. Recently, several EEG processing neural network-based systems have been proposed for monitoring depth of anesthesia [4]. Using EEG recordings,several investigators have also developed neural network-based systems to access the vigilance level of the subject under record [5]. In view of these works, we have considered the application of neural networks in the classification of different levels of consciousness using meditation EEG data.