Analyzing time series of brain electrical activity through wavelet transform

Physics

EEG (Electroencephalography) is deals with human brain. EEG signals more useful in neurology and play prominent role in study about activity of the brain. EEG study are mostly useful for diagnosis of Epileptic activity. But afterward it is also useful for understanding of cognitive process. The EEG technique read by neurologist who has taken specific training in the interpretation of EEGs. The purpose of the paper is to construct a base for the development of future methodology for reading and interpreting EEG correctly even in absence of qualified person. In present work, used EEG signals for analyzed electrical activity of brain at different condition of person: normal behavior of person (eyes open (a) and eyes close (b)), seizure free interval (epileptic zone (c) and hippocampal formation (d)) and the seizure activity (e) via continuous wavelet transform. Continuous Wavelet transform is known as mathematical tool which provides Multiresolution analysis. It is found that Morlet wavelet coefficients (low pass coefficient and high pass coefficient) clearly identify distinguishing features of the signals using matlab technique. After pin-pointing these robust features in the wavelet scalogram, systematically work on the autocorrelation property of the wavelet coefficients of the signal. This can help tremendously in the developing front telemedicine. Using EEG data extracted from University of Bonn, Germany, which is available in public domain [12]. So in present paper, continuous wavelet transform can be employed here for making precise analysis of electrical activity of brain at different condition. So the difference of EEG time series at different state represent by periodic nature and also find out behavior of the brain.

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