A critical review of the principal strategies of the EEG description as a piecewise stationary process is given. Achievements, problems, and prospects of parametric and nonparametric strategies of the EEG segment structure assessment are discussed on the basis of the literature and the author's data. Among them, attention is directed to the adequacy of the EEG segmentation based on the autoregr...

introduction increase in alpha band is observed when blood perfusion in frontal area of head decreases. the present study evaluated some changes in the alpha band particularly, alpha-1 of frontal and central areas of the head, when several areas were exposed simultaneously to magnetic field. materials and methods five points of head (f3, f4, cz, t3, and t4) of twenty healthy male participants w...

Decomposition of non-stationary signals such as electroencephalogram (EEG) and electrocardiogram (ECG) into stationary or quasi-stationary, signal segmentation, is a wellknown problem in many signal processing applications. Previous methods for segmenting a signal had problems such as slow speed, low performance, and several parameters which must be defined experimentally. In this paper a new m...

This paper compares the correlation dimension (D2) and Higuchi fractal dimension (HFD) approaches in estimating BIS index based on of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room and different anesthetic drugs, including propofol and isoflurane were used. For better analysis, application of adaptive segmentation on EEG signal for estimating...

This paper compares the correlation dimension (D2) ,Higuchi fractal dimension (HFD),Katz fractal dimension(KFD)and Sevcik fractal dimension(SFD) approaches in estimating Depth of Anesthesia (DOA) based on of electroencephalogram (EEG). The single-channel EEG data was captured in both ICU and operating room and different anesthetic drugs, including propofol and isoflurane were used. For better a...

the volterra model is widely used for nonlinearity identification in practical applications. in this paper, we employed volterra model to find the nonlinearity relation between electroencephalogram (eeg) signal and the noise that is a novel approach to estimate noise in eeg signal. we show that by employing this method. we can considerably improve the signal to noise ratio by the ratio of at le...

In this paper, unique approach is presented for the electroencephalography (EEG) signals analysis. This is based on Eigen values distribution of a matrix which is called as scaled Hankel matrix. This gives us a way to find out the number of Eigen values essential for noise reduction and extraction of signal in singular spectrum analysis. This paper gives us an approach to classify the EEG signa...

In many applications of the signal processing such as automatic analysis of EEG signal, it is needed that signal is split to smaller parts that each part has the same statistical characterizations such as the amplitude and frequency. This act has been called signal segmentation. In this paper, the signal is initially filtered by weighted moving average (WMA). Not only WMA can emphasize recent e...

Improving EEG signal interpretation, specificity, and sensitivity is a primary focus of many current investigations, and the successful application of EEG signal processing methods requires a detailed knowledge of both the topography and frequency spectra of low-amplitude, high-frequency craniofacial EMG. This information remains limited in clinical research, and as such, there is no known reli...