Fractal complexity of EEG signal
نویسندگان
چکیده
The paper deals with the presence of exponential or power-law decay in the power spectra of electroencephalogram (EEG). About 2300 EEG time series recorded during relaxed wakefulness were analysed. The whole spectrum of EEG was studied and power-law decay of about 2.28 prevailing over the exponential falling off was established. Correspondence between spectrum power-law decay and correlation dimension estimated for EEG was also validated.
منابع مشابه
Brain complexity increases during the manic episode of bipolar mood disorder type I
Fractal dimension of the electroencephalographic (EEG) signal has been argued to reflect the complexity of the underlying brain processes. To this date, conventional studies of EEG in mood disorders have not been able to distinguish between patients and normal individuals. Here we show that, compared to normal subjects, EEG fractal dimension is significantly augmented in the manic episode of bi...
متن کاملBrain complexity increases during the manic episode of bipolar mood disorder type I
Fractal dimension of the electroencephalographic (EEG) signal has been argued to reflect the complexity of the underlying brain processes. To this date, conventional studies of EEG in mood disorders have not been able to distinguish between patients and normal individuals. Here we show that, compared to normal subjects, EEG fractal dimension is significantly augmented in the manic episode of bi...
متن کاملAdaptive Segmentation with Optimal Window Length Scheme using Fractal Dimension and Wavelet Transform
In many signal processing applications, such as EEG analysis, the non-stationary signal is often required to be segmented into small epochs. This is accomplished by drawing the boundaries of signal at time instances where its statistical characteristics, such as amplitude and/or frequency, change. In the proposed method, the original signal is initially decomposed into signals with different fr...
متن کاملAn Adaptive Segmentation Method Using Fractal Dimension and Wavelet Transform
In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...
متن کاملAn Adaptive Segmentation Method Using Fractal Dimension and Wavelet Transform
In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...
متن کاملA New Approach for Investigating the Complexity of Short Term EEG Signal Based on Neural Network
Background and purpose: The nonlinear quality of electroencephalography (EEG), like other irregular signals, can be quantified. Some of these values, such as Lyapunovchr('39')s representative, study the signal path divergence and some quantifiers need to reconstruct the signal path but some do not. However, all of these quantifiers require a long signal to quantify the signal complexity. Mate...
متن کامل