Testing for nonlinearity in time series: the method of surrogate data
Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Nonlinear time series analysis
Nonlinear time series analysis
Phase Correlations in Human EEG Signal: A Case Study
DELTA '04 Proceedings of the Second IEEE International Workshop on Electronic Design, Test and Applications
Linear correlation between fractal dimension of EEG signal and handgrip force
Biological Cybernetics
Signal Processing - Neuronal coordination in the brain: A signal processing perspective
Signal Processing - Neuronal coordination in the brain: A signal processing perspective
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
Electroencephalograph Signal Analysis During Bramari
ICIT '06 Proceedings of the 9th International Conference on Information Technology
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Analysis of EEG background activity in Autism disease patients with bispectrum and STFT measure
ICCOM'07 Proceedings of the 11th Conference on 11th WSEAS International Conference on Communications - Volume 11
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Non-linear analysis of EEG signals at various sleep stages
Computer Methods and Programs in Biomedicine
Fuzzy model for detection and estimation of the degree of autism spectrum disorder
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Detecting different tasks using EEG-Source-Temporal features
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
Computers in Biology and Medicine
Computers in Biology and Medicine
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The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.