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In this paper, we present a new method for classification of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) method. The intrinsic mode functions (IMFs) generated by EMD method can be considered as a set of amplitude and frequency modulated (AM-FM) signals. The Hilbert transformation of IMFs provides an analytic signal(More)
A new method for analysis of electroencephalogram (EEG) signals using Empirical Mode Decomposition (EMD) and Fourier-Bessel (FB) expansion has been presented in this paper. The EMD decomposes a EEG signal into a finite set of band-limited signals termed Intrinsic Mode Functions (IMFs). The mean frequency (MF) for each IMF has been computed using FB(More)
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for(More)
In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation(More)
Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. This paper presents a new method for classification of ictal and seizure-free EEG signals. The proposed method is based on the empirical mode(More)
Center of pressure (COP) measurements are often used to identify balance problems. A new method for COP signal analysis using mean frequency is proposed in this paper. The computation of mean frequency is based on the Fourier–Bessel (FB) expansion that is suitable for use in non-stationary COP signals. In addition, FB expansion provides better frequency(More)
This paper presents a new method for analysis of center of pressure (COP) signals using Empirical Mode Decomposition (EMD). The EMD decomposes a COP signal into a finite set of band-limited signals termed as intrinsic mode functions (IMFs). Thereafter, a signal processing technique used in continuous chaotic modeling is used to investigate the difference(More)