Electroencephalogram (EEG), the measures and records of the electrical activity of the brain, exhibits evidently nonlinear, non-stationary, chaotic and complex dynamic properties. Based on these properties, many nonlinear dynamical analysis techniques have emerged, and much valuable information has been extracted from complex EEG signals using these nonlinear analysis techniques. Among these te...

Electroencephalography (EEG) signals’ interpretation is based on waveform analysis, where meaningful information should emerge from a plethora of data. Nonetheless, the continuous increase in computational power and the development of new data processing algorithms in the recent years have put into reach the possibility of analysing raw EEG signals. Bearing that motivation, the authors propose ...

To elucidate the cortical control of handwriting, we examined time-dependent statistical and correlational properties of simultaneously recorded 64-channel electroencephalograms (EEGs) and electromyograms (EMGs) of intrinsic hand muscles. We introduced a statistical method, which offered advantages compared to conventional coherence methods. In contrast to coherence methods, which operate in th...

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 expansion. The MF measure of the IMFs has been...

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 ...

Abstract Electroencephagram (EEG) is the recording of electrical activity of the brain. Though it is intended to record cerebral signals,it also records the signals that are not of cerebral origin called artifacts. Artifact removal from EEG signals is essential for better diagnosis. This paper proposes a hybrid learning algorithm based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for elimin...

At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain c...

The encephalography has undergone massive progress during 100’s of year. The existence of electrical currents in the brain was discovered in 1875 by an English physician Richard Caton. In 1924 Hans Berger, a German neurologist, used ordinary radio equipment to amplify the brain's electrical activity measured on the human scalp. The electroencephalogram (EEG) is defined as electrical activity of...

A new algorithm for Electroencephalogram (EEG) signals classification is proposed in this paper. This classification is used for automatic detection of patients with epilepsy in a medical system for decision support. The classification algorithm is based on Ordered Fuzzy Decision Tree (OFDT) for EEG signals. The application of OFDT requires special transformation of EEG signal that is named as ...

Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of brain damage, for categorizing sleep stages and various central nervous system disorders like seizures and epilepsy. The EEG source signals are mixed however with other signals such as Electrooculog...