Dynamics of brain signals such as electroencephalogram (EEG) can be characterized as a sequence of quasi-stable patterns. Such patterns in the brain signals can be associated with coordinated neural oscillations, which can be modeled by non-linear systems. Further, these patterns can be quantified through dynamical non-stationarity based on detection of qualitative changes in the state of the s...

In this paper, motor imagery EEG signals are preprocessed, using the state-of-the-art measurement and control software LabVIEW. Firstly, the elliptic filter is used for digital band-pass filtering. Then the soft-thresholding approach is adopted for wavelet denoising to enable programming with the BCI Competition 2005 dataset. The experimental results show that the method not only saves a lot of...

At the presentmoment, threemethods can record functional and physiological changes within the brain with high temporal resolution of neuronal interactions at the network level: the electroencephalogram (EEG), the magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI); each of these has advantages and shortcomings. MEG is not practical for experimental work when subjects ma...

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 di...

Seizure activity takes place due to an irregular excessive electrical action in the human brain. The electrical activity in the form of brain waves (signals) can measured by using the device called Electroencephalogram (EEG). In this paper, we have reviewed our work so far made regarding EEG signals. Comparative between Artificial Neural Network (ANN) and Support Vector Machine (SVM) this revie...

Electroencephalogram (EEG) is used to record electrical activity of brain. Human brain is fascinated by the different idea of thoughts and feelings generated from external and internal stimuli. Feature extraction and classification of EEG signal plays an important role in diagnosis of various brain diseases and mental tasks. In this paper, powerful technique of empirical mode decomposition (EMD...

Brain maturation in adolescence is mirrored by the EEG as a pronounced decrease in low frequency activity. This EEG power attenuation parallels reductions of structural and metabolic markers of neuronal maturation (i.e., gray matter loss and decrease of absolute cerebral glucose utilization). However, it is largely unknown what causes these electrophysiological changes, and how this functional ...

It has long been assumed that the surface electroencephalography (EEG) signal depends on both the amplitude and spatial synchronization of underlying neural activity, though isolating their respective contribution remains elusive. To address this, we made simultaneous surface EEG measurements along with intracortical recordings of local field potentials (LFPs) in the primary visual cortex of be...

Electroencephalograms (EEGs) contain responses to visual stimulus, however, signal noise often prevents these from being easily obtained. To classify EEG waveforms, a signal processing procedure using the relationship between EEG and ERP, which is the summation of EEG waveforms, was developed. The processing technique involves the prediction of signals using Support Vector Regression. The proce...

The human electroencephalogram (EEG) is often corrupted by ocular artefacts (OAs) caused by the movement of the eyes and/or the eyelids, making the recognition of abnormal EEG signals more difficult. The removal of OAs using conventional signal processing is complicated by the similarity between abnormal EEGs and OAs, which can lead to corruption of the EEG signal. The paper describes the devel...