abstract background and aims: paper-pencil tests have always its own problems in the mental disorders evaluation, including learning questions, bad or good blazon are the problems with this methodology. this study aimed to propose a new alternative method of measuring mental disorders without paper-pencil test using eeg. methods: the research society involved depressed patients referred the psy...

Background: Muscle fatigue has been known to influence brain activity, but very little is known about how cortical centers respond to muscle fatigue.Objective: This study was conducted to investigate the effects of muscle contraction and fatigue induced by two different percents of maximal voluntary contraction (MVC) on Electroencephalography (EEG) signals.Material and Methods: In t...

Objective: In this research, a new approach termed as “evolutionary-based brain map” is presented as a diagnostic tool to classify schizophrenic and control subjects by distinguishing their electroencephalogram (EEG) features.Methods: Particle swarm optimization (PSO) is employed to find discriminative frequency bands from different EEG channels. By deploying the energy of those selected fr...

Introduction: Brain visual evoked potential (VEP) signals are commonly known to be accompanied by high levels of background noise typically from the spontaneous background brain activity of electroencephalography (EEG) signals. Material and Methods: A model based on dyadic filter bank, discrete wavelet transform (DWT), and singular value decomposition (SVD) was developed to analyze the raw data...

The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...

The analysis of EEG signals plays a vital role in the detection of Seizure. The EEG signal of a normal person varies when compared to that of a seizure affected person. A new wavelet is created which closely represents a normal EEG wave. The discrete wavelet transform using the new wavelet family is applied to the input EEG signals. Since the new wavelet represents the normal EEG wave pattern t...

objective: in this research, a new approach termed as “evolutionary-based brain map” is presented as a diagnostic tool to classify schizophrenic and control subjects by distinguishing their electroencephalogram (eeg) features. methods: particle swarm optimization (pso) is employed to find discriminative frequency bands from different eeg channels. by deploying the energy of those selected frequ...