Resting state functional connectivity, which is defined as temporal correlation of spontaneous activity between diverse brain regions, has been reported to form resting state networks (RSNs), consisting of a specific set of brain regions, based on functional magnetic resonance imaging (fMRI). Recently, studies using near-infrared spectroscopy (NIRS) reported that NIRS signals also show temporal...

In this paper, a method to recognise persons using brain signal features classified by Grow and Learn (GAL) network is proposed. The features are obtained from brain signals and consist of gamma band spectral power. These brain signals are recorded from 61 electrodes located on the human scalp while the subjects are seeing a visual stimulus in the form of a picture. The experimental results usi...

The human brain is a complex system consist of connected nerve cells that adapts with and learn from the environment by changing its regional activities. Synchrony between these regional activities called functional network changes during the life, and with learning of new skills. Time perception and interval discrimination are among the most necessary skills for the human being to perceive mot...

A Brain-Computer Interface (BCI) is a communication system that decodes and transfers information directly from the brain to external devices. The electroencephalogram (EEG) technique used measure electrical signals corresponding commands occurring in control functions. for applications BCI are called Motor Imagery (MI) EEG signals. noisy, so it important use right methods recognize patterns co...

introduction: according to role of emotions in human’s life, if they can be recognized contemporary with facial expression by eeg signals, it can discriminate between real abd artificial emotions. this ability can be applied in lie detector and be also helpful to patients that able to feel emotions but can’t show it with facial expression. methods: in this study, some emotion induction experime...

introduction many features, emerging from mathematical techniques, have been used in the analysis of brain signals. in this study, the physical quantity of “moment of inertia” (moi) was introduced as a feature to enhance high-frequency waves (hfws) in electroencephalography (eeg). materials and methods in this research, the recorded eegs from f3, f4, and cz points in 20 males were used. a total...

How do we know how the brain works? Doctors cannot open skull like a jar of candy and close lid afterward… or can they? In fact, putting electrical wires into human brains has been safely performed for many years. These devices, termed brain-machine interfaces, help doctors scientists record signals from brain, to tell them interacts with world. Researchers are using this technique improve qual...

the objective of this study is development of driver’s sleepiness using visually evoked potentials (vep). vep computed from eeg signals from the visual cortex. we use the steady state veps (ssveps) that are one of the most important eeg signals used in human computer interface systems. ssvep is a response to visual stimuli presented. we present a classification method to discriminate between cl...

Abstract Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints actions their processing across different structures brain. These recordings generate a large volume data that susceptible to noise artifacts. Therefore, review these ensure high quality by automatically detecting removing artifacts ...