BACKGROUND Monitoring the functional connectivity between brain regions is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded electroencephalogram (EEG) such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear...

In this paper, we present an approach for graph signal representation of EEG toward deep learning-based modeling. In order to overcome the low dimensionality and spatial resolution of EEG, our approach divides the EEG signal into multiple frequency bands, builds an intra-band graph for each of them, and merges them with inter-band connectivity to obtain rich graph representation. The signal fea...

In this paper we proposed a technique to remove eye blink artifact from electroencephalogram (EEG) using lifting wavelet transform (LWT). The LWT has been successfully used in eye blink artifact suppression form the recorded electroencephalography (EEG) signals using a data-adaptive subband filtering approach. The LWT is applied to decompose EEG signal into a finite set of subbands. The energy ...

In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the com...

background: noise pollution is one of the most harmful ambiance disturbances. it may cause many deficits in ability and activity of persons in the urban and industrial areas. it also may cause many kinds of psychopathies. therefore, it is very important to measure the risk of this pollution in different area. methods: this study was conducted in the department of medical physics and biomedical ...

Both categorization and segmentation processes play a crucial role in face perception. However, the functional relation between these subprocesses is currently unclear. The present study investigates the temporal relation between segmentation-related and category-selective responses in the brain, using electroencephalography (EEG). Surface segmentation and category content were both manipulated...

The different background rhythms and their restraint in electroencephalgraph(EEG) signal can reflect the different physiological states of the patient. The concept of frequency band relative intensity ratio(FBRIR) is introduced to evaluate the state of EEG background rhythms of a patient, and empirical mode decomposition(EMD) is used to realize the separation of the frequency bands in EEG signa...

Brain Computer Interface (BCI) is often directed at mapping, assisting, or repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a non-invasive method of acquisition brain electrical activities. Noises are impure the EEG recorded signal due to the physiologic and extra-physiologic artifacts. There are several techniques are intended to manipulate the EEG recorded s...

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor retardation, myotonia, quiescent tremor, and postural gait abnormality, as well nonmotor symptoms such anxiety depression. Biofeedback improves functions of patients regulating abnormal electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG), electromyography (EMG), respiration (RSP), or othe...

Simultaneously acquired EEG and BOLD (Blood Oxygenation level dependent contrast) MRI allowed to study on line the neurophysiological changes in rat brain during epileptic seizures. MRI and EEG data were acquired with a specially designed high quality MR RF-antenna with incorporated non-invasive carbon EEG electrodes. The problem of severe pollution of the EEG data due to MR gradient switching ...