An electroencephalogram (EEG) signal is extremely nonstationary, highly composite and very complex, all of which reflects the underlying integral neurodynamics. Understanding the EEG "grammar", its internal structural organization would place a "Rozetta stone" in researchers' hands, allowing them to more adequately describe the information processes of the brain in terms of EEG phenomenology. T...

Scalp recordings of the electroencephalogram (EEG) have been used in association with repetitive transcranial magnetic stimulation (rTMS) investigations as a safety measure in monitoring ongoing EEG activity and as a neurophysiologic tool in examining the specific effects induced by the magnetic stimulus on the EEG or evoked potentials (EPs). Medline review on the use of EEG or EPs with rTMS re...

With the development of information technology, music education in universities is also changing. Traditional can not effectively explore feature students, resulting quality being restricted. The rapid Electroencephalogram (EEG) signals has brought a new educational model to education. Through extraction students’ psychological features by EEG, be identified and different programs formulated ac...

Background: The last decade was marked by increased neuroscience research involving machine Learning (ML) and medical images such as functional magnetic resonance electroencephalogram (EEG). There are many challenges in this field, including the need for more data a standard presenting results. Since ML models tend to be sensitive input data, different strategies acquisition, preprocessing, fea...

The present study attempt to investigate the changes of the electroencephalogram (EEG) rhythms due to physical fatigue. Three different methods to enhance EEG signals have been applied. Aside from the Referential method, the Common Average Reference method as well as the Current Source Density method has been applied to the raw data prior to processing. Ten subjects participated in this study. ...

Abstract Epilepsy is a nervous system disease, which caused by abnormal discharge of brain neurons. The clinical manifestations are generalized seizures, clonus, loss consciousness, and shock. An electroencephalogram (EEG) can accurately capture the changes in EEG activities. Therefore, signals used to detect seizures. In this paper, an epilepsy detection model based on time-gated feature netwo...

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...

Deep learning has achieved excellent performance in a wide range of domains, especially speech recognition and computer vision. Relatively less work been done for electroencephalogram (EEG), but there is still significant progress attained the last decade. Due to lack comprehensive topic widely covered survey deep EEG, we attempt summarize recent provide an overview, as well perspectives future...

We present a new method for separation of the rhythms of the electroencephalogram (EEG) signal. The proposed method is based on the Hilbert-Huang transform (HHT). The HHT consists two steps namely empirical mode decomposition (EMD) and the Hilbert transform (HT). The EMD decomposes EEG signal into set of narrow-band intrinsic mode functions (IMFs), and the Hilbert transformation of these IMFs p...