Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …

In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so
on) for analyzing EEG data for decoding the activity of human's brain and diagnosing …

Electroencephalogram (EEG) can track the brain waves which contain the neural activity of
the brain. EEG signals help to understand the physiological and functional details and …

Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic
oscillations of neural activity, which is one of the core technologies of brain-computer …

Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

There are many sources of interference encountered in the electroencephalogram (EEG)
recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is …

Objective. Electroencephalogram (EEG) recordings are often contaminated with artifacts.
Various methods have been developed to eliminate or weaken the influence of artifacts …

Epilepsy is the fourth most common neurological disorder, affecting about 1% of the
population at all ages. As many as 60% of people with epilepsy experience focal seizures …

Electroencephalogram (EEG) signals acquired from brain can provide an effective
representation of the human's physiological and pathological states. Up to now, much work …