EEG signals are very difficult to interpret because they are dynamic, non-linear and non-stationary signals. Human expertise also indicates that multi-level analysis must be performed to integrate various sources of knowledge. In this paper, we review these difficulties and propose that artificial neural networks could be good candidates to handle such a difficult problem
Human electroencephalogram (EEG) contains useful diag-nostic information on a variety of neurologica...
In recent years, deep learning algorithms have been developed rapidly, and they are becoming a power...
Human electroencephalogram (EEG) contains useful diagnostic information on a variety of neurological...
In recent years, neural networks showed unprecedented growth that ultimately influenced dozens of di...
In the last decade, unprecedented progress in the development of neural networks influenced dozens o...
The basis of the work of electroencephalography (EEG) is the registration of electrical impulses fro...
Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induc...
We apply artificial neural network (ANN) for recognition and classification of electroencephalograph...
The study of Artificial Neural Networks (ANN) has proved to be fascinating over the years and the de...
This paper presents pattern recognition of electroencephalograph (EEG) signals using artificial neur...
Brain-computer interface is a promising research area that has the potential to aid impaired individ...
The aim of this work is to analyze the artificial neural network (ANN), which may help identifying p...
EEG stands for Electroencephalogram. EEG is used to record signals from brain; signals are recorded ...
This paper focuses on electroencephalograms (EEG) - the main tools in diagnosis and treatment of spe...
Nowadays, machine and deep learning techniques are widely used in different areas, ranging from econ...
Human electroencephalogram (EEG) contains useful diag-nostic information on a variety of neurologica...
In recent years, deep learning algorithms have been developed rapidly, and they are becoming a power...
Human electroencephalogram (EEG) contains useful diagnostic information on a variety of neurological...
In recent years, neural networks showed unprecedented growth that ultimately influenced dozens of di...
In the last decade, unprecedented progress in the development of neural networks influenced dozens o...
The basis of the work of electroencephalography (EEG) is the registration of electrical impulses fro...
Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induc...
We apply artificial neural network (ANN) for recognition and classification of electroencephalograph...
The study of Artificial Neural Networks (ANN) has proved to be fascinating over the years and the de...
This paper presents pattern recognition of electroencephalograph (EEG) signals using artificial neur...
Brain-computer interface is a promising research area that has the potential to aid impaired individ...
The aim of this work is to analyze the artificial neural network (ANN), which may help identifying p...
EEG stands for Electroencephalogram. EEG is used to record signals from brain; signals are recorded ...
This paper focuses on electroencephalograms (EEG) - the main tools in diagnosis and treatment of spe...
Nowadays, machine and deep learning techniques are widely used in different areas, ranging from econ...
Human electroencephalogram (EEG) contains useful diag-nostic information on a variety of neurologica...
In recent years, deep learning algorithms have been developed rapidly, and they are becoming a power...
Human electroencephalogram (EEG) contains useful diagnostic information on a variety of neurological...