To increase the classification accuracy of the mental tasks with speech imagery, a parallel
convolutional neural network based on multi-band brain networks (MBBN-PCNN) is …

Electroencephalogram (EEG) is a commonly used non-invasive method that acquires
voltage of the brain during neural activities by using conventional disc electrodes. However …

The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …

Currently the dynamics and working of the brain are not fully understood. The detection of
brain-wave patterns has been possible for decades, but the missing element was the ability …

In this paper, a new model of focal and non-focal electroencephalography classification is
carried out using a deep neural network (DNN). The Convolution Architecture For Feature …

The success of deep learning in computer vision has inspired the scientific community to
explore new analysis methods. Within the field of neuroscience, specifically in …

Electroencephalography (EEG) serves as an effective diagnostic tool for mental disorders
and neurological abnormalities. Enhanced analysis and classification of EEG signals can …

In this paper, an algorithm based on a combination of Riemannian space and convolutional
neural network is proposed for the feature extraction as well as classification of motor …

Extracting relevant feature and classification are significant in brain-computer interface (BCI)
systems. Deep learning have achieved remarkable growth in many fields like speech …

This study explores a possible improvement to automated eye state prediction using an
electroencephalogram (EEG). A Convolutional Neural Network (CNN) is used for EEG …