It is incredibly challenging to build an intelligent algorithm for emotion recognition that can deliver high accuracy because electroencephalography (EEG) signals are not stationary, nonlinear, and noisy. First, decomposing the preprocessed EEG of SEED dataset into five frequency bands: delta, theta, alpha, beta, gamma, then calculated their energy entropy from extracted features. Then Principa...

In order to fully extract the temporal and spatial features contained in motor imagery electroencephalography (EEG) signals for effective identification of imagery, a three-dimensional capsule network (3D-CapsNet) EEG signal recognition model is proposed, which can integrate MI-EEG dimension, channel dimension intrinsic relationship between maximize feature representation capability. Firstly, m...

An electroencephalogram (EEG) is a graphical record of ongoing electrical activity produced by firing of neurons of the human brain due to internal and/or external stimuli. Feature extraction and classification of the EEG signals are used for diagnosis the epileptic seizure (i.e., physical changes in behaviour that occur due to abnormal electrical activity in the brain). Classification of Ictal...

The review focuses on the most promising methods for classifying EEG signals non-invasive BCIs and theoretical approaches successful classification of patterns. paper provides an overview articles using Riemannian geometry, deep learning various options preprocessing "clustering" signals, example, common-spatial pattern (CSP). Among other approaches, pre-processing CSP is often used, both offli...