Objective. Processing strategies are analyzed with respect to the classification of
electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor …

Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel
approach for controlling external devices. BCI technologies can be important enabling …

Motor imagery brain-computer interface (MI-BCI) provides a novel way for human-computer
interaction. Traditional neural networks often use serial structure to extract spatial features …

Motor imagery (MI) electroencephalography (EEG) decoding plays an important role in brain-
computer interface (BCI), which enables motor-disabled patients to communicate with the …

Objective. Learning the structures and unknown correlations of a motor imagery
electroencephalogram (MI-EEG) signal is important for its classification. It is also a major …

A major challenge in motor imagery (MI) of electroencephalogram (EEG) based brain–
computer interfaces (BCIs) is the individual differences for different people. That the …

Epilepsy is one of the most common neurological disorders. Accurate detection of epileptic
seizures is essential for treatment. A seizure detection method with the structure of functional …

Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an important
component of BCI system that helps motor-disabled people interact with the outside world …

Optimal feature extraction for multi-category motor imagery brain-computer interfaces (MI-
BCIs) is a research hotspot. The common spatial pattern (CSP) algorithm is one of the most …

In this paper, a general overview regarding neural recording, classical signal processing
techniques and machine learning classification algorithms applied to monitor brain activity is …