Abstract Purpose We built Bayesian Network (BN) models to explain roles of different patient-
specific factors affecting racial differences in breast cancer stage at diagnosis, and to identify …

Feature extraction and selection are important parts of motor imagery electroencephalogram
(EEG) decoding and have always been the focus and difficulty of brain‐computer interface …

Motor imagery brain-computer interface (MI-BCI) is a promising tool for neuro-rehabilitation.
The real-time MI-BCI enables people with motor dysfunction disease to interact with the …

Brain-computer interface (BCI) aims to translate human intention into a control output signal.
In motor-imaginary (MI) BCI, the imagination of movement modifies the cortex brain activity …

Background Motor imagery (MI) related features are typically extracted from a fixed
frequency band and time window of EEG signal. Meanwhile, the time when the brain activity …

Background Brain-computer interface (BCI) systems based on motor imagery (MI) have
been widely used in neurorehabilitation. Feature extraction applied by the common spatial …

Multiple sclerosis (MS), a disease of the central nervous system, affects the white matter of
the brain. Neurologists interpret magnetic resonance images that are often complicated, time …

With the rapid advancement in analyzing high-volume and complex data, machine learning
has become one of the most critical and essential tools for classification and prediction. This …

Motor imagery (MI) classification using electroencephalography (EEG) signal analysis is
gaining significant interest for movement intent recognition, where feature extraction is …

Use of Motor Imagery in EEG signals is gaining importance to develop Brain Computer
Interface (BCI) applications in various fields ranging from bio-medical to entertainment. Filter …