Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development motor imagery (MI) brain–computer interface (BCI) systems. In this study, two sliding window techniques are proposed to enhance MI. The first one calculates longest consecutive repetition (LCR) sequence prediction all windows and named SW-LCR. second mode SW-Mode. Common spatial patt...

The need for repeated calibration and accounting intersubject variability is a major challenge the practical applications of brain–computer interface (BCI). problem becomes more challenging while decoding brain signals stroke patients due to altered neurodynamics caused by lesions. Recently, several deep learning architectures came into picture although they often failed produce superior accura...

Brain-computer interfaces (BCIs) based on the electroencephalogram (EEG) generated during motor imagery (MI) have potential to be used in brain-controlled prosthetics, neurorehabilitation and gaming. Many MI EEG classification systems segment into windows for classification. However, a comprehensive analysis of decision fusion segmented data, within context different classifiers, has not been c...