Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit
sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the …

Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and
cognitive content of neural processing based on noninvasively measured brain activity …

Multi-channel EEG data are usually necessary for spatial pattern identification in motor
imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some …

The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for
feature extraction in motor imagery (MI)-based brain–computer interfaces (BCIs). However …

Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …

In this study, three popular signal processing techniques (Empirical Mode Decomposition,
Discrete Wavelet Transform, and Wavelet Packet Decomposition) were investigated for the …

This paper introduces a methodology based on deep convolutional neural networks (DCNN)
for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More …

The robustness and computational load are the key challenges in motor imagery (MI) based
on electroencephalography (EEG) signals to decode for the development of practical brain …

The common spatial patterns (CSP) algorithm is the most popular spatial filtering method
applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain …

One of the major limitations of brain-computer interfaces (BCI) is their long calibration time,
which limits their use in practice, both by patients and healthy users alike. Such long …