Decoding motor commands from noninvasively measured neural signals has become
important in brain-computer interface (BCI) research. Applications of BCI include …

Low frequency signals recorded from non-invasive electroencephalography (EEG), in
particular movement-related cortical potentials (MRPs), are associated with preparation and …

Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive
electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG …

Goal-directed motor behavior is associated with changes in patterns of rhythmic neuronal
activity across widely distributed brain areas. In particular, movement initiation and …

A motion trajectory prediction (MTP)-based brain–computer interface (BCI) aims to
reconstruct the three-dimensional (3D) trajectory of upper limb movement using …

Over the last few decades, the advances in disciplines such as neuroscience and
engineering have introduced the brain-computer interface (BCI) as a promising tool for …

In this study, across-participant and across-session transfer learning was investigated to
minimize the calibration time of the brain–computer interface (BCI) system in the context of …

Reconstruction of the three-dimensional (3D) trajectory of an imagined limb movement using
electro-encephalography (EEG) poses many challenges. However, if achieved, more …

Objective. The complicated processes of carrying out a hand reach are still far from fully
understood. In order to further the understanding of the kinematics of hand movement, the …

A brain-computer interface (BCI) can be used to control a limb neuroprosthesis in patients. In
particular, decoding trajectory of upper limb with motor imagery (MI) can support motor …