Background A widely used paradigm for brain-computer interfaces (BCI) is based on the
detection of event-related (des) synchronization (ERD/S) in response to hand motor imagery …

Abstract Recently, Brain computer Interface (BCI), plays an important role in recognizing
brain activities and rehabilitation. Motor imagery (MI) classification based on …

More natural and intuitive control is expected to maximize the auxiliary effect of the powered
prosthetic leg for lower limb amputees. In order to realize the stable and flexible walking of …

Stroke is a neurological syndrome that usually causes a loss of voluntary control of
lower/upper body movements, making it difficult for affected individuals to perform Activities …

To improve the spatial resolution of the electroen-cephalogram (EEG) signal, it is
conventional to use a large number of scalp electrode while recording oscillatory rhythms in …

Background Unilateral upper limb multitasking brings essential improvements to stroke
rehabilitation and prosthetic control. However, the influence and recognition of multiple tasks …

Transfer learning is increasingly used to decode multi-class motor imagery tasks. Previous
transfer learning ignored the optimizability of the source model, weakened the adaptability to …

Electroencephalography (EEG) signals are known to manifest differential patterns when
individuals visually concentrate on different objects. In this work, we present an end-to-end …

Motor imagery-based brain-computer interfaces (BCIs) have garnered significant attention in
neuro engineering for their potential to enable communication and control in individuals with …

El presente trabajo busca determinar la eficiencia de algoritmos de clasificación para
analizar señales de electroencefalogramas de movimiento imaginario. Para este objetivo se …