Brain–computer interface (BCI) technology has attracted significant attention over recent
decades, and has made remarkable progress. However, BCI still faces a critical hurdle, in …

Objective. To develop and implement a novel approach which combines the technique of
scout EEG source imaging (ESI) with convolutional neural network (CNN) for the …

The closed-loop control of rehabilitative technologies by neural commands has shown a
great potential to improve motor recovery in patients suffering from paralysis. Brain–machine …

Background Brain-machine interfaces (BMI) have recently been integrated within motor
rehabilitation therapies by actively involving the central nervous system (CNS) within the …

Brain–computer interfaces (BCIs) allow control of various applications or external devices
solely by brain activity, eg, measured by electroencephalography during motor imagery …

Previously, modulations in power of neuronal oscillations have been functionally linked to
sensory, motor and cognitive operations. Such links are commonly established by relating …

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 …

Motor imagery modulates specific neural oscillations like actual movement does.
Representatively, suppression of the alpha power (eg, event-related desynchronization …

Proper signal measurement and processing are crucial in electroencephalography (EEG)-
based brain-computer interfaces (BCIs), as they form the basis of brain insight and precise …

The common spatial patterns (CSP) algorithm is one of the most frequently used and
effective spatial filtering methods for extracting relevant features for use in motor imagery …