Deep representation learning has been widely explored for decoding motor imagery
electroencephalogram (MI-EEG) to build EEG-tailored brain-computer interfaces. Due to the …

Objective. Brain–computer interfaces (BCIs) enable a direct communication of the brain with
the external world, using one's neural activity, measured by electroencephalography (EEG) …

Objective. In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing
calibration time has become increasingly critical for real-world applications. Recently …

In this work, we study the problem of cross-subject motor imagery (MI) decoding from
electroencephalography (EEG) data. Multi-subject EEG datasets present several kinds of …

Brain–computer-interface (BCI) plays an important role in the Internet of Things (IoT). With
the development of electroencephalogram (EEG) signal processing and deep learning …

The electroencephalogram-based (EEG) brain-computer interface (BCI) has garnered
significant attention in recent research. However, the practicality of EEG remains constrained …

Motor imagery EEG classification plays a crucial role in non-invasive Brain-Computer
Interface (BCI) research. However, the performance of classification is affected by the non …

Objective. Machine learning (ML) models have opened up enormous opportunities in the
field of brain–computer Interfaces (BCIs). Despite their great success, they usually face …

Objective: As brain-computer interface (BCI) research advances, many new applications are
being developed. Tasks can be performed in different virtual environments, and whether a …

Brain-computer interface (BCI) technology enables direct interaction between humans and
computers by analyzing brain signals. Electroencephalogram (EEG) is one of the non …