After implementing 5G technology, academia and industry started researching 6th
generation wireless network technology (6G). 6G is expected to be implemented around the …

Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices
through the utilization of brain waves. It is worth noting that the application of BCI is not …

Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …

Brain computer interfaces (BCI) provide a direct communication link between the brain and a
computer or other external devices. They offer an extended degree of freedom either by …

Objective: This paper tackles the problem of transfer learning in the context of
electroencephalogram (EEG)-based brain-computer interface (BCI) classification. In …

Electroencephalography (EEG) provides access to neuronal dynamics non-invasively with
millisecond resolution, rendering it a viable method in neuroscience and healthcare …

ABSTRACT A decade ago, a group of researchers from academia and industry identified a
dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis …

In recent years, deep-learning models gained attention for electroencephalography (EEG)
classification tasks due to their excellent performance and ability to extract complex features …