In recent years, deep learning (DL) has contributed significantly to the improvement of motor-
imagery brain–machine interfaces (MI-BMIs) based on electroencephalography (EEG) …

Deep neural networks have recently become increasingly used for a wide range of
applications,(eg, image and video processing). The demand for edge inference is growing …

High spatiotemporal resolution biosignal that is vital for biomedical applications results in an
information bottleneck that poses challenges for their transferring and processing. The …

This work is an introduction and a survey for the Special Issue on Machine Intelligence at the
Edge. The authors argue that workloads that were formerly performed in the cloud are …

A brain–machine interface (BMI) based on motor imagery (MI) enables the control of devices
using brain signals while the subject imagines performing a movement. It plays a key role in …

Thanks to the fast evolution of electroencephalography (EEG)-based brain-computer
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …

Brain-computer interfaces (BCIs) is promising in interacting with machines through
electroencephalogram (EEG) signal. The compact end-to-end neural network model for …

One of the most popular Brain-Computer Interface (BCI) paradigms is the classification of
motor imagery tasks using Electroencephalograph signals (EEG). Recent works suggest the …

Abstract Electroencephalography (EEG)-based Motor Imagery (MI) brain-computer interface
(BCI) systems play essential roles in motor function rehabilitation for patients with post …

We present a simple deep learning-based framework commonly used in computer vision
and demonstrate its effectiveness for cross-dataset transfer learning in mental imagery …