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

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those
using motor-imagery (MI) data, have the potential to become groundbreaking technologies …

Abstract Objective. Deep learning (DL)-based brain–computer interface (BCI) in motor
imagery (MI) has emerged as a powerful method for establishing direct communication …

Abstract In the Motor Imagery (MI)-based Brain Computer Interface (BCI), users' intention is
converted into a control signal through processing a specific pattern in brain signals …

A brain–computer interface (BCI) enables a user to communicate with a computer directly
using brain signals. The most common noninvasive BCI modality, electroencephalogram …

The high temporal resolution and the asymmetric spatial activations are essential attributes
of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the …

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …

Background Electroencephalography (EEG)-based brain-computer interface (BCI) systems
are mainly divided into three major paradigms: motor imagery (MI), event-related potential …

Deep learning (DL) methods and architectures have been the state-of-the-art classification
algorithms for computer vision and natural language processing problems. However, the …

Objective Electroencephalogram (EEG) based motor imagery (MI) classification is an
important aspect in brain-machine interfaces (BMIs) which bridges between neural system …