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 …

Brain signals refer to the biometric information collected from the human brain. The research
on brain signals aims to discover the underlying neurological or physical status of the …

Objective. Brain–computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This neural signal is generally chosen from a …

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 …

Driver fatigue evaluation is of great importance for traffic safety and many intricate factors
would exacerbate the difficulty. In this paper, based on the spatial-temporal structure of …

Existing deep convolutional neural networks (CNNs) have found major success in image
deraining, but at the expense of an enormous number of parameters. This limits their …

Hyperspectral image (HSI) sharpening, which aims at fusing an observable low spatial
resolution (LR) HSI (LR-HSI) with a high spatial resolution (HR) multispectral image (HR …

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on
single-channel electroencephalography (EEG) to learn task-specific filters for classification …

For a brain-computer interface (BCI) system, a calibration procedure is required for each
individual user before he/she can use the BCI. This procedure requires approximately 20-30 …

Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow
control of several applications by decoding neurophysiological phenomena, which are …