The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

Brain–computer interfaces are used for direct two-way communication between the human
brain and the computer. Brain signals contain valuable information about the mental state …

Blind source separation (BSS) methods have received a great deal of attention in
electroencephalogram (EEG) artifact elimination as they are routine and standard signal …

Electroencephalogram (EEG) signals are well-known nonstationary brain signals of lower
strength. Due to their small amplitude, they attract other biomedical artifacts from the …

The main intention of this proposal is to design and develop a new heart disease prediction
model via WBAN using three stages. The first stage is data aggregation, in which data is …

Abstract Objective Convolutional Neural Networks (CNNs) are promising for artifact
detection in electroencephalography (EEG) data, but require large amounts of data. Despite …

Discrete & continuous optimization constitutes a challenging task and generally rises as an
NP-hard problem. In the literature, as a derivative of this type of optimization issue …

Electroencephalogram (EEG) signals recorded from the scalp are often affected by artifacts.
Most existing artifact detection methods rely on multi-channel statistics such as inter-channel …

Electroencephalogram (EEG) signals contains a major role in examining the behavior of
brain activity. Moreover, these signals are contaminated with artifacts, which may affect the …

The logic dendritic neuron model (LDNM), which is inspired by natural neurons, has
emerged as a novel machine learning model in recent years. However, recent studies have …