Objective. Deep learning (DL) networks are increasingly attracting attention across various
fields, including electroencephalography (EEG) signal processing. These models provide …

Electroencephalography (EEG), as a non-invasive modality, enables the representation of
the underlying neuronal activities as electrical signals with high temporal resolution. In …

Epilepsy is the second most common neurological disease after Alzheimer. It is a disorder of
the brain which results in recurrent seizures. Though the epilepsy in general is considered …

Objective: Recent advances in development of low-cost single-channel
electroencephalography (EEG) headbands have opened new possibilities for applications …

Motion artifacts are one of the most challenging non-physiological noise sources present in
the biomedical signal, which can hinder the true performance of EEG-based neuro …

Error entropy is a well-known learning criterion in information theoretic learning (ITL), and it
has been successfully applied in robust signal processing and machine learning. To date …

As an important neurorecording technique, electroencephalography (EEG) is often
contaminated by various artifacts, which obstructs subsequent analysis. In recent years …

In this study, we consider the parameter estimation problem for an errors-in-variables (EIV)
model with impulse noise. New adaptive filtering, called the maximum total generalized …

Cognitive/mental task classification using single/limited channel (s) electroencephalogram
(EEG) signals in real-time play an important role in designing portable brain-computer …

The movement of eyeballs and eye blinks produces ocular artifacts in the
electroencephalogram (EEG) signal during recording. It is necessary to filter out these …