This review focuses on electroencephalogram (EEG) acquisition and feedback technology
and its core elements, including the composition and principles of the acquisition devices, a …

Background and objectives Convolutional neural networks (CNNs) are the most widely used
deep-learning framework for decoding electroencephalograms (EEGs) due to their …

Objective: Electroencephalography signals are frequently used for various Brain–Computer
interface (BCI) tasks. While deep learning (DL) techniques have shown promising results …

Depression is a complex mental illness that has significant effects on people as well as
society. The traditional techniques for the diagnosis of depression, along with the potential of …

Within the framework of the advanced human-cybernetic interfaces (HCI), Cross-subject
electroencephalogram (EEG)-based driver fatigue recognition is emerging as a pivotal …

In recent years, the development of Internet of Things (IoT) applications has increased,
resulting in higher demands for sufficient bandwidth, data rates, latency, and quality of …

Cognitive state identification based on Electroencephalography (EEG) not only helps to
diagnose various types of cognitive dysfunctions as early as possible in the clinical field, but …

Abstract Background and Objective: Brain–Computer Interface (BCI) technology has recently
been advancing rapidly, bringing significant hope for improving human health and quality of …

This study investigates the relationship between various target exposure signs and brain
activation patterns by analyzing the EEG signals of 35 subjects observing four types of …

Objective. Identifying the seizure occurrence period (SOP) in extended EEG recordings is
crucial for neurologists to diagnose seizures effectively. However, many existing computer …