Artificial Intelligence (AI) uses systems and machines to simulate human intelligence and
solve common real-world problems. Machine learning and deep learning are Artificial …

Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

The electroencephalography (EEG) signal is a noninvasive and complex signal that has
numerous applications in biomedical fields, including sleep and the brain–computer …

Recent substantial advancements in computational techniques, particularly in artificial
intelligence (AI) and machine learning (ML), have raised the demand for smart self-powered …

In neuroscience, electroencephalography (EEG) data is often used to extract features
(biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment …

Electroencephalography (EEG) is used in the diagnosis and prognosis of mental disorders
because it provides brain biomarkers. However, only highly trained doctors can interpret …

Electroencephalogram (EEG)-based emotion recognition has gained high attention in Brain-
Computer Interfaces. However, due to the non-linearity and non-stationarity of EEG signals …

Inspired by the great success of deep neural networks in image classification, recent works
use Recurrence Plots (RP) to encode time series as images for classification. RP provide …

Over the years, several deep learning algorithms have been proposed for
electroencephalography (EEG) signal classification. The performance of any learning …