We present a new approach to classifying the sleep stage that incorporates a
computationally inexpensive method based on permutations for channel selection and takes …

How to decode human vision through neural signals has attracted a long-standing interest in
neuroscience and machine learning. Modern contrastive learning and generative models …

Motor Imagery (MI) classification with electroencephalography (EEG) is a critical aspect of
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …

Motor imagery (MI) decoding plays a crucial role in the advancement of
electroencephalography (EEG)-based brain-computer interface (BCI) technology. Currently …

Deep learning has revolutionized EEG decoding, showcasing its ability to outperform
traditional machine learning models. However, unlike other fields, EEG decoding lacks …

Objective. BCI (Brain–Computer Interfaces) operate in three modes: online, offline, and
pseudo-online. In online mode, real-time EEG data is constantly analyzed. In offline mode …

The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its
nascent stages compared to their success in fields like Computer Vision, especially in Brain …

Abstract Objective: The integration of Deep Learning (DL) algorithms on brain signal
analysis is still in its nascent stages compared to their success in fields like Computer Vision …

Deep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art
performance on a variety of decoding tasks. Despite their high performance, existing …

Depression is a serious mental health condition affecting hundreds of millions of people
worldwide. Electroencephalogram (EEG) is a spontaneous and rhythmic physiological …