Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

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

Objective. Supervised learning paradigms are often limited by the amount of labeled data
that is available. This phenomenon is particularly problematic in clinically-relevant data …

Automatic sleep staging has been often treated as a simple classification problem that aims
at determining the label of individual target polysomnography epochs one at a time. In this …

Sleep stage classification constitutes an important preliminary exam in the diagnosis of
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …

The electroencephalogram (EEG) has great attraction in emotion recognition studies due to
its resistance to deceptive actions of humans. This is one of the most significant advantages …

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders.
This paper proposes a joint classification-and-prediction framework based on convolutional …

Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a
human expert, according to official standards. It could appear then a suitable task for modern …

Examination of the brain's condition with the Electroencephalogram (EEG) can be helpful to
predict abnormality and cerebral activities. The purpose of this study was to develop an …

Sleep stage classification is one of the most critical steps in effective diagnosis and the
treatment of sleep-related disorders. Visual inspection undertaken by sleep experts is a time …