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 …

In the era of big data, transformation of biomedical big data into valuable knowledge has
been one of the most important challenges in bioinformatics. Deep learning has advanced …

In this paper, we present a multimodal emotion recognition framework called EmotionMeter
that combines brain waves and eye movements. To increase the feasibility and wearability …

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …

Objective. Brain–computer interfaces (BCI) enable direct communication with a computer,
using neural activity as the control signal. This neural signal is generally chosen from a …

Deep learning (DL) methods and architectures have been the state-of-the-art classification
algorithms for computer vision and natural language processing problems. However, the …

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 …

Depression is a psychological disorder characterized by the continuous occurrence of bad
mood state. It is critical to understand that this disorder is severely affecting people of …

Objective: This paper investigates the hypothesis that focal seizures can be predicted using
scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish …

In order to improve the accuracy of emotional recognition by end-to-end automatic learning
of emotional features in spatial and temporal dimensions of electroencephalogram (EEG) …