Deep learning-based electroencephalography analysis: a systematic review
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 …
of training, as well as advanced signal processing and feature extraction methodologies to …
Deep learning in bioinformatics
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 …
been one of the most important challenges in bioinformatics. Deep learning has advanced …
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg… - Human brain …, 2017 - Wiley Online Library
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 …
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces
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 …
using neural activity as the control signal. This neural signal is generally chosen from a …
Learning temporal information for brain-computer interface using convolutional neural networks
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 …
algorithms for computer vision and natural language processing problems. However, the …
Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
S Chambon, MN Galtier, PJ Arnal… - … on Neural Systems …, 2018 - ieeexplore.ieee.org
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 …
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …
Focus: Querying large video datasets with low latency and low cost
Large volumes of videos are continuously recorded from cameras deployed for traffic control
and surveillance with the goal of answering “after the fact” queries: identify video frames with …
and surveillance with the goal of answering “after the fact” queries: identify video frames with …
Spatio-spectral feature representation for motor imagery classification using convolutional neural networks
Convolutional neural networks (CNNs) have recently been applied to electroencephalogram
(EEG)-based brain–computer interfaces (BCIs). EEG is a noninvasive neuroimaging …
(EEG)-based brain–computer interfaces (BCIs). EEG is a noninvasive neuroimaging …
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Brain signals refer to the biometric information collected from the human brain. The research
on brain signals aims to discover the underlying neurological or physical status of the …
on brain signals aims to discover the underlying neurological or physical status of the …