Deep learning for electroencephalogram (EEG) classification tasks: a review
Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …
Deep learning in physiological signal data: A survey
Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
tasks, has recently proved its high potential in 2D medical imaging analysis; however …
A transformer-based approach combining deep learning network and spatial-temporal information for raw EEG classification
The attention mechanism of the Transformer has the advantage of extracting feature
correlation in the long-sequence data and visualizing the model. As time-series data, the …
correlation in the long-sequence data and visualizing the model. As time-series data, the …
Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea
The identification of sleep stages is essential in the diagnostics of sleep disorders, among
which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring …
which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring …
Deep learning for predicting respiratory rate from biosignals
In the past decade, deep learning models have been applied to bio-sensors used in a body
sensor network for prediction. Given recent innovations in this field, the prediction accuracy …
sensor network for prediction. Given recent innovations in this field, the prediction accuracy …
A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG
Driver drowsiness is one of the main factors leading to road fatalities and hazards in the
transportation industry. Electroencephalography (EEG) has been considered as one of the …
transportation industry. Electroencephalography (EEG) has been considered as one of the …
Automatic sleep stage classification: From classical machine learning methods to deep learning
RN Sekkal, F Bereksi-Reguig… - … Signal Processing and …, 2022 - Elsevier
Background and objectives The classification of sleep stages is a preliminary exam that
contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time …
contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time …
Deep learning in EEG: Advance of the last ten-year critical period
Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …
in speech recognition and computer vision. Relatively less work has been done for …
[HTML][HTML] An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea
Automatic deep-learning models used for sleep scoring in children with obstructive sleep
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings …
Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network
Objective. Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community
to find evidence of cortical involvement at walking initiation and during locomotion. However …
to find evidence of cortical involvement at walking initiation and during locomotion. However …