Deep transfer learning for bearing fault diagnosis: A systematic review since 2016
The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …
training and test data follow the same distribution. This assumption, however, is not always …
EEG based emotion recognition: A tutorial and review
Emotion recognition technology through analyzing the EEG signal is currently an essential
concept in Artificial Intelligence and holds great potential in emotional health care, human …
concept in Artificial Intelligence and holds great potential in emotional health care, human …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review
The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …
revolutionize the world, with numerous applications ranging from healthcare to human …
Deep common spatial pattern based motor imagery classification with improved objective function
Common spatial pattern (CSP) technique has been very popular in terms of
electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain …
electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
[HTML][HTML] Explaining deep neural networks: A survey on the global interpretation methods
A substantial amount of research has been carried out in Explainable Artificial Intelligence
(XAI) models, especially in those which explain the deep architectures of neural networks. A …
(XAI) models, especially in those which explain the deep architectures of neural networks. A …
Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques
The electroencephalography (EEG) signal is a noninvasive and complex signal that has
numerous applications in biomedical fields, including sleep and the brain–computer …
numerous applications in biomedical fields, including sleep and the brain–computer …
Deep transfer learning for automatic speech recognition: Towards better generalization
Automatic speech recognition (ASR) has recently become an important challenge when
using deep learning (DL). It requires large-scale training datasets and high computational …
using deep learning (DL). It requires large-scale training datasets and high computational …
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface
Objective. Brain-computer interface (BCI) aims to establish communication paths between
the brain processes and external devices. Different methods have been used to extract …
the brain processes and external devices. Different methods have been used to extract …