Deep transfer learning for bearing fault diagnosis: A systematic review since 2016

X Chen, R Yang, Y Xue, M Huang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
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 …

EEG based emotion recognition: A tutorial and review

X Li, Y Zhang, P Tiwari, D Song, B Hu, M Yang… - ACM Computing …, 2022 - dl.acm.org
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 …

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

L Alzubaidi, J Bai, A Al-Sabaawi, J Santamaría… - Journal of Big Data, 2023 - Springer
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 …

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review

H Altaheri, G Muhammad, M Alsulaiman… - Neural Computing and …, 2023 - Springer
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 …

Deep common spatial pattern based motor imagery classification with improved objective function

N Yu, R Yang, M Huang - International Journal of Network Dynamics and …, 2022 - sciltp.com
Common spatial pattern (CSP) technique has been very popular in terms of
electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain …

Self-supervised learning for electroencephalography

MH Rafiei, LV Gauthier, H Adeli… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …

[HTML][HTML] Explaining deep neural networks: A survey on the global interpretation methods

R Saleem, B Yuan, F Kurugollu, A Anjum, L Liu - Neurocomputing, 2022 - Elsevier
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 …

Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques

A Chaddad, Y Wu, R Kateb, A Bouridane - Sensors, 2023 - mdpi.com
The electroencephalography (EEG) signal is a noninvasive and complex signal that has
numerous applications in biomedical fields, including sleep and the brain–computer …

Deep transfer learning for automatic speech recognition: Towards better generalization

H Kheddar, Y Himeur, S Al-Maadeed, A Amira… - Knowledge-Based …, 2023 - Elsevier
Automatic speech recognition (ASR) has recently become an important challenge when
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

F Mattioli, C Porcaro… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. Brain-computer interface (BCI) aims to establish communication paths between
the brain processes and external devices. Different methods have been used to extract …