Deep learning for motor imagery EEG-based classification: A review
Objectives The availability of large and varied Electroencephalogram (EEG) datasets,
rapidly advances and inventions in deep learning techniques, and highly powerful and …
rapidly advances and inventions in deep learning techniques, and highly powerful and …
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 for healthcare applications based on physiological signals: A review
Background and objective: We have cast the net into the ocean of knowledge to retrieve the
latest scientific research on deep learning methods for physiological signals. We found 53 …
latest scientific research on deep learning methods for physiological signals. We found 53 …
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 …
[PDF][PDF] A survey on deep learning based brain computer interface: Recent advances and new frontiers
Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world
by decoding individuals' brain signals into commands recognizable by computer devices …
by decoding individuals' brain signals into commands recognizable by computer devices …
Deep learning in the biomedical applications: Recent and future status
Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …
Major depressive disorder classification based on different convolutional neural network models: deep learning approach
The human brain is characterized by complex structural, functional connections that
integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation …
integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation …
On the deep learning models for EEG-based brain-computer interface using motor imagery
Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm
which requires powerful classifiers. Recent development of deep learning technology has …
which requires powerful classifiers. Recent development of deep learning technology has …
Convolutional neural network-based EEG signal analysis: A systematic review
S Rajwal, S Aggarwal - Archives of Computational Methods in …, 2023 - Springer
The identification and classification of human brain activities are essential for many medical
and Brain-Computer Interface (BCI) systems, saving human lives and time …
and Brain-Computer Interface (BCI) systems, saving human lives and time …
Spatial‐Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network
M Miao, W Hu, H Yin, K Zhang - … and mathematical methods in …, 2020 - Wiley Online Library
EEG pattern recognition is an important part of motor imagery‐(MI‐) based brain computer
interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two …
interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two …