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 …
Physics-informed attention temporal convolutional network for EEG-based motor imagery classification
H Altaheri, G Muhammad… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
The brain-computer interface (BCI) is a cutting-edge technology that has the potential to
change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used …
change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used …
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 …
Toward the development of versatile brain–computer interfaces
Recent advances in artificial intelligence demand an automated framework for the
development of versatile brain–computer interface (BCI) systems. In this article, we …
development of versatile brain–computer interface (BCI) systems. In this article, we …
DeepFeature: feature selection in nonimage data using convolutional neural network
Artificial intelligence methods offer exciting new capabilities for the discovery of biological
mechanisms from raw data because they are able to detect vastly more complex patterns of …
mechanisms from raw data because they are able to detect vastly more complex patterns of …
Dynamic convolution with multilevel attention for EEG-based motor imagery decoding
H Altaheri, G Muhammad… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Brain–computer interface (BCI) is an innovative technology that utilizes artificial intelligence
(AI) and wearable electroencephalography (EEG) sensors to decode brain signals and …
(AI) and wearable electroencephalography (EEG) sensors to decode brain signals and …
An improved model using convolutional sliding window-attention network for motor imagery EEG classification
Y Huang, J Zheng, B Xu, X Li, Y Liu, Z Wang… - Frontiers in …, 2023 - frontiersin.org
Introduction The classification model of motor imagery-based electroencephalogram (MI-
EEG) is a new human-computer interface pattern and a new neural rehabilitation …
EEG) is a new human-computer interface pattern and a new neural rehabilitation …
A cross-space CNN with customized characteristics for motor imagery EEG classification
Y Hu, Y Liu, S Zhang, T Zhang, B Dai… - … on Neural Systems …, 2023 - ieeexplore.ieee.org
The classification of motor imagery-electroencephalogram (MI-EEG) based brain-computer
interface (BCI) can be used to decode neurological activities, which has been widely applied …
interface (BCI) can be used to decode neurological activities, which has been widely applied …
Deep temporal networks for EEG-based motor imagery recognition
The electroencephalogram (EEG) based motor imagery (MI) signal classification, also
known as motion recognition, is a highly popular area of research due to its applications in …
known as motion recognition, is a highly popular area of research due to its applications in …
CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data
Background The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal
task in the biosignal classification process in the brain-computer i nterface (BCI) …
task in the biosignal classification process in the brain-computer i nterface (BCI) …