Fusion convolutional neural network for multi-class motor imagery of EEG signals classification
Classification of EEG signals based on motor imagery is an important task in Brain-
Computer Interface (BCI). Deep learning approaches have been successfully used in …
Computer Interface (BCI). Deep learning approaches have been successfully used in …
Comparison of motor imagery EEG classification using feedforward and convolutional neural network
Brain-computer interface (BCI) is widely used in several clinical applications. Motor imagery-
based BCI can help patients who have lost their motor functions in communication and …
based BCI can help patients who have lost their motor functions in communication and …
Classification of motor imagery EEG signals based on deep autoencoder and convolutional neural network approach
JF Hwaidi, TM Chen - IEEE access, 2022 - ieeexplore.ieee.org
The technology of the brain-computer interface (BCI) employs electroencephalogram (EEG)
signals to establish direct interaction between the human body and its surroundings with …
signals to establish direct interaction between the human body and its surroundings with …
A novel approach to classify motor-imagery EEG with convolutional neural network using network measures
L Mousapour, F Agah, S Salari… - 2018 4th Iranian …, 2018 - ieeexplore.ieee.org
Electroencephalogram (EEG) signal recorded throughout motor imaging (MI) tasks has been
wide applied in brain-computer interface (BCI) applications as a communication approach …
wide applied in brain-computer interface (BCI) applications as a communication approach …
Motor imagery EEG classification with self-attention-based convolutional neural network
R Zhang, N Zhang, C Chen, D Lv, G Liu… - 2022 7th …, 2022 - ieeexplore.ieee.org
Motor Imagery-based Brain-Computer Interfaces have been widely utilized in neuro-
rehabilitation. Motor Imagery electroencephalogram (MI-EEG) refers to the EEG signals that …
rehabilitation. Motor Imagery electroencephalogram (MI-EEG) refers to the EEG signals that …
EEG-based motor imagery classification using convolutional neural networks with local reparameterization trick
W Huang, W Chang, G Yan, Z Yang, H Luo… - Expert Systems with …, 2022 - Elsevier
Objectives Deep learning (DL) method has emerged as a powerful tool in studying the
behavior of Electroencephalogram (EEG)-based motor imagery (MI). Although prospective …
behavior of Electroencephalogram (EEG)-based motor imagery (MI). Although prospective …
A novel motor imagery EEG classification approach based on time-frequency analysis and convolutional neural network
Q Wang, L Wang, S Xu - Recent Advances in AI-enabled …, 2022 - taylorfrancis.com
Motor imagery (MI) classification using electroencephalography (EEG) is crucial to a brain-
computer interface (BCI)-based neuro-rehabilitation system. However, due to the …
computer interface (BCI)-based neuro-rehabilitation system. However, due to the …
Comparison of machine learning methods for two class motor imagery tasks using EEG in brain-computer interface
The Brain-Computer Interface (BCI) systems can improve the life quality of physically
impaired people. It allows them to perform tasks like gripping objects, turning on light …
impaired people. It allows them to perform tasks like gripping objects, turning on light …
Self-attention-based convolutional neural network and time-frequency common spatial pattern for enhanced motor imagery classification
Background Motor imagery (MI) based brain-computer interfaces (BCIs) have promising
potentials in the field of neuro-rehabilitation. However, due to individual variations in active …
potentials in the field of neuro-rehabilitation. However, due to individual variations in active …
Classification of four class motor imagery for brain computer interface
In this paper, four class motor imagery classification has been studied for brain computer
interface. Feature investigations were conducted on the Enobio device, firstly with all 8 …
interface. Feature investigations were conducted on the Enobio device, firstly with all 8 …