Recognition of EEG signal motor imagery intention based on deep multi-view feature learning
J Xu, H Zheng, J Wang, D Li, X Fang - Sensors, 2020 - mdpi.com
Recognition of motor imagery intention is one of the hot current research focuses of brain-
computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their …
computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their …
A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning
F Li, F He, F Wang, D Zhang, Y Xia, X Li - Applied Sciences, 2020 - mdpi.com
Left and right hand motor imagery electroencephalogram (MI-EEG) signals are widely used
in brain-computer interface (BCI) systems to identify a participant intent in controlling …
in brain-computer interface (BCI) systems to identify a participant intent in controlling …
EEG-based motor imagery classification with deep multi-task learning
In the past decade, Electroencephalogram (EEG) has been applied in many fields, such as
Motor Imagery (MI) and Emotion Recognition. Traditionally, for classification tasks based on …
Motor Imagery (MI) and Emotion Recognition. Traditionally, for classification tasks based on …
Classification of motor imagery EEG based on time-domain and frequency-domain dual-stream convolutional neural network
Background and objective An important task of the brain-computer interface (BCI) of motor
imagery is to extract effective time-domain features, frequency-domain features or time …
imagery is to extract effective time-domain features, frequency-domain features or time …
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 …
MMCNN: A multi-branch multi-scale convolutional neural network for motor imagery classification
Electroencephalography (EEG) based motor imagery (MI) is one of the promising Brain–
computer interface (BCI) paradigms enable humans to communicate with the outside world …
computer interface (BCI) paradigms enable humans to communicate with the outside world …
Short time Fourier transformation and deep neural networks for motor imagery brain computer interface recognition
Motor imagery (MI) is an important control paradigm in the field of brain‐computer interface
(BCI), which enables the recognition of personal intention. So far, numerous methods have …
(BCI), which enables the recognition of personal intention. So far, numerous methods have …
A novel classification method for EEG-based motor imagery with narrow band spatial filters and deep convolutional neural network
Abstract The Common Spatial Pattern (CSP) algorithm is the most widely used method for
decoding Electroencephalography (EEG) signals from motor imagery (MI) paradigm …
decoding Electroencephalography (EEG) signals from motor imagery (MI) paradigm …
Self-attention-based convolutional neural network and time-frequency common spatial pattern for enhanced motor imagery classification
R Zhang, G Liu, Y Wen, W Zhou - Journal of Neuroscience Methods, 2023 - Elsevier
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 …
Improving multi-class motor imagery EEG classification using overlapping sliding window and deep learning model
Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems.
MI tasks are performed by imagining doing a specific task and classifying MI through EEG …
MI tasks are performed by imagining doing a specific task and classifying MI through EEG …