A spiking neural network with adaptive graph convolution and lstm for eeg-based brain-computer interfaces
Electroencephalography (EEG) signals classification is essential for the brain-computer
interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great …
interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great …
MI-DABAN: A dual-attention-based adversarial network for motor imagery classification
H Li, D Zhang, J Xie - Computers in Biology and Medicine, 2023 - Elsevier
The brain–computer interface (BCI) based on motor imagery electroencephalography (EEG)
is widely used because of its convenience and safety. However, due to the distributional …
is widely used because of its convenience and safety. However, due to the distributional …
MI-DAGSC: A domain adaptation approach incorporating comprehensive information from MI-EEG signals
D Zhang, H Li, J Xie, D Li - Neural Networks, 2023 - Elsevier
Non-stationarity of EEG signals leads to high variability between subjects, making it
challenging to directly use data from other subjects (source domain) for the classifier in the …
challenging to directly use data from other subjects (source domain) for the classifier in the …
Temporal–spatial transformer based motor imagery classification for BCI using independent component analysis
Motor Imagery (MI) classification with electroencephalography (EEG) is a critical aspect of
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …
Brain–Computer Interface (BCI) systems, enabling individuals with mobility limitations to …
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 …
A cross-session motor imagery classification method based on Riemannian geometry and deep domain adaptation
W Liu, C Guo, C Gao - Expert Systems with Applications, 2024 - Elsevier
Recently, more and more studies have begun to use deep learning to decode and classify
EEG signals. The use of deep learning has led to an increase in the classification accuracy …
EEG signals. The use of deep learning has led to an increase in the classification accuracy …
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 …
Filter bank sinc-convolutional network with channel self-attention for high performance motor imagery decoding
Objective. Motor Imagery Brain-Computer Interface (MI-BCI) is an active Brain-Computer
Interface (BCI) paradigm focusing on the identification of motor intention, which is one of the …
Interface (BCI) paradigm focusing on the identification of motor intention, which is one of the …
Interpretable Dual-branch EMGNet: A transfer learning-based network for inter-subject lower limb motion intention recognition
C Zhang, X Wang, Z Yu, B Wang, C Deng - Engineering Applications of …, 2024 - Elsevier
Currently, the fusion of surface Electromyography (EMG) and deep learning is gradually
showing immense potential in the research of Lower Limb Motion Intention Recognition …
showing immense potential in the research of Lower Limb Motion Intention Recognition …
A multiscale feature fusion network based on attention mechanism for motor imagery EEG decoding
D Gao, W Yang, P Li, S Liu, T Liu, M Wang… - Applied Soft …, 2024 - Elsevier
The decoding of motor imagery (MI) electroencephalogram (EEG) is an essential component
of the brain–computer interface (BCI), which can help patients with motor impairment …
of the brain–computer interface (BCI), which can help patients with motor impairment …