Parallel spatial–temporal self-attention CNN-based motor imagery classification for BCI
X Liu, Y Shen, J Liu, J Yang, P Xiong… - Frontiers in neuroscience, 2020 - frontiersin.org
Motor imagery (MI) electroencephalography (EEG) classification is an important part of the
brain-computer interface (BCI), allowing people with mobility problems to communicate with …
brain-computer interface (BCI), allowing people with mobility problems to communicate with …
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 …
A multi-branch convolutional neural network with squeeze-and-excitation attention blocks for EEG-based motor imagery signals classification
Electroencephalography-based motor imagery (EEG-MI) classification is a critical
component of the brain-computer interface (BCI), which enables people with physical …
component of the brain-computer interface (BCI), which enables people with physical …
Joint spatial and temporal features extraction for multi-classification of motor imagery EEG
X Jia, Y Song, L Yang, L Xie - Biomedical Signal Processing and Control, 2022 - Elsevier
The application of brain-computer interface (BCI) has always been limited by low decoding
accuracy due to excessive noise in electroencephalogram (EEG) signals. The traditional …
accuracy due to excessive noise in electroencephalogram (EEG) signals. The traditional …
A temporal dependency learning CNN with attention mechanism for MI-EEG decoding
Deep learning methods have been widely explored in motor imagery (MI)-based brain
computer interface (BCI) systems to decode electroencephalography (EEG) signals …
computer interface (BCI) systems to decode electroencephalography (EEG) signals …
[HTML][HTML] TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI
Brain–computer interface (BCI) is a promising intelligent healthcare technology to improve
human living quality across the lifespan, which enables assistance of movement and …
human living quality across the lifespan, which enables assistance of movement and …
Spatial component-wise convolutional network (SCCNet) for motor-imagery EEG classification
We study brain-computer interfaces (BCI) based on the decoding of motor imagery (MI) from
electroencephalography (EEG) neuromonitoring. The robustness of MI-BCI is a major …
electroencephalography (EEG) neuromonitoring. The robustness of MI-BCI is a major …
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 …
MIN2Net: End-to-end multi-task learning for subject-independent motor imagery EEG classification
P Autthasan, R Chaisaen… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow
control of several applications by decoding neurophysiological phenomena, which are …
control of several applications by decoding neurophysiological phenomena, which are …
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 …