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 …
Brain-computer interface: Advancement and challenges
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art
Objective. Processing strategies are analyzed with respect to the classification of
electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor …
electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor …
[HTML][HTML] Decoding movement kinematics from EEG using an interpretable convolutional neural network
Continuous decoding of hand kinematics has been recently explored for the intuitive control
of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural …
of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural …
Status of deep learning for EEG-based brain–computer interface applications
In the previous decade, breakthroughs in the central nervous system bioinformatics and
computational innovation have prompted significant developments in brain–computer …
computational innovation have prompted significant developments in brain–computer …
Data augmentation for deep neural networks model in EEG classification task: a review
Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic
oscillations of neural activity, which is one of the core technologies of brain-computer …
oscillations of neural activity, which is one of the core technologies of brain-computer …
Dual attention relation network with fine-tuning for few-shot EEG motor imagery classification
Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using
deep learning have shown improved performance over conventional techniques. However …
deep learning have shown improved performance over conventional techniques. However …
A parallel multi-scale time-frequency block convolutional neural network based on channel attention module for motor imagery classification
H Li, H Chen, Z Jia, R Zhang, F Yin - Biomedical Signal Processing and …, 2023 - Elsevier
The motor imagery brain-computer interface (MI-BCI) based on electroencephalography
(EEG) enables direct communication between the human brain and external devices. In this …
(EEG) enables direct communication between the human brain and external devices. In this …
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 …
FBMSNet: A filter-bank multi-scale convolutional neural network for EEG-based motor imagery decoding
K Liu, M Yang, Z Yu, G Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Object: Motor imagery (MI) is a mental process widely utilized as the experimental paradigm
for brain-computer interfaces (BCIs) across a broad range of basic science and clinical …
for brain-computer interfaces (BCIs) across a broad range of basic science and clinical …