Results 241 to 250 of about 43,477 (290)

Action Observation Combined With Motor Imagery Training to Improve Motor Function in People With Stroke: Systematic Review and Meta-Analysis. [PDF]

JMIR Rehabil Assist Technol
Sun P, Liang X, Zhang X, Huang M, Zhang X, Ni C.   +5 more
europepmc   +1 more source

A Domain Generalization Method for EEG Based on Domain-Invariant Feature and Data Augmentation. [PDF]

Cyborg Bionic Syst
Jin J, Li J, Pan X, Xu R, Cichocki A, Du W, Qian F.   +6 more
europepmc   +1 more source

The Effect of Motor Imagery Ability on Function and Proprioception in Myoelectric Prosthesis Users: Protocol for a Cross-Sectional Study.

JMIR Res Protoc
Yazgan A, Algun ZC, Yazgan EA.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

MI-EEGNET: A novel convolutional neural network for motor imagery classification

Journal of Neuroscience Methods, 2021
Brain-computer interfaces (BCI) permits humans to interact with machines by decoding brainwaves to command for a variety of purposes. Convolutional neural networks (ConvNet) have improved the state-of-the-art of motor imagery decoding in an end-to-end approach. However, shallow ConvNets usually perform better than their deep counterparts.
Mouad, Riyad, Mohammed, Khalil, Abdellah, Adib   +2 more
openaire   +2 more sources

MI-DABAN: A dual-attention-based adversarial network for motor imagery classification

Computers in Biology and Medicine, 2023
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 disparity between EEG signals, data from other subjects cannot be used directly to train a subject-specific classifier.
Huiying Li, Dongxue Zhang, Jingmeng Xie
openaire   +2 more sources

MI-CAT: A transformer-based domain adaptation network for motor imagery classification

Neural Networks, 2023
Due to its convenience and safety, electroencephalography (EEG) data is one of the most widely used signals in motor imagery (MI) brain-computer interfaces (BCIs). In recent years, methods based on deep learning have been widely applied to the field of BCIs, and some studies have gradually tried to apply Transformer to EEG signal decoding due to its ...
Dongxue Zhang, Huiying Li, Jingmeng Xie
openaire   +2 more sources

A graph-theoretic sensor-selection scheme for covariance-based Motor Imagery (MI) decoding

2020 28th European Signal Processing Conference (EUSIPCO), 2021
Optimal sensor selection is an issue of paramount importance in brain decoding. When associated with estimates of covariance, its implications concern not only classification accuracy, but also computational efficiency. However, very few attempts have been made so far, since it constitutes a challenging mathematical problem.
Kostas Georgiadis, Dimitrios A. Adamos, Spiros Nikolopoulos, Nikos Laskaris, Ioannis Kompatsiaris   +4 more
openaire   +1 more source

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

Neural Computing and Applications, 2021
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 augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms that have been used extensively in smart healthcare applications such as post ...
Hamdi Altaheri, Ghulam Muhammad, Mansour Alsulaiman, Syed Umar Amin, Ghadir Ali Altuwaijri, Wadood Abdul, Mohamed A. Bencherif, Mohammed Faisal   +7 more
openaire   +1 more source

Equivalent is not equal: Primary motor cortex (MI) activation during motor imagery and execution of sequential movements

Brain Research, 2008
The motor hierarchy hypothesis and the related debate about the role of the primary motor cortex (MI) in motor preparation are major topics in cognitive neuroscience today. The present study combines the two strategies that have been followed to clarify the role of MI in motor preparation independently from execution: motor imagery and the use of ...
M T, Carrillo-de-la-Peña, S, Galdo-Alvarez, C, Lastra-Barreira   +2 more
openaire   +2 more sources