Cross-dataset transfer learning for motor imagery signal classification via multi-task learning and pre-training
Y Xie, K Wang, J Meng, J Yue, L Meng… - Journal of Neural …, 2023 - iopscience.iop.org
Objective. Deep learning (DL) models have been proven to be effective in decoding motor
imagery (MI) signals in Electroencephalogram (EEG) data. However, DL models' success …
imagery (MI) signals in Electroencephalogram (EEG) data. However, DL models' success …
Deep learning in motor imagery EEG signal decoding: A Systematic Review
Thanks to the fast evolution of electroencephalography (EEG)-based brain-computer
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …
Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model
Abstract Coronavirus disease 2019 (COVID-19) pneumonia has erupted worldwide, causing
massive population deaths and huge economic losses. In clinic, lung ultrasound (LUS) plays …
massive population deaths and huge economic losses. In clinic, lung ultrasound (LUS) plays …
Decoding motor imagery tasks using ESI and hybrid feature CNN
T Fang, Z Song, G Zhan, X Zhang, W Mu… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. Brain–computer interface (BCI) based on motor imaging electroencephalogram
(MI-EEG) can be useful in a natural interaction system. In this paper, a new framework is …
(MI-EEG) can be useful in a natural interaction system. In this paper, a new framework is …
Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition
D Li, K Li, Y Xia, J Dong, R Lu - Scientific Reports, 2024 - nature.com
In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel
electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve …
electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve …
SincMSNet: a Sinc filter convolutional neural network for EEG motor imagery classification
Objective. Motor imagery (MI) is widely used in brain-computer interfaces (BCIs). However,
the decode of MI-EEG using convolutional neural networks (CNNs) remains a challenge due …
the decode of MI-EEG using convolutional neural networks (CNNs) remains a challenge due …
A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design
K Jindal, R Upadhyay, HS Singh - International Journal of …, 2022 - Wiley Online Library
Multi‐class MI EEG analysis is an extensively used paradigm in BCI. However, multiple EEG
channels lead to redundant information extraction and would reduce the distinction among …
channels lead to redundant information extraction and would reduce the distinction among …
fNIRS-based upper limb motion intention recognition using an artificial neural network for transhumeral amputees
Prosthetic arms are designed to assist amputated individuals in the performance of the
activities of daily life. Brain machine interfaces are currently employed to enhance the …
activities of daily life. Brain machine interfaces are currently employed to enhance the …
Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface
Background Motor imagery brain-computer interfaces (BCIs) is a classic and potential BCI
technology achieving brain computer integration. In motor imagery BCI, the operational …
technology achieving brain computer integration. In motor imagery BCI, the operational …
Applications of Brain Computer Interface for Motor Imagery Using Deep Learning: Review on Recent Trends
Abstract Motor Imagery-Brain Computer Interface (MI-BCI) is a very important technology
gaining momentum throughout the last decade. This technology enables the linkage of brain …
gaining momentum throughout the last decade. This technology enables the linkage of brain …