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A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi‐Kernel Extreme Learning Machine
S Guan, L Cong, F Wang, T Dong - Journal of Neuroscience Methods, 2024 - Elsevier
Background In the pursuit of finer Brain-Computer Interface commands, research focus has
shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking …
shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking …
Activation of a rhythmic lower limb movement pattern during the use of a multimodal brain–computer interface: a case study of a clinically complete spinal cord injury
Brain–computer interfaces (BCIs) that integrate virtual reality with tactile feedback are
increasingly relevant for neurorehabilitation in spinal cord injury (SCI). In our previous case …
increasingly relevant for neurorehabilitation in spinal cord injury (SCI). In our previous case …
[HTML][HTML] TMSA-Net: A novel attention mechanism for improved motor imagery EEG signal processing
Q Zhao, W Zhu - Biomedical Signal Processing and Control, 2025 - Elsevier
Electroencephalography (EEG) is a non-invasive method used to record the brain's
electrical activity, widely employed in brain-computer interface (BCI) applications for …
electrical activity, widely employed in brain-computer interface (BCI) applications for …
Spatial-Temporal Mamba Network for EEG-based Motor Imagery Classification
X Yang, Z Jia - International Conference on Advanced Data Mining …, 2024 - Springer
Motor imagery (MI) classification is key for brain-computer interfaces (BCIs). Until recent
years, numerous models had been proposed, ranging from classical algorithms like …
years, numerous models had been proposed, ranging from classical algorithms like …
Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients
J Ma, W Ma, J Zhang, Y Li, B Yang, C Shan - Scientific Reports, 2024 - nature.com
The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown
to assist stroke patients activate motor regions in the brain. In particular, the brain regions …
to assist stroke patients activate motor regions in the brain. In particular, the brain regions …
MBCNN-EATCFNet: A multi-branch neural network with efficient attention mechanism for decoding EEG-based motor imagery
S **ong, L Wang, G **a, J Deng - Robotics and Autonomous Systems, 2025 - Elsevier
The decoding performance of motor imagery (MI) based on electroencephalogram (EEG)
limits the practical applications of brain-computer interface (BCI). In this paper, we propose a …
limits the practical applications of brain-computer interface (BCI). In this paper, we propose a …
[HTML][HTML] Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment
J Khabti, S AlAhmadi, A Soudani - Sensors, 2024 - mdpi.com
One of the most promising applications for electroencephalogram (EEG)-based brain–
computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks …
computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks …
[HTML][HTML] A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain–Computer Interfaces to Enhance Motor Imagery Classification
Enhancing motor disability assessment and its imagery classification is a significant concern
in contemporary medical practice, necessitating reliable solutions to improve patient …
in contemporary medical practice, necessitating reliable solutions to improve patient …
EEG-DBNet: A Dual-Branch Network for Temporal-Spectral Decoding in Motor-Imagery Brain-Computer Interfaces
X Lou, X Li, H Meng, J Hu, M Xu, Y Zhao… - arxiv preprint arxiv …, 2024 - arxiv.org
Motor imagery electroencephalogram (EEG)-based brain-computer interfaces (BCIs) offer
significant advantages for individuals with restricted limb mobility. However, challenges such …
significant advantages for individuals with restricted limb mobility. However, challenges such …
STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition
Introduction Emotion recognition using electroencephalography (EEG) is a key aspect of
brain-computer interface research. Achieving precision requires effectively extracting and …
brain-computer interface research. Achieving precision requires effectively extracting and …