Enhanced Online Continuous Brain-Control by Deep Learning-Based EEG Decoding
Objective: A growing amount of deep learning models for motor imagery (MI) decoding from electroencephalogram (EEG) have demonstrated their superiority over traditional machine learning approaches in offline dataset analysis. However, current online MI-based brain-computer interfaces (BCIs) still pr...
Saved in:
| Main Authors: | Jiaheng Wang, Lin Yao, Yueming Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11087643/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization
by: Yonghao Song, et al.
Published: (2023-01-01) -
IFNet: An Interactive Frequency Convolutional Neural Network for Enhancing Motor Imagery Decoding From EEG
by: Jiaheng Wang, et al.
Published: (2023-01-01) -
Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning
by: Zhibin Jiang, et al.
Published: (2025-04-01) -
Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery
by: Mustafa Yazıcı, et al.
Published: (2025-06-01) -
Systematic review: progress in EEG-based speech imagery brain-computer interface decoding and encoding research
by: Ke Su, et al.
Published: (2025-06-01)