MSMGE-CNN: a multi-scale multi-graph embedding convolutional neural network for motor related EEG decoding
Deep learning technique has been widely used for decoding motor related electroencephalography (EEG) signals, which has considerably driven the development of motor related brain–computer interfaces (BCIs). However, traditional convolutional neural networks (CNNs) cannot fully represent spatial topo...
Saved in:
| Main Authors: | Binren Wang, Minmin Miao, Ke Zhang, Wenzhe Liu, Zhenzhen Sheng, Baoguo Xu, Wenjun Hu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/ad9135 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
FBATCNet: A Temporal Convolutional Network With Frequency Band Attention for Decoding Motor Imagery EEG
by: Shuaishuai Ma, et al.
Published: (2025-01-01) -
3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal
by: Xiaoguang Li, et al.
Published: (2024-12-01) -
AMEEGNet: attention-based multiscale EEGNet for effective motor imagery EEG decoding
by: Xuejian Wu, et al.
Published: (2025-01-01) -
FACT-Net: A Frequency Adapter CNN With Temporal-Periodicity Inception for Fast and Accurate MI-EEG Decoding
by: Sixiong Ke, et al.
Published: (2024-01-01) -
Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients
by: Jun Ma, et al.
Published: (2024-11-01)