Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain–Computer Interfaces
Neurofeedback training (NFT) has been widely used in motor rehabilitation. However, NFT combined with motor imagery-based brain-computer interface (MI-BCI) faces challenges such as mental fatigue and non-personalized training strategies. Therefore, we proposed an adaptive NFT based on a VR game that...
Saved in:
| Main Authors: | Kun Wang, Yuwei Liu, Feifan Tian, Weibo Yi, Yang Zhang, Tzyy-Ping Jung, Minpeng Xu, Dong Ming |
|---|---|
| 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/11097354/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model
by: Rajesh Kannan Megalingam, et al.
Published: (2024-11-01) -
Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models
by: Abir Das, et al.
Published: (2025-07-01) -
A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification
by: Xin Deng, et al.
Published: (2025-05-01) -
LWRNPIP: Design of a light weight restrictive non-fungible token based on practically unclonable functions via image signature patterns
by: Mahesh Kumar Singh, et al.
Published: (2025-11-01) -
Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm
by: Zainab Hadi-Saleh, et al.
Published: (2025-07-01)