Motor Imagery Classification for Asynchronous EEG-Based Brain–Computer Interfaces
Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user’s MI without ex...
Saved in:
Main Authors: | Huanyu Wu (Author), Siyang Li (Author), Dongrui Wu (Author) |
---|---|
Format: | Book |
Published: |
IEEE,
2024-01-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
by: Tianwang Jia, et al.
Published: (2024) -
User Identity Protection in EEG-Based Brain–Computer Interfaces
by: Lubin Meng, et al.
Published: (2023) -
Front-End Replication Dynamic Window (FRDW) for Online Motor Imagery Classification
by: Xinru Chen, et al.
Published: (2023) -
Evaluating the Feasibility of Visual Imagery for an EEG-Based Brain–Computer Interface
by: Justin Kilmarx, et al.
Published: (2024) -
EEG-Based Brain–Computer Interfaces are Vulnerable to Backdoor Attacks
by: Lubin Meng, et al.
Published: (2023)