Using Brain Network Features to Increase the Classification Accuracy of MI-BCI Inefficiency Subject

Using Brain Network Features to Increase the Classification Accuracy of MI-BCI Inefficiency Subject

Motor imagery-based brain-computer interface (MI-BCI) inefficiency phenomenon is one of the biggest challenges in MI-BCI research. BCI inefficiency subject is defined as the subject who cannot achieve classification accuracy higher than 70% since 70% is considered to be the minimum accuracy for comm...

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Bibliographic Details
Main Authors: Rui Zhang, Xianpeng Li, Yinwang Wang, Bo Liu, Li Shi, Mingming Chen, Lipeng Zhang, Yuxia Hu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Motor imagery
brain–computer interface (BCI)
BCI inefficiency
network feature
feature extraction
Online Access:https://ieeexplore.ieee.org/document/8718003/

Internet

https://ieeexplore.ieee.org/document/8718003/

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