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Graph-based Recurrence Quantification Analysis of EEG Spectral Dynamics for Motor Imagery-based BCIs
Summary
Recurrence Quantification Analysis (RQA) and complex network features significantly enhance brain-computer interface (BCI) performance by analyzing nonlinear electroencephalogram (EEG) dynamics, improving motor imagery classification accuracy.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Complex Systems
Background:
- Brain-computer interfaces (BCIs) currently lack efficiency and reliability for severely disabled patients.
- Conventional motor imagery (MI)-based BCIs using spectral analysis show limited performance.
Purpose of the Study:
- To investigate Recurrence Quantification Analysis (RQA) and complex network theory for improved MI-BCI performance.
- To explore nonlinear dynamics of neural responses for enhanced MI classification.
Main Methods:
- EEG data from MI-Rest tasks were analyzed using phase space trajectories and recurrence plots (RPs).
- Eight nonlinear graph-based RQA features were extracted and compared to spectral features.
- A linear support vector machine (SVM) classifier with 5-fold cross-validation was used.
Main Results:
- Nonlinear graph-based RQA features improved MI-BCI average performance by 5.8% compared to classical spectral features.
Conclusions:
- RQA and complex network analysis offer new dimensions for analyzing nonlinear EEG characteristics.
- These methods show potential for enhancing MI-BCI performance and reliability.
