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A Hybrid Method Fusing Frequency Recognition With Attention Detection to Enhance an Asynchronous Brain-Computer

Jing Zhao, Ye Shi, Wenzheng Liu

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 12, 2023
    PubMed
    Summary

    This study introduces a hybrid attention detection and frequency recognition method (ADFR-DS) to improve asynchronous brain-computer interface (BCI) control. The new method enhances accuracy and information transfer rates in steady-state visual evoked potential (SSVEP)-based BCI systems.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Controlling asynchronous brain-computer interface (BCI) systems requires accurately distinguishing between user control and idle states.
    • Steady-state visual evoked potential (SSVEP)-based BCIs are a common type, but their asynchronous control performance needs enhancement.

    Purpose of the Study:

    • To propose and evaluate a novel hybrid attention detection and frequency recognition method based on weighted Dempster-Shafer theory (ADFR-DS).
    • To improve the asynchronous control performance of SSVEP-based BCI systems by integrating information from two brain regions.

    Main Methods:

    • The ADFR-DS method employs a hybrid architecture processing electroencephalogram (EEG) data from prefrontal (attention detection via IFBOCN) and occipital (frequency recognition via eTRCA) areas.
    • A novel weighted Dempster-Shafer fusion approach is used to combine classification results from both algorithms at the decision level.
    • The method was evaluated using a 40-target dataset from 35 participants.

    Main Results:

    • The ADFR-DS method demonstrated superior performance compared to the eTRCA algorithm in true positive rate (TPR), true negative rate (TNR), accuracy (ACC), and information transfer rate (ITR).
    • ADFR-DS improved average ACC from 62.71% to 69.30% and average ITR from 184.28 to 216.89 bits/min (at 0.3s data length).

    Conclusions:

    • The proposed ADFR-DS method effectively enhances asynchronous SSVEP-based BCI systems.
    • Integrating multi-region EEG data and employing weighted Dempster-Shafer fusion improves BCI control performance.