Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials

Using Hybrid Feature and Classifier Fusion for an Asynchronous Brain–Computer Interface Framework Based on Steady-State Motion Visual Evoked Potentials

This study proposes an asynchronous brain–computer interface (BCI) framework based on steady-state motion visual evoked potentials (SSMVEPs), designed to enhance the accuracy and robustness of control state recognition. The method integrates filter bank common spatial patterns (FBCSPs) and filter ba...

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Bibliographic Details
Main Authors: Bo Hu, Jun Xie, Huanqing Zhang, Junjie Liu, Hu Wang
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Applied Sciences
Subjects:
asynchronous BCI
SSMVEP
feature fusion
ensemble classification
Online Access:https://www.mdpi.com/2076-3417/15/11/6010
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