Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

Canonical correlation analysis (CCA) has been used for the steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) for a long time. However, the reference signal of CCA is relatively simple and lacks subject-specific information. Moreover, over-fitting may occur when a shor...

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Bibliographic Details
Main Authors: Yuhang Gao, Juanning Si, Sijin Wu, Weixian Li, Hao Liu, Jianhu Chen, Qing He, Yujin Zhang
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Applied Sciences
Subjects:
steady-state visual evoked potential (SSVEP)
brain-computer interface (BCI)
l1-regularized multiway canonical correlation analysis (L1-MCCA)
support vector machine (SVM)
particle swarm optimization (PSO)
Online Access:https://www.mdpi.com/2076-3417/11/23/11453

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https://www.mdpi.com/2076-3417/11/23/11453

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