Multi-Objective Optimization-Based High-Pass Spatial Filtering for SSVEP-Based Brain-Computer Interfaces

Many spatial filtering methods have been proposed to enhance the target identification performance for the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). The existing approaches tend to learn spatial filter parameters of a certain target using only the training da...

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Vydáno v:	IEEE transactions on instrumentation and measurement Ročník 71; s. 1 - 9
Hlavní autoři:	Zhang, Yue, Li, Zhenhong, Xie, Sheng Quan, Wang, He, Yu, Zhibin, Zhang, Zhi-Qiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Brain–computer interface (BCI) Correlation Correlation analysis Datasets Electrodes Electroencephalography electroencephalography (EEG) Feature extraction high-pass spatial filter Human-computer interface multi-objective optimization Multiple objective analysis Optimization Signal to noise ratio Spatial filtering steady-state visual evoked potential (SSVEP) Target recognition Training Visualization
ISSN:	0018-9456, 1557-9662
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Abstract	Many spatial filtering methods have been proposed to enhance the target identification performance for the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). The existing approaches tend to learn spatial filter parameters of a certain target using only the training data from the same stimulus, and they rarely consider the information from other stimuli or the volume conduction problem during the training process. In this article, we propose a novel multi-objective optimization-based high-pass spatial filtering method to improve the SSVEP detection accuracy and robustness. The filters are derived via maximizing the correlation between the training signal and the individual template from the same target whilst minimizing the correlation between the signal from other targets and the template. The optimization will also be subject to the constraint that the sum of filter elements is zero. The evaluation study on two self-collected SSVEP datasets (including 12 and four frequencies, respectively) shows that the proposed method outperformed the compared methods such as canonical correlation analysis (CCA), multiset CCA (MsetCCA), sum of squared correlations (SSCOR), and task-related component analysis (TRCA). The proposed method was also verified on a public 40-class SSVEP benchmark dataset recorded from 35 subjects. The experimental results have demonstrated the effectiveness of the proposed approach for enhancing the SSVEP detection performance.
AbstractList	Many spatial filtering methods have been proposed to enhance the target identification performance for the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). The existing approaches tend to learn spatial filter parameters of a certain target using only the training data from the same stimulus, and they rarely consider the information from other stimuli or the volume conduction problem during the training process. In this article, we propose a novel multi-objective optimization-based high-pass spatial filtering method to improve the SSVEP detection accuracy and robustness. The filters are derived via maximizing the correlation between the training signal and the individual template from the same target whilst minimizing the correlation between the signal from other targets and the template. The optimization will also be subject to the constraint that the sum of filter elements is zero. The evaluation study on two self-collected SSVEP datasets (including 12 and four frequencies, respectively) shows that the proposed method outperformed the compared methods such as canonical correlation analysis (CCA), multiset CCA (MsetCCA), sum of squared correlations (SSCOR), and task-related component analysis (TRCA). The proposed method was also verified on a public 40-class SSVEP benchmark dataset recorded from 35 subjects. The experimental results have demonstrated the effectiveness of the proposed approach for enhancing the SSVEP detection performance.
Author	Zhang, Zhi-Qiang Xie, Sheng Quan Li, Zhenhong Wang, He Zhang, Yue Yu, Zhibin
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SubjectTerms	Brain–computer interface (BCI) Correlation Correlation analysis Datasets Electrodes Electroencephalography electroencephalography (EEG) Feature extraction high-pass spatial filter Human-computer interface multi-objective optimization Multiple objective analysis Optimization Signal to noise ratio Spatial filtering steady-state visual evoked potential (SSVEP) Target recognition Training Visualization
Title	Multi-Objective Optimization-Based High-Pass Spatial Filtering for SSVEP-Based Brain-Computer Interfaces
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