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

doi:10.1109/TIM.2022.3146950

2022-01-01

张智强 (Zhiqiang Zhang) *

DOI：10.1109/TIM.2022.3146950

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摘要

En 中文

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.

Keyword:

Training

Electroencephalography

Visualization

Correlation

Feature extraction

Electrodes

Signal to noise ratio

Brain-computer interface (BCI)

electroencephalography (EEG)

high-pass spatial filter

multi-objective optimization

steady-state visual evoked potential (SSVEP)

期刊

IEEE Transactions on Instrumentation and Measurement

IF:

5.9

论文数:

1.9W

被引数:

5.8W

机构

Southwest Jiaotong University

学者数:

2.9W

论文数: 2.1W

被引数: 2.3W

university of leeds

学者数:

3.6W

论文数: 3.3W

被引数: 45

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