Zhang, Yue;
Li, Zhenhong;
Xie, Sheng Quan;
Wang, He;
Yu, Zhibin;
Zhang, Zhi-Qiang;
(2022)
Multi-Objective Optimization-Based High-Pass Spatial Filtering for SSVEP-Based Brain-Computer Interfaces.
IEEE Transactions on Instrumentation and Measurement
, 71
, Article 4000509. 10.1109/TIM.2022.3146950.
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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.
| Type: | Article |
|---|---|
| Title: | Multi-Objective Optimization-Based High-Pass Spatial Filtering for SSVEP-Based Brain-Computer Interfaces |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TIM.2022.3146950 |
| Publisher version: | https://doi.org/10.1109/tim.2022.3146950 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Brain-computer interface (BCI), CANONICAL CORRELATION-ANALYSIS, COMPONENTS, Correlation, Electrodes, Electroencephalography, electroencephalography (EEG), Engineering, Engineering, Electrical & Electronic, ENHANCING DETECTION, Feature extraction, high-pass spatial filter, Instruments & Instrumentation, multi-objective optimization, RECOGNITION, Science & Technology, Signal to noise ratio, steady-state visual evoked potential (SSVEP), Technology, Training, VISUAL-EVOKED POTENTIALS, Visualization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215233 |
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