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<p>Spatial filter-based calibration-training algorithms play a crucial role in improving the information transfer rate (ITR) of steady-state visual evoked potential based brain-computer interfaces (SSVEP-BCIs). These algorithms optimize spatial filters by suppressing the non-SSVEP related components, thereby enhancing the signal-to-noise ratio (SNR) of electroencephalogram (EEG) signals. However, conventional methods neglect the temporally-varying and spatially-coupled characteristics of EEG signals, leading to inherent ITR bottlenecks in BCIs. To this end, we propose a novel SSVEP detection algorithm, termed as <b>m</b>ulti-<b>s</b>timulus <b>G</b>eneralized and <b>C</b>orrected <b>C</b>anonical <b>C</b>orrelation <b>A</b>nalysis (msGC<sup>3</sup>A), which is extended and corrected from the generalized canonical correlation analysis algorithm. Specifically, we develop corrected sine-cosine reference templates that enhance the spatial filters’ generalization capability across multiple stimuli. Moreover, we formulate a weighted correlation coefficient that synergistically integrates both generalized and corrected multi-stimulus templates for further enhancement. Empirical experiments have been conducted on two publicly available benchmark SSVEP datasets, and we compared the ensemble version of our msGC<sup>3</sup>A algorithm with four state-of-the-art algorithms. The results have shown that our algorithm significantly improves SSVEP detection performance while requiring less calibration data. Furthermore, we also conducted ablation experiments to show the adaptive capacity of employing our algorithm for SSVEP-BCIs.</p>

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Multi-stimulus generalized and corrected canonical correlation analysis for enhancing SSVEP detection

  • 期刊论文

  • 发表日期：

    2025年9月24日

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  • Yanhao Lv,
  • Tian-jian Luo

摘要

Spatial filter-based calibration-training algorithms play a crucial role in improving the information transfer rate (ITR) of steady-state visual evoked potential based brain-computer interfaces (SSVEP-BCIs). These algorithms optimize spatial filters by suppressing the non-SSVEP related components, thereby enhancing the signal-to-noise ratio (SNR) of electroencephalogram (EEG) signals. However, conventional methods neglect the temporally-varying and spatially-coupled characteristics of EEG signals, leading to inherent ITR bottlenecks in BCIs. To this end, we propose a novel SSVEP detection algorithm, termed as multi-stimulus Generalized and Corrected Canonical Correlation Analysis (msGC3A), which is extended and corrected from the generalized canonical correlation analysis algorithm. Specifically, we develop corrected sine-cosine reference templates that enhance the spatial filters’ generalization capability across multiple stimuli. Moreover, we formulate a weighted correlation coefficient that synergistically integrates both generalized and corrected multi-stimulus templates for further enhancement. Empirical experiments have been conducted on two publicly available benchmark SSVEP datasets, and we compared the ensemble version of our msGC3A algorithm with four state-of-the-art algorithms. The results have shown that our algorithm significantly improves SSVEP detection performance while requiring less calibration data. Furthermore, we also conducted ablation experiments to show the adaptive capacity of employing our algorithm for SSVEP-BCIs.