论文标题
混合模板的规范相关分析方法，用于在数据限制条件下增强SSVEP识别
Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition
论文作者
论文摘要
在这项研究中，提出了一种基于CCA的高级算法，称为混合模板规范相关分析（HTCCA），以基于稳态视觉引起的电势（SSVEP）UUDER数据限制条件来提高脑部计算机界面（BCI）的性能。 HTCCA方法结合了来自几个受试者的训练数据以构建SSVEP模板。在两个公共基准数据集上评估的实验结果表明，当TuiaLS数量较小时，所提出的方法比检测准确性和信息传输速率比较的方法的表现。考虑到该用户友好的体验将成为BCI在实际应用中的关键因素，这是基于有限的EEG样品开发有效方法的实际应用的关键因素。这项研究表明，所提出的方法在基于SSVEP的脑部计算机界面的应用中具有巨大的潜力。
In this study, an advanced CCA-based algorithn called hybrid template canonical correlation analysis (HTCCA) was proposed to improve the performance of brain-computer interface (BCI) based on steady state visual evoked potential (SSVEP) uuder data-linited condition. The HTCCA method combines the training data from several subjects to construct SSVEP templates. The experinental results evaluated on two public benchmark datasets showed that the proposed method outperforms the compared methods in both detection accuracy and information transfer rate when the number of tuials is small.Considering that user-friendly experience will become a key factor for BCI in practical application, it is very necessary to develop effective methods based on limited EEG samples. This study demonstrates that the proposed method has great potential in the application of SSVEP-based brain-computer interfaces.
