Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.
利用高频刺激进行编码能够缓解基于稳态视觉诱发电位（SSVEP）的脑-机接口（BCI）产生的用户视觉疲劳，提升系统的舒适度和安全性，具有广阔的应用前景。然而，当前先进的SSVEP解码算法大多在低频数据集上进行对比验证，在高频SSVEP信号上的识别性能仍然未知。针对此问题，本文采集了20名受试者在高频SSVEP范式下的脑电（EEG）数据，对目前主流的2种典型相关分析算法、3种集成任务相关成分分析算法和1种任务判别成分分析算法展开对比。结果表明，它们均能有效解码高频SSVEP信号，且在不同条件下算法的分类性能指标和速度存在差异。本研究为高频SSVEP-BCI系统的算法选择提供了依据，在构建舒适友好型BCI系统方面具有潜在的应用价值。.
Keywords: Brain-computer interface; Decoding algorithm; High-frequency; Steady-state visual evoked potential.