Besharat Amin, Samadzadehaghdam Nasser, Ghadiri Tahereh
Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
Department of Neuroscience and Cognition, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran.
Front Neurosci. 2025 Apr 25;19:1544452. doi: 10.3389/fnins.2025.1544452. eCollection 2025.
Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) rely on the brain's response to visual stimuli. However, accurately recognizing target frequencies using training-based methods remains challenging due to the time-consuming calibration sessions required by subject-specific training methods.
To address this limitation, this study proposes a novel hybrid method called Hybrid task-related component and canonical correlation analysis (H-TRCCA). In the training phase, four spatial filters are derived using canonical correlation analysis (CCA) to maximize the correlation between the training data and reference signals. Additionally, a spatial filter is also computed using task-related component analysis (TRCA). In the test phase, correlation coefficients obtained from the CCA method are clustered using the k-means++ clustering algorithm. The cluster with the highest average correlation identifies the candidate stimuli. Finally, for each candidate, the correlation values are summed and combined with the TRCA-based correlation coefficients.
The H-TRCCA algorithm was validated using two publicly available benchmark datasets. Experimental results using only two training trials per frequency with 1s data length showed that H-TRCCA achieved average accuracies of 91.44% for Dataset I and 80.46% for Dataset II. Additionally, it achieved maximum average information transfer rates of 188.36 bits/min and 139.96 bits/min for Dataset I and II, respectively.
Remarkably H-TRCCA achieves comparable performance to other methods that require five trials, utilizing only two or three training trials. The proposed H-TRCCA method outperforms state-of-the-art techniques, showing superior performance and robustness with limited calibration data.
基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）依赖于大脑对视觉刺激的反应。然而，由于特定受试者训练方法需要耗时的校准环节，使用基于训练的方法准确识别目标频率仍然具有挑战性。
为解决这一局限性，本研究提出了一种名为混合任务相关成分与典型相关分析（H-TRCCA）的新型混合方法。在训练阶段，使用典型相关分析（CCA）导出四个空间滤波器，以最大化训练数据与参考信号之间的相关性。此外，还使用任务相关成分分析（TRCA）计算一个空间滤波器。在测试阶段，使用k均值++聚类算法对从CCA方法获得的相关系数进行聚类。平均相关性最高的聚类确定候选刺激。最后，对每个候选刺激，将相关值求和并与基于TRCA的相关系数相结合。
使用两个公开可用的基准数据集对H-TRCCA算法进行了验证。使用每个频率仅两次训练试验且数据长度为1秒的实验结果表明，对于数据集I，H-TRCCA的平均准确率达到91.44%，对于数据集II为80.46%。此外，对于数据集I和II，它分别实现了最大平均信息传输速率188.36比特/分钟和139.96比特/分钟。
值得注意的是，H-TRCCA仅使用两到三次训练试验就实现了与其他需要五次试验的方法相当的性能。所提出的H-TRCCA方法优于现有技术，在有限的校准数据下表现出卓越的性能和鲁棒性。
