1 Key Laboratory for Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, P. R. China.
2 Shanghai Ruanzhong Information Technology Co., Ltd., Shanghai, P. R. China.
Int J Neural Syst. 2018 May;28(4):1750039. doi: 10.1142/S0129065717500393. Epub 2017 Aug 13.
Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.
多集正则相关分析（MsetCCA）已成功应用于通过从多个脑电（EEG）集提取共同特征来优化参考信号，以用于脑机接口应用中的稳态视觉诱发电位（SSVEP）识别。为了避免提取可能的噪声分量作为共同特征，本研究提出了 MsetCCA 的一种复杂扩展，称为多层相关最大化（MCM）模型，以进一步提高 SSVEP 识别精度。MCM 通过执行三层相关最大化过程，结合了 CCA 和 MsetCCA 的优点。第一层是通过在 EEG 样本和正弦余弦参考信号之间进行 CCA 来提取与刺激频率相关的信息。第二层是通过 MsetCCA 提取共同特征来学习参考信号。第三层是通过再次使用 CCA 与正弦余弦参考信号重新优化参考信号集。实验研究验证了所提出的 MCM 模型在与标准 CCA 和 MsetCCA 算法的比较中的有效性。MCM 的优越性能表明，它有望为开发改进的基于 SSVEP 的脑机接口提供潜力。
