Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, 610054, China.
J Neurosci Methods. 2014 Jan 15;221:32-40. doi: 10.1016/j.jneumeth.2013.07.018. Epub 2013 Aug 6.
Multichannel frequency recognition methods are prevalent in SSVEP-BCI systems. These methods increase the convenience of the BCI system for users and require no calibration data. A novel multivariate synchronization index (MSI) for frequency recognition was proposed in this paper. This measure characterized the synchronization between multichannel EEGs and the reference signals, the latter of which were defined according to the stimulus frequency. For the simulation and real data, the proposed method showed better performance than the widely used canonical correlation analysis (CCA) and minimum energy combination (MEC), especially for short data length and a small number of channels. The MSI was also implemented successfully in an online SSVEP-based BCI system, thus further confirming its feasibility for application systems. Because fast and accurate recognition is crucial for practical systems, we recommend MSI as a potential method for frequency recognition in future SSVEP-BCI.
多通道频率识别方法在 SSVEP-BCI 系统中较为常见。这些方法提高了 BCI 系统对用户的便利性，且无需校准数据。本文提出了一种新的多变量同步指数 (MSI) 用于频率识别。该指标刻画了多通道 EEG 与参考信号之间的同步性，参考信号是根据刺激频率定义的。对于仿真和真实数据，与广泛使用的典型相关分析 (CCA) 和最小能量组合 (MEC) 相比，所提出的方法表现出更好的性能，尤其是在数据长度较短和通道数量较少的情况下。MSI 还成功地应用于在线 SSVEP 基 BCI 系统中，从而进一步证实了其在应用系统中的可行性。由于快速准确的识别对于实际系统至关重要，因此我们建议将 MSI 作为未来 SSVEP-BCI 中频率识别的一种潜在方法。
