Hemakom Apit, Goverdovsky Valentin, Looney David, Mandic Danilo P
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065):20150199. doi: 10.1098/rsta.2015.0199.
An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative brain-computer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate.
提出了一种多元经验模态分解（MEMD）的扩展方法，称为自适应投影本征变换MEMD（APIT-MEMD），以适应现实世界多通道数据中的功率不平衡和通道间相关性。结果表明，对于大量投影向量，APIT-MEMD表现出与MEMD相似或更好的性能，而在筛选算法中投影向量数量较少的关键情况下，它优于MEMD。我们还在提出的本征多尺度分析框架内采用了噪声辅助APIT-MEMD，并基于稳态视觉诱发电位和P300反应，说明了这种方法在以噪声为主的协作式脑机接口（BCI）中的优势。最后，我们表明，对于联合认知BCI任务，所提出的本征多尺度分析框架在信息传输率方面提高了系统性能。
