Unsupervised Decomposition Methods for Analysis of Multimodal Neural Data

Technical advances in the field of noninvasive neuroimaging allow for innovative therapeutical strategies with application potential in neural rehabilitation. To improve these methods, combinations of multiple imaging modalities have become an important topic of research. This chapter reviews some of the most popular unsupervised statistical learning techniques used in the context of neuroscientific data analysis, and places a special focus on multimodal neural data. It starts with the well-known principal component analysis (PCA). First, the chapter shows how to derive the algorithm and provides illustrative examples of the advantages and disadvantages of standard PCA. The second method presented is canonical correlation analysis (CCA): a multivariate analysis method that reveals maximally correlated features of simultaneously acquired multiple data streams. Finally the chapter presents a straightforward extension of CCA that estimates the correct solution even in the presence of noninstantaneous couplings, that is, temporal delays or convolutions between data sources.