PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analysis of Multimodal Neuroimaging Data
Felix Bießmann, Sergej Plis, Frank Meinecke, Tom Eichele and Klaus-Robert Müller
IEEE Reviews in Biomedical Engineering Volume 4, pp. 26-58, 2011.

Abstract

Each method for imaging brain activity has technical or physiological limits. Thus, combinations of neuroimaging modalities that can alleviate these limitations such as simultaneous recordings of neurophysiological and hemodynamic activity have become increasingly popular. Multimodal imaging setups can take advantage of complementary views on neural activity and enhance our understanding about how neural information processing is reflected in each modality. However, dedicated analysis methods are needed to exploit the potential of multimodal methods. Many solutions to this data integration problem have been proposed, which often renders both comparisons of results and the choice of the right method for the data at hand difficult. In this review we will discuss different multimodal neuroimaging setups, the advances achieved in basic research and clinical application and the methods used. We will provide a comprehensive overview of mathematical tools reoccurring in multimodal neuroimaging studies for artifact removal, data-driven and model-driven analyses, enabling the practitioner to try established or new combinations from these algorithmic building blocks.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
Multimodal Integration
Theory & Algorithms
ID Code:9477
Deposited By:Frank Meinecke
Deposited On:16 March 2012