Machine Learning for Multimodal Neuroimaging
published: April 3, 2014, recorded: February 2014, views: 3380
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The combination of multiple neuroimaging modalities has become an important field of research. While the technical challenges associated with multimodal neuroimaging have been mastered more than a decade ago, analysis techniques for multimodal neuroimaging data are still being developed. This tutorial will cover data driven analysis techniques for multimodal neuroimaging, including recent advances in multimodal brain-computer-interfaces and in integration of neural bandpower signals with hemodynamic signals. A special focus will be placed on simple and efficient subspace methods that are useful in all stages of multimodal neuroimaging analyses, starting from basic preprocessing and artifact removal to integration of multiple modalities with complex spatiotemporal coupling dynamics.
Download slides: bbci2014_biessmann_machine_learning_01.pdf (17.3 MB)
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