Van De Ville, DimitriDimitriVan De VilleTHIERRY BLUUnser, MichaelMichaelUnser2024-03-082024-03-082006-12-01142440469X15206149https://scholars.lib.ntu.edu.tw/handle/123456789/640619Recently, we have proposed a new framework for detecting brain activity from fMRI data, which is based on the spatial discrete wavelet transform. The standard wavelet-based approach performs a statistical test in the wavelet domain, and therefore fails to provide a rigorous statistical interpretation in the spatial domain. The new framework provides an "integrated" approach: the data is processed in the wavelet domain (by thresholding wavelet coefficients), and a suitable statistical testing procedure is applied afterwards in the spatial domain. This method is based on conservative assumptions only and has a strong type-I error control by construction. At the same time, it has a sensitivity comparable to that of SPM. Here, we discuss the extension of our algorithm to the redundant discrete wavelet transform, which provides a shift-invariant detection scheme. The key features of our technique are illustrated with experimental results. An implementation of our framework is available as a toolbox (WSPM) for the SPM2 software. © 2006 IEEE.conference paper2-s2.0-33846008183https://api.elsevier.com/content/abstract/scopus_id/33846008183