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Semi-blind Functional Source Separation Algorithm from Non-invasive Electrophysiology to Neuroimaging

Lookup NU author(s): Dr Camillo Porcaro


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Neuroimaging, investigating how specific brain sources play a particular role in a definite cognitive or sensorimotor process, can be achieved from non-invasive electrophysiological (EEG, EMG, MEG) and multimodal (concurrent EEG-fMRI) recordings. However, especially for the non-invasive electrophysiological techniques, the signals measured at the scalp are a mixture of the contributions from multiple generators or sources added to background activity and system noise, meaning that it is often difficult to identify the dynamic activity of generators of interest starting from the electrode/sensor recordings.Although themost commonmethod of overcoming this limitation is time-domain averaging with or without source localization, blind source separation (BSS) algorithms are becoming increasingly widely accepted as a way of extracting the different neuronal sources that contribute to the measured scalp signals without trial exclusion. The advantage of BSS or semi-blind source separation (semi-BSS) techniques compared to methods such as time-domain averaging lies in their ability to extract sources exploring the whole time evolving data. Taking into account the whole signal without averaging, it also provides a means suitable to investigate non-phase locked oscillatory processes and single-trial behaviour. This characteristic becomes a crucial issue when investigating combined EEG-fMRI data, particularly when the focus is on neurovascular coupling definitely dependent on single trial variability of the two datasets. In this context, this chapter describes a semi-BSS technique, Functional Source Separation (FSS), which is a tool to identify cerebral sources by exploiting a priori knowledge, such as spectral or evoked activity, which cannot be expressed by sources other than the one to be identified (functional fingerprint). In other words, FSS allows the identification of specific neuronal pools on the bases of their functional roles, independent of their spatial position.

Publication metadata

Author(s): Porcaro C, Tecchio F

Editor(s): G. R. Naik and W. Wang

Publication type: Book Chapter

Publication status: Published

Book Title: Blind Source Separation

Year: 2014

Volume: Signals and Communication Technology

Pages: 521-551

Number of Volumes: 19

Publisher: Springer-Verlag Berlin Heidelberg


DOI: 10.1007/978-3-642-55016-4_19