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

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Blind Source Separation

Part of the book series: Signals and Communication Technology ((SCT))

Abstract

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 the most common method 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.

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Notes

  1. 1.

    See Sect. 19.3.1 for more details.

  2. 2.

    See Sect. 19.3.2 for more details.

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Authors and Affiliations

  1. LET’S-ISTC-CNR, Fatebenefratelli Hospital—Isola Tiberna, Rome, Italy

    Camillo Porcaro & Franca Tecchio

  2. Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK

    Camillo Porcaro

  3. Department of Neuroimaging, IRCCS San Raffaele Pisana, Rome, Italy

    Franca Tecchio

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  1. Camillo Porcaro
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  2. Franca Tecchio
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Correspondence to Camillo Porcaro .

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Editors and Affiliations

  1. University of Technology, Sydney, Sydney, Australia

    Ganesh R. Naik

  2. University of Surrey, Guildford, United Kingdom

    Wenwu Wang

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Porcaro, C., Tecchio, F. (2014). Semi-blind Functional Source Separation Algorithm from Non-invasive Electrophysiology to Neuroimaging. In: Naik, G., Wang, W. (eds) Blind Source Separation. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55016-4_19

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  • DOI: https://doi.org/10.1007/978-3-642-55016-4_19

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