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Tensor based blind source separation for current source density analysis of evoked potentials from somatosensory cortex of mice

: Costa, J.P.C.L. da; Kehrle Miranda, R.; Rosa Zanatta, M. da


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Engineering in Medicine and Biology Society -EMBS-:
8th International IEEE EMBS Conference on Neural Engineering 2017 : Regal International East Asia Hotel, Shanghai, China, May 25-28, 2017
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-1916-2
ISBN: 978-1-5090-4603-4
ISBN: 978-1-5090-4604-1
International Conference on Neural Engineering (NER) <8, 2017, Shanghai>
Conference Paper
Fraunhofer IIS ()

In order to understand brain mechanisms and functionalities, neural probes with electrode arrays are incorporated into mice and Local Field Potentials (LFP) are recorded indicating the activities of groups of neurons. Next, the brain activity CP can be analyzed in terms of Current Source Density CP (CSD), which are computed via the LFP. In this paper, we propose the analysis of the somatosensory cortex signals of a mouse applying Blind Source Separation (BSS) schemes. In contrast to the standard CSD, we show that signal separation using BSS schemes can be useful to identify groups of neurons of different layers of the somatosensory cortex that are associated. Another contribution of this work is to propose the use of the PARAFAC model on the analysis of somatosensory cortex signals, whose results are consistent with results obtained via Spatiotemporal Independent Component Analysis (stICA).