Complexity-based Encoded Information Quantification in Neurophysiological Recordings

“Complexity-based Encoded Information Quantification in Neurophysiological Recordings” by Julian Fuhrer, Alejandro Blenkmann, Tor Endestad, Anne-Kristin Solbakk, and Kyrre Glette. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022, pp. 2319-2323.

Abstract

Brain activity differs vastly between sleep, cognitive tasks, and action. Information theory is an appropriate concept to analytically quantify these brain states. Based on neurophysiological recordings, this concept can handle complex data sets, is free of any requirements about the data structure, and can infer the present underlying brain mechanisms. Specifically, by utilizing algorithmic information theory, it is possible to estimate the absolute information contained in brain responses. While current approaches that apply this theory to neurophysiological recordings can discriminate between different brain states, they are limited in directly quantifying the degree of similarity or encoded information between brain responses. Here, we propose a method grounded in algorithmic information theory that affords direct statements about responses' similarity by estimating the encoded information through a compression-based scheme. We validated this method by applying it to both synthetic and real neurophysiological data and compared its efficiency to the mutual information measure. This proposed procedure is especially suited for task paradigms contrasting different event types because it can precisely quantify the similarity of neuronal responses.

BibTeX entry:

@inproceedings{fuhrer-embc2022,
   author = {Julian Fuhrer and Alejandro Blenkmann and Tor Endestad and
	Anne-Kristin Solbakk and Kyrre Glette},
   title = {Complexity-based Encoded Information Quantification in
	Neurophysiological Recordings},
   booktitle = {2022 44th Annual International Conference of the IEEE
	Engineering in Medicine and Biology Society (EMBC)},
   pages = {2319-2323},
   publisher = {IEEE},
   year = {2022},
   url = {https://doi.org/10.1109/EMBC48229.2022.9871501}
}

(This webpage was created with bibtex2web.)

Back to publications.