{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T10:27:37Z","timestamp":1783765657945,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T00:00:00Z","timestamp":1644796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the background of all human thinking\u2014acting and reacting are sets of connections between different neurons or groups of neurons. We studied and evaluated these connections using electroencephalography (EEG) brain signals. In this paper, we propose the use of the complex Pearson correlation coefficient (CPCC), which provides information on connectivity with and without consideration of the volume conduction effect. Although the Pearson correlation coefficient is a widely accepted measure of the statistical relationships between random variables and the relationships between signals, it is not being used for EEG data analysis. Its meaning for EEG is not straightforward and rarely well understood. In this work, we compare it to the most commonly used undirected connectivity analysis methods, which are phase locking value (PLV) and weighted phase lag index (wPLI). First, the relationship between the measures is shown analytically. Then, it is illustrated by a practical comparison using synthetic and real EEG data. The relationships between the observed connectivity measures are described in terms of the correlation values between them, which are, for the absolute values of CPCC and PLV, not lower that 0.97, and for the imaginary component of CPCC and wPLI\u2014not lower than 0.92, for all observed frequency bands. Results show that the CPCC includes information of both other measures balanced in a single complex-numbered index.<\/jats:p>","DOI":"10.3390\/s22041477","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T20:58:03Z","timestamp":1644872283000},"page":"1477","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":135,"title":["Complex Pearson Correlation Coefficient for EEG Connectivity Analysis"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3461-963X","authenticated-orcid":false,"given":"Zoran","family":"\u0160verko","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Department of Automation and Electronics, University of Rijeka, 51000 Rijeka, Croatia"},{"name":"Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, 6000 Koper, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1036-9261","authenticated-orcid":false,"given":"Miroslav","family":"Vranki\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Department of Automation and Electronics, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-9253","authenticated-orcid":false,"given":"Sa\u0161a","family":"Vlahini\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Department of Automation and Electronics, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2939-6945","authenticated-orcid":false,"given":"Peter","family":"Rogelj","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, 6000 Koper, Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,14]]},"reference":[{"key":"ref_1","unstructured":"Fornito, A., Zalesky, A., and Bullmore, E. 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