Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets

C. Vidaurre*, G. Nolte, I. E.J. de Vries, M. Gómez, T. W. Boonstra, K. R. Müller, A. Villringer, V. V. Nikulin

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

Original languageEnglish
Article number116009
Pages (from-to)1-11
Number of pages11
Early online date11 Jul 2019
Publication statusPublished - 1 Nov 2019


C.V. was supported by the Spanish Ministry of Economy with Grant RyC 2014-15671 . G.N. was partially funded by the German Research Foundation (DFG, SFB936 Z3 and TRR169, B4 ). K.-R.M. work was supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E , 01GQ1115 and 01GQ0850 ; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689 and by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451 , No. 2017-0-01779 ). T.W.B. was supported by a Future Fellowship from the Australian Research Council ( FT180100622 ). V.V.N. was partially supported by the Center for Bioelectric Interfaces NRU HSE, RF Government grant , ag. No. 14.641.31.0003 . The authors thank Katherina von Carlowitz-Ghori for her support with rCMC code and results. Appendix A

FundersFunder number
Center for Bioelectric Interfaces NRU HSE
German Ministry for Education and Research
Institute for Information & Communications Technology Planning & Evaluation
RF Government14.641.31.0003
Spanish Ministry of Economy
Australian Research CouncilFT180100622
Deutsche ForschungsgemeinschaftTRR169, SFB936 Z3
Bundesministerium für Bildung und Forschung01GQ1115, 01IS14013A-E, 01GQ0850, EXC 2046/1, 390685689
Bundesministerium für Bildung und Frauen
Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaRyC 2014-15671
Institute for Information and Communications Technology Promotion2017-0-00451, 2017-0-01779


    • Coherence optimization
    • Cortico-muscular coherence (CMC)
    • Electroencephalography (EEG)
    • Electromyography (EMG)
    • High density electromyography (HDsEMG)
    • Local field potentials (LFP)
    • Magnetoencephalography (MEG)
    • Multimodal methods
    • Multivariate methods


    Dive into the research topics of 'Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets'. Together they form a unique fingerprint.

    Cite this