Hypergraphs from multivariate connectivity: caCOH-based EEG/MEG representation

European Physical Journal: Special Topics. 2026. P. 1–16.

Vlasenko D., Saranskaia I., Zakharov D.

Hypergraphs provide a natural framework for representing neurophysiological interactions distributed across sets of sensors. A key methodological question is how hyperedges should be defined from frequency-resolved electroencephalography/magnetoencephalography (EEG/MEG) data. We demonstrate a construction strategy in which hyperedges are obtained from canonical coherence (caCOH), an extension of coherence that estimates coupling between multidimensional signal spaces. To our knowledge, this is the first work to construct hypergraphs directly from a multivariate connectivity measure specifically designed for frequency-resolved neurophysiological analysis. We propose two caCOH-based representations: a one-to-space hypergraph, where each external signal defines a hyperedge over the EEG/MEG sensor space, and a space-to-space hypergraph, where two multidimensional signal spaces are represented by a single hyperedge. We evaluate the approach in controlled simulations with known coupling frequencies and varying signal-to-noise ratio (SNR). Compared with graphs based on magnitude-squared coherence (MSC), caCOH-based hypergraphs showed statistically higher target–baseline contrasts at almost all SNR levels, indicating stronger recovery of coupling frequencies. They also recovered sensor-level spatial patterns associated with the simulated sources. In addition, one-to-space and space-to-space representations reduced 610 MSC edges per frequency to ten hyperedges and one hyperedge, respectively. These results establish multivariate spectral connectivity as a natural methodological basis for EEG/MEG hypergraphs.

Keywords: EEG, ЭЭГ, MEG, МЭГ, hypergraphs, гиперграфы, caCOH, frequency-resolved connectivity, multivariate connectivity, Каноническая когерентность caCOH, Частотно-разрешающая связность, Многомерная связность

Publication based on the results of: Multidisciplinary study of behavior and decision-making in health population and patients using behavioral, economic, neurocognitive, neuroeconomic, neurocomputational and neural network approaches (2025)