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Efficient coherence inference on complex time–frequency coefficients using a general linear model
Journal article   Peer reviewed

Efficient coherence inference on complex time–frequency coefficients using a general linear model

Md Rakibul Mowla, Sukhbinder Kumar, Ariane E. Rhone, Brian J. Dlouhy and Christopher K. Kovach
Journal of neuroscience methods, Vol.433, 110791
09/2026
DOI: 10.1016/j.jneumeth.2026.110791
PMCID: PMC13196732
PMID: 42114625

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Abstract

Statistical significance testing of neural coherence is essential for distinguishing genuine cross-signal coupling from spurious correlations. Surrogate-based inference—typically using time shifts or phase randomization—is widely used but computationally expensive and produces discrete and sometimes unstable p-values, limiting scalability for large EEG/iEEG datasets. We introduce a parametric framework based on a general linear model (GLM) applied to complex-valued time–frequency coefficients (e.g., from the demodulated band transform or short-time Fourier transform). A likelihood ratio test provides continuous coherence significance estimates without surrogate resampling. Using real respiration traces as a driver and simulated neural signals with Gaussian broadband noise, we performed dense sweeps of ground-truth coherence. The GLM achieved sensitivity comparable to or better than surrogate testing and produced stable continuous p-values. At 80% detection power, the GLM detected coherence at C≈0.16, whereas surrogate testing required C≈0.31, corresponding to an ∼8 dB improvement in signal-to-noise ratio. Runtime benchmarking showed an ∼190× speed increase over surrogate-based methods. Compared with time-shift and phase-randomization surrogates, the GLM provided matched or superior sensitivity while eliminating the permutation floor and dramatically reducing computation, particularly for dense frequency grids and multichannel datasets. GLM-based inference offers a robust, statistically principled, and computationally scalable alternative to surrogate-based coherence testing, enabling efficient analysis across channels, frequencies, and participants in large EEG/iEEG studies. •Presents a GLM-based parametric method for coherence significance testing.•Removes the need for surrogate resampling in spectral coherence analysis.•Matches the sensitivity of phase-randomization tests with continuous p-values.•Achieves ̃190-fold computational speedup over surrogate-based methods.•Scales efficiently to large multichannel EEG/iEEG datasets and high-rate recordings.
 Circular time shift Demodulated band transform (DBT) General linear model (GLM) Neural coherence Phase randomization Surrogate testing

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