{"ID":2859138,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.05559","arxiv_id":"2510.05559","title":"Efficient Coherence Inference Using the Demodulated Band Transform and a Generalized Linear Model","abstract":"Statistical significance testing of neural coherence is essential for distinguishing genuine cross-signal coupling from spurious correlations. A widely accepted approach uses surrogate-based inference, where null distributions are generated via time-shift or phase-randomization procedures. While effective, these methods are computationally expensive and yield discrete p-values that can be unstable near decision thresholds, limiting scalability to large EEG/iEEG datasets. We introduce and validate a parametric alternative based on a generalized linear model (GLM) applied to complex-valued time--frequency coefficients (e.g., from DBT or STFT), using a likelihood-ratio test. Using real respiration belt traces as a driver and simulated neural signals contaminated with broadband Gaussian noise, we perform dense sweeps of ground-truth coherence and compare GLM-based inference against time-shift/phase-randomized surrogate testing under matched conditions. GLM achieved comparable or superior sensitivity while producing continuous, stable p-values and a substantial computational advantage. At 80% detection power, GLM detects at C=0.25, whereas surrogate testing requires C=0.49, corresponding to an approximately 6--7 dB SNR improvement. Runtime benchmarking showed GLM to be nearly 200x faster than surrogate approaches. These results establish GLM-based inference on complex time--frequency coefficients as a robust, scalable alternative to surrogate testing, enabling efficient analysis of large EEG/iEEG datasets across channels, frequencies, and participants.","short_abstract":"Statistical significance testing of neural coherence is essential for distinguishing genuine cross-signal coupling from spurious correlations. A widely accepted approach uses surrogate-based inference, where null distributions are generated via time-shift or phase-randomization procedures. While effective, these method...","url_abs":"https://arxiv.org/abs/2510.05559","url_pdf":"https://arxiv.org/pdf/2510.05559v1","authors":"[\"Md Rakibul Mowla\",\"Sukhbinder Kumar\",\"Ariane E. Rhone\",\"Brian J. Dlouhy\",\"Christopher K. Kovach\"]","published":"2025-10-07T04:09:31Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}