Lagged Hilbert autocoherence (LHaC)

Lagged Hilbert autocoherence, phase-locking value, and amplitude autocoherence. Library accompanying the paper:

Zhang S, Szul MJ, Papadopoulos S, Massera A, Rayson H*, & Bonaiuto JJ* (* authors contributed equally)
Multi-scale parameterization of neural rhythmicity with lagged Hilbert autocoherence.
Imaging Neuroscience 2025, https://direct.mit.edu/imag/article/doi/10.1162/IMAG.a.993/133660.

Requirements

python version: joblib, scipy, numpy, MNE

matlab version: parallel processing toolbox, signal processing toolbox

Python files

• lagged_autocoherence.py: Core functions
• demo.ipynb: Demonstration of the new lagged autocoherence algorithm
• multi_trial_demo.ipynb: Demonstration of new lagged autocoherence algorithm with multiple trials

Matlab files

• lagged_hilbert_autocoherence.m: Core function
• generate_surrogate.m: Phase-shuffled surrogate data generation
• demo.m: Demonstration of new lagged autocoherence algorithm
• multi_trial_demo.m: Demonstration of new lagged autocoherence algorithm with multiple trials
• rfft.m: Fourier transform of real signal
• irfft.m: Inverse Fourier transform of real signal
• hilbert.m: Hilbert transform

Analyses from Zhang et al. (2025)

• python/run_sims_0_surrogate_comparison.py: Compare phase-shuffled and ARMA surrogates
• python/run_sims_1_joint_amp_norm_factor.py: Investigate joint amplitude normalization factor inflation
• python/run_sims_2_oscillation.py: Run oscillation simulations
• python/run_sims_3_burst_duration.py: Run simulations varying burst duration
• python/run_sims_4_burst_number.py: Run simulations varying burst number
• R/analyze_sims.R: Analyze simulations at -5 dB SNR
• R/analyze_sims_snr.R: Analyze simulations across SNR levels
• python/run_dev_umd.py: Run LHaC on infant and adult EEG data
• python/run_dev_umd_burst_detection.py: Run amplitude thresholding-based burst detection on infant and adult EEG data
• R/analyze_dev_umd_alpha_bursts.R: Analyze duration of alpha bursts in infant and adult EEG data
• python/run_explicit-implicit.py: Run LHaC on adult MEG data
• R/analyze_explicit-implicit.R: Analyze alpha and beta crossover point and decay rate in adult MEG data