Cortical detection of bilateral somatosensory motion neural oscillatory activity with time–frequency multiple sparse priors

Brain's phase-unlocked rhythmic oscillatory activity typically induces synchronized discharges in large-scale neuronal populations. Accurate imaging of oscillatory sources is crucial for brain science and brain disease research. However, traditional brain source imaging methods struggle with the signals for locking phase and time, but handle the time-locked phase-locked signals lacking phase locking with difficulty. To address these challenges, the time–frequency multiple sparse priors Champagne (TF-MSPC) approach is proposed. By leveraging data-driven pre-segmentation (DDP) and incorporating Bayesian learning with a diagonal noise variance structure, TF-MSPC improves coherence matrix estimation accuracy. Additionally, TF-MSPC introduces priors from different modalities through matrix rearrangement encoding, further improving coherence matrix estimation accuracy and constructs a frequency-domain beamformer based on the averaged estimation of cross-trial coherence matrices, thereby overcoming the attenuation of non-phase-locked signals caused by direct time-domain averaging. TF-MSPC outperforms other benchmark algorithms in both numerical simulations and optically pumped magnetometer magnetoencephalography (OPM-MEG)-fMRI bilateral movement experiments. Notably, under low signal-to-noise ratio and multi-source conditions, TF-MSPC effectively reconstructs transient neural activities across various frequency bands and mitigates energy leakage due to correlated sources. These findings underscore the strong potential and practicality of TF-MSPC for reconstructing neural oscillation activities.