Automated spatiotemporal response identification and separation for averaged and single-trial EEG and MEG data

Conventional analysis approaches of evoked EEG and MEG typically rely on assumptions of independence or uncorrelatedness, fixed temporal windows, and predefined regions of interest to extract neural responses. However, cortical activity is spatially and temporally overlapping and interactive, meaning that independence or uncorrelatedness cannot be assumed. As a result, these methods often fail to account for overlapping cortical activity and individual variability, limiting the accuracy and interpretability of the results. To enhance the validity of research and clinical inferences, we aimed to reliably isolate spatiotemporally localized neural evoked responses.

We developed Spatiotemporal Event Response ENcoding ("SEREN"), an algorithm that automatically identifies spatiotemporally localized evoked response components by leveraging the spatiotemporal density properties of post-synaptic currents using Gaussian kernels in time and space. SEREN can extract individual evoked response components for both averaged and single-trial data and operates in both sensor and source space.

We demonstrate SEREN's effectiveness on auditory and visual-evoked MEG data as well as on simulated datasets. Additionally, we show that SEREN can be calibrated for robust single trial monitoring in noisy EEG systems using transcranial magnetic stimulation-evoked potentials, including simulated in real-time applications.

SEREN reliably isolates cortical evoked responses, overcoming limitations of conventional analysis approaches that do not account for inter-response overlaps or individualization.

By improving the precision of neural response extraction, SEREN provides a powerful tool for advancing the analysis of neural dynamics and improving the validity of research and clinical applications.