Source localization of simulated neural signals in a cervical spinal cord model

Abstract Objective. The spinal cord is a vital part of the central nervous system, and its neural signals offer valuable insight into sensory and motor function. Accurate localization of the neural sources that generate spinal cord potentials (SCPs) is essential for advancing both basic research and clinical applications. This study aims to assess the feasibility of applying established EEG source localization methods to simulated SCP data. Approach. We constructed a biophysical model of the upper body and head to simulate surface potentials generated by dipolar sources within the gray matter of the cervical spinal cord. Electrodes were distributed around the neck and upper back to capture these signals. Inverse solutions were obtained using established source localization methods, including sLORETA, and performance was evaluated across varying signal-to-noise ratios (SNRs), electrode layouts, and anatomical model variants. Main results. Regularization parameters between 1 × 10 − 4 and 1 × 10 − 1 yielded the lowest errors, depending on SNR. Under these conditions, predicted source locations were typically within 10 mm of the true source. Higher SNR levels favored larger regularization values. Localization accuracy improved with increasing electrode density, though performance gains plateaued beyond approximately 50% coverage of the neck circumference. Significance. These results demonstrate that established source localization methods can be adapted for spinal cord applications in simulation. The findings highlight the importance of both regularization and sensor configuration, providing a foundation for future improvements in inverse modeling and experimental validation with real SCP recordings.