Single-Trial Extraction of Pure Somatosensory Evoked Potential Based on Expectation Maximization Approach

It is of great importance for intraoperative monitoring to accurately extract somatosensory evoked potentials (SEPs) and track its changes fast. Currently, multi-trial averaging is widely adopted for SEP signal extraction. However, because of the loss of variations related to SEP features across different trials, the estimated SEPs in such a way are not suitable for the purpose of real-time monitoring of every single trial of SEP. In order to handle this issue, a number of single-trial SEP extraction approaches have been developed in the literature, such as ARX and SOBI, but most of them have their performance limited due to not sufficient utilization of multi-trial and multi-condition structures of the signals. In this paper, a novel Bayesian model of SEP signals is proposed to make systemic use of multi-trial and multi-condition priors and other structural information in the signal by integrating both a cortical source propagation model and a SEP basis components model, and an Expectation Maximization (EM) algorithm is developed for single-trial SEP estimation under this model. Numerical simulations demonstrate that the developed method can provide reasonably good single-trial estimations of SEP as long as signal-to-noise ratio (SNR) of the measurements is no worse than $-25~{\rm dB}$. The effectiveness of the proposed method is further verified by its application to real SEP measurements of a number of different subjects during spinal surgeries. It is observed that using the proposed approach the main SEP features (i.e., latencies) can be reliably estimated at single-trial basis, and thus the variation of latencies in different trials can be traced, which provides a solid support for surgical intraoperative monitoring.