title: Single-Trial Extraction of Pure Somatosensory Evoked Potential Based on Expectation Maximization Approach.
abstract: 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 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.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.forename: Chunqi
authors.initials: C
authors.lastname: Chang
authors.forename: Yong
authors.initials: Y
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D064795:Intraoperative Neurophysiological Monitoring / Q000379:methods; D016013:Likelihood Functions; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011930:Reaction Time / Q000502:physiology; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio