Effect of stimulation parameters on intraoperative spinal cord evoked potential monitoring

Single-trial detection for intraoperative somatosensory evoked potentials monitoring

Abnormalities of somatosensory evoked potentials (SEPs) provide effective evidence for impairment of the somatosensory system, so that SEPs have been widely used in both clinical diagnosis and intraoperative neurophysiological monitoring. However, due to their low signal-to-noise ratio (SNR), SEPs are generally measured using ensemble averaging across hundreds of trials, thus unavoidably producing a tardiness of SEPs to the potential damages caused by surgical maneuvers and a loss of dynamical information of cortical processing related to somatosensory inputs. Here, we aimed to enhance the SNR of single-trial SEPs using Kalman filtering and time-frequency multiple linear regression (TF-MLR) and measure their single-trial parameters, both in the time domain and in the time-frequency domain. We first showed that, Kalman filtering and TF-MLR can effectively capture the single-trial SEP responses and provide accurate estimates of single-trial SEP parameters in the time domain and time-frequency domain, respectively. Furthermore, we identified significant correlations between the stimulus intensity and a set of indicative single-trial SEP parameters, including the correlation coefficient (between each single-trial SEPs and their average), P37 amplitude, N45 amplitude, P37-N45 amplitude, and phase value (at the zero-crossing points between P37 and N45). Finally, based on each indicative single-trial SEP parameter, we investigated the minimum number of trials required on a single-trial basis to suggest the existence of SEP responses, thus providing important information for fast SEP extraction in intraoperative monitoring.

An automated and fast approach to detect single-trial visual evoked potentials with application to brain-computer interface

OBJECTIVE

This study aims (1) to develop an automated and fast approach for detecting visual evoked potentials (VEPs) in single trials and (2) to apply the single-trial VEP detection approach in designing a real-time and high-performance brain-computer interface (BCI) system.

METHODS

The single-trial VEP detection approach uses common spatial pattern (CSP) as a spatial filter and wavelet filtering (WF) a temporal-spectral filter to jointly enhance the signal-to-noise ratio (SNR) of single-trial VEPs. The performance of the joint spatial-temporal-spectral filtering approach was assessed in a four-command VEP-based BCI system.

RESULTS

The offline classification accuracy of the BCI system was significantly improved from 67.6±12.5% (raw data) to 97.3±2.1% (data filtered by CSP and WF). The proposed approach was successfully implemented in an online BCI system, where subjects could make 20 decisions in one minute with classification accuracy of 90%.

CONCLUSIONS

The proposed single-trial detection approach is able to obtain robust and reliable VEP waveform in an automatic and fast way and it is applicable in VEP based online BCI systems.

SIGNIFICANCE

This approach provides a real-time and automated solution for single-trial detection of evoked potentials or event-related potentials (EPs/ERPs) in various paradigms, which could benefit many applications such as BCI and intraoperative monitoring.

Signal-to-noise ratio of intraoperative tibial nerve somatosensory-evoked potentials

To reveal the intrinsic signal-to-noise ratio (SNR) of single-trial somatosensory-evoked potentials (SEP). SEP was recorded from 13 scoliosis patients during surgery. The power of SEP was estimated with least-square fitting to obtain the most accurate value and then to estimate the SNR of every trial of SEP. The SNR of cortical SEP from 13 cases presented individual difference among each other. According to the mean and standard deviation, the coefficients of variation of cortical and subcortical SEP were 4.2% and 23%, respectively. The SNR of SEP was estimated to be -24 +/- 1 dB in cortical SEP and -22 +/- 5 dB in subcortical SEP. The lowest SNR of individual case was found to be -30 dB in cortical SEP and -53 dB in subcortical SEP. The results showed that SNR of intraoperative SEP recordings varies from person to person and presents a higher variability in subcortical than that in cortical, with a broad range from -53 to -5 dB. The results from this study can be used to understand the nature of SEP signals, which could guide researchers and designers on SEP denoising method selection, extraction, and measurement, as well as equipment development.