Topographic distribution of the tibial somatosensory evoked potential using coherence

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 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.

A spatial approach of magnitude-squared coherence applied to selective attention detection

BACKGROUND

Auditory selective attention is the human ability of actively focusing in a certain sound stimulus while avoiding all other ones. This ability can be used, for example, in behavioral studies and brain-machine interface.

NEW METHOD

In this work we developed an objective method - called Spatial Coherence - to detect the side where a subject is focusing attention to. This method takes into consideration the Magnitude Squared Coherence and the topographic distribution of responses among electroencephalogram electrodes. The individuals were stimulated with amplitude-modulated tones binaurally and were oriented to focus attention to only one of the stimuli.

RESULTS

The results indicate a contralateral modulation of ASSR in the attention condition and are in agreement with prior studies. Furthermore, the best combination of electrodes led to a hit rate of 82% for 5.03 commands per minute.

COMPARISON WITH EXISTING METHOD(S)

Using a similar paradigm, in a recent work, a maximum hit rate of 84.33% was achieved, but with a greater a classification time (20s, i.e. 3 commands per minute).

CONCLUSIONS

It seems that Spatial Coherence is a useful technique for detecting focus of auditory selective attention.

Multiple Coherence vs Multiple Component Synchrony Measure for somatosensory evoked response detection

This work aims at comparing the performance of two Multivariate Objective Response Detection (MORD) techniques in the frequency domain, the Multiple Coherence (MC) and the Multiple Component Synchrony Measure (MCSM), for tibial nerve somatosensory evoked potential (SEP) detection. Electroencephalographic (EEG) signals during somatosensory stimulation were collected from forty adult volunteers using the 10-20 International System. The stimulation was carried out throughout current pulses (200 µs width) applied to the right posterior tibial nerve (motor threshold intensity level) at the rate of 5 Hz. The response detection was based on rejecting the null hypothesis of response absence (M = 100 and M = 800 epochs and significance level α = 0.05). The MORD techniques were applied to the pairs of derivations [Cz][Fz] and [C3][C4]. The MC outperforms the MCSM, regardless the pair of derivations or the number of epochs used for the estimates calculation. Hence, the MC should be used, if two derivations are available for SEP recording.