Danilo Barbosa Melges

Topographic distribution of the tibial somatosensory evoked potential using coherence

The objective of the present study was to determine the adequate cortical regions based on the signal-to-noise ratio (SNR) for somatosensory evoked potential (SEP) recording. This investigation was carried out using magnitude-squared coherence (MSC), a frequency domain objective response detection technique. Electroencephalographic signals were collected (International 10-20 System) from 38 volunteers, without history of neurological pathology, during somatosensory stimulation. Stimuli were applied to the right posterior tibial nerve at the rate of 5 Hz and intensity slightly above the motor threshold. Response detection was based on rejecting the null hypothesis of response absence (significance level alpha= 0.05 and M = 500 epochs). The best detection rates (maximum percentage of volunteers for whom the response was detected for the frequencies between 4.8 and 72 Hz) were obtained for the parietal and central leads mid-sagittal and ipsilateral to the stimulated leg: C4 (87%), P4 (82%), Cz (89%), and Pz (89%). The P37-N45 time-components of the SEP can also be observed in these leads. The other leads, including the central and parietal contralateral and the frontal and fronto-polar leads, presented low detection capacity. If only contralateral leads were considered, the centro-parietal region (C3 and P3) was among the best regions for response detection, presenting a correspondent well-defined N37; however, this was not observed in some volunteers. The results of the present study showed that the central and parietal regions, especially sagittal and ipsilateral to the stimuli, presented the best SNR in the gamma range. Furthermore, these findings suggest that the MSC can be a useful tool for monitoring purposes.

A multiple coherence-based detector for evoked responses in the EEG during sensory stimulation

The sampling distribution of the multiple coherence estimate between a periodic signal and a set of filtered versions of evoked responses embedded in additive noise signals is derived for the zero-coherence case. For a fixed number of signals used in the estimation, the probability density function varies with the number of data segments. Analytical expressions for both bias and variance of the estimate were derived and together with the critical values constitute the statistical apparatus for the detector based on this multiple coherence estimate. An illustration of the technique as applied to detect evoked responses in the Electroencephalogram during sensory stimulation is also provided.

Frequency-Domain Objective Response Detection Techniques Applied to Evoked Potentials: A Review

The Electroencephalogram (EEG) is the biological signal collected at the scalp as a summation result of ionic currents generated from the post-synaptic potentials of the brain neurons. Differently from some other biosignals such as the electrocardiogram, which presents a visual identifiable pattern – particularly the QRS complex, the EEG exhibits a very large random variability. It is indeed quite often assumed to be a white Gaussian noise, and this stochastic behavior turns the analysis of EEG signals by visual inspection a very difficult task. In spite of this, the EEG is known to be correlated with sensorial information processing, and it is widely used for neurophysiologic assessment and neuropathies diagnosis.

Using the Discrete Hilbert Transform for the comparison between Tracé Alternant and High Voltage Slow patterns extracted from full-term neonatal EEG

This work aims at statistically evaluating the differences between two quiet sleep patterns, Trace Alternant (TA) and High Voltage Slow (HVS) of EEG signals collected from derivation F4–P4 of 25 full-term newborns. Firstly, 124 artifact-free segments (30 s duration) during TA and 46 during HVS were selected. Then, each segment was filtered (6th order Butterwoth, zero-phase) in three distinct bands: slow delta (0.25–2 Hz), fast delta (2–4 Hz) and theta (4–8 Hz). By applying the Discrete Hilbert Transform (DHT) to these filtered signals, the envelope (A[n]) and the modulus of the instantaneous frequency (|Fi[n]|) were estimated. From these DHT dynamic spectral parameters, the sample distributions were determined and the medians obtained for each data segment. Based on this procedure, the medianvectors of HVS (mHVS) and of TA (mTA) for each parameter and each frequency band were formed. The time evolution of A[n] and |Fi[n]| for any pattern resembles the same characteristics (amplitude and frequency) of the original EEG signal. Moreover, applying the Wilcoxon rank sum test to mHVS and mTA, showed statistically significant differences (α = 0.05) between TA and HVS for A[n] in the three frequency bands. On the other hand, when this test is applied to |Fi[n]|, only the slow delta band presents significant difference. Hence, these findings could be used for distinguishing between HVS and TA.

Using Objective Response Detection techniques for detecting the tibial somatosensory evoked response with different stimulation rates

This work investigates the influence of the stimulus frequency in the performance of two Objective Response Detection (ORD) techniques, the Magnitude-Squared Coherence (MSC) and the Component Synchrony Measure (CSM), as applied in somatosensory stimulation. Electroencephalographic signals were collected (10-20 International System) from forty adult volunteers without history of neurological pathologies. The stimuli were applied to the right posterior tibial nerve at the frequencies of 2, 5, 7 and 9 Hz and motor threshold intensity level. The detection was based on the rejection of the null hypothesis of response absence (significance level α=0.05 and M=100 and 500 epochs). The performances of the MSC at the four stimulation frequencies were compared, two-by-two, using the Proportion Test applied to the mean percentage rates in the total (2-100 Hz) and optimal (20-60 Hz) bands. The same was proceeded to the CSM. The evaluated derivations were Cz, C4, Pz and P4. No significant difference was found for any studied technique (MSC or CSM), any M-value, at any derivation. Thus, the highest stimulation frequency (9 Hz) can be used in order to obtain a reduction in the time of response detection in an ORD approach for a fixed M-value.

N1 response attenuation and the mismatch negativity (MMN) to within‐ and across‐category phonetic contrasts

According to the neural adaptation model of the mismatch negativity (MMN), the sensitivity of this event-related response to both acoustic and categorical information in speech sounds can be accounted for by assuming that (a) the degree of overlapping between neural representations of two sounds depends on both the acoustic difference between them and whether or not they belong to distinct phonetic categories, and (b) a release from stimulus-specific adaptation causes an enhanced N1 obligatory response to infrequent deviant stimuli. On the basis of this view, we tested in Experiment 1 whether the N1 response to the second sound of a pair (S2 ) would be more attenuated in pairs of identical vowels compared with pairs of different vowels, and in pairs of exemplars of the same vowel category compared with pairs of exemplars of different categories. The psychoacoustic distance between S1 and S2 was the same for all within-category and across-category pairs. While N1 amplitudes decreased markedly from S1 to S2 , responses to S2 were quite similar across pair types, indicating that the attenuation effect in such conditions is not stimulus specific. In Experiment 2, a pronounced MMN was elicited by a deviant vowel sound in an across-category oddball sequence, but not when the exact same deviant vowel was presented in a within-category oddball sequence. This adds evidence that MMN reflects categorical phonetic processing. Taken together, the results suggest that different neural processes underlie the attenuation of the N1 response to S2 and the MMN to vowels.

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.

Topographical distribution of the somatosensory evoked potential: an objective response detection approach

This work aims at investigating the somatosensory evoked potential topographical distribution by applying the Magnitude Squared Coherence (MSC), an Objective Response Detection technique in the frequency domain. The EEG was collected from eight volunteers at derivations according to the 10–20 International System during stimulation of the right posterior tibial nerve. The stimuli were applied at the rate of 5 Hz and with intensity slightly above the motor threshold. Detection was identified based on the null hypothesis of response absence rejection (significance level α = 0.05 and M = 500). The best percentages of detection were achieved in the parietal and central regions ipsilateral to the stimulation limb. C4, P4, Cz and Pz were considered the best derivations for SEP monitoring when monopolar derivations are used.