A comparison of methods for separation of transient and oscillatory signals in EEG

Jmail, Nawel; Gavaret, Martine; Wendling, Fabrice; Kachouri, Abdennaceur; Ghariani, Hamadi; Badier, Jean‐Michel; Bénar, Christian‐George

doi:10.1016/j.jneumeth.2011.04.028

Cited by 38 publications

(40 citation statements)

References 37 publications

(32 reference statements)

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“…Another promising venue is to investigate high frequency oscillations (Epstein et al , 2014). Such high frequencies have been shown to be a good marker of the epileptogenic zone (Urrestarazu et al , 2007), but require careful handling of the frequency overlap between epileptic spikes and oscillations (Bénar et al , 2010, Jmail et al , 2011. Further work is needed to test the impact of these methodological choices.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy

et al. 2016

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The reference electrophysiological pattern at seizure onset is the "rapid discharge," as visible on intracerebral electroencephalography (EEG). This discharge typically corresponds to a decrease of synchrony across brain areas. In contrast, the preictal period can exhibit patterns of increased synchrony, which can be quantified by network measures. Our objective was to compare preictal synchrony with a quantification of the rapid discharge as provided by the epileptogenicity index (EI). We investigated 24 seizures from 12 patients recorded by stereotaxic EEG (SEEG). Seizures were classified visually as containing preictal synchrony or not. We computed pairwise nonlinear correlation (h(2)) across channels in the 8 sec preceding the rapid discharge. The sum of ingoing and outgoing links (IN and OUT node strength), as well as the sum of all links (total strength, TOT) were computed for each region. We tested several filtering schemes, and quantified the capacity of each strength measure to serve as a detector of regions with high EI values using a receiver operating characteristic (ROC) analysis. We found that the best correspondence between node strength and EI was obtained for the OUT and TOT measures, for signals filtered in the 15-40 Hz band-that is, for the band corresponding to the spiky part of epileptic discharges. In agreement with these results, we also found that the ROC results were improved when considering only seizures with visible synchronous patterns in the preictal period. Our results suggest that measuring strength of preictal connectivity graphs can bring useful clinical information on the epileptogenic zone.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy

et al. 2016

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“…First, the decomposition is adapted to the dataset and hence only extracts features representing common waveforms reflecting pain-specific information [13]. Secondly, the decomposition does not require any a priori choice of the best matched filter for decomposition into a filter bank by either a finite impulse response filter or wavelet transform [32]. Third, the decomposition into atoms described in both time and frequency bridges the gap between feature extraction based on a pure frequency analysis, where the time information is discarded, and feature extraction based on time-frequency coefficients, where minor fluctuations in peak latencies may influence the classification accuracy due to phase variability between the sweeps.…”

Section: Discussionmentioning

confidence: 99%

Multivariate Analysis of Single-Sweep Evoked Brain Potentials for Pharmaco-Electroencephalography

Graversen¹,

Olesen²,

Staahl³

et al. 2015

Neuropsychobiology

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Background and Aims: Current findings on altered evoked potentials (EPs) caused by morphine are based on common alterations for a group of subjects after drug administration. However, this ignores the analysis of individual responses, which may explain the clinical differences in efficacy. Therefore, we explored the individual responses to morphine in terms of the altered single-sweep characteristics in a placebo-controlled crossover study. To account for multifactorial mechanisms, several characteristics were assessed simultaneously by multivariate pattern analysis (MVPA). Methods: EPs were recorded from 62 channels and obtained before and after morphine and placebo administration during repeated electrical stimulations of the oesophagus in 12 healthy males. Additionally, the pain detection threshold was recorded to reflect the subjective analgesic effect in each subject. The characteristics of the sweeps were extracted by a multivariate matching pursuit algorithm with Gabor atoms implemented with a variable amplitude and constant phase across the sweeps. The single-sweep amplitudes were used as input to an MVPA algorithm to discriminate individual responses. The accuracy of the MVPA for each individual subject was used for correlation analysis of the analgesic effect. Results: The mean classification accuracy when discriminating pre- and posttreatment morphine responses was 72% (p = 0.01). The individual classification accuracy was positively correlated to the analgesic effect of morphine (p = 0.03). Furthermore, the 2 posttreatment responses were classified and validated by the classification of the 2 pretreatment responses (p = 0.001). Conclusions: The alterations in the single-sweep EPs after morphine reflect the analgesic effect. The MVPA approach is a novel methodology for monitoring the individual efficacy of analgesics.

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“…We selected oscillations that were temporally separated from spikes in order to avoid spurious oscillations by filtering transient signals (Bénar et al 2010). Sections of 300 ms were created around each spike or oscillation (Jmail et al 2011). Oscillations in the 15-45 Hz band were marked on MEG and IEEG by visual inspection.…”

Section: Detection Of Patternsmentioning

confidence: 99%

“…We concatenated separately the time windows containing spikes and oscillations and applied FIR filtering on the (Jmail et al 2011). For spikes, a band-pass filter was applied between 10 and 45 Hz in order to eliminate the following slow component.…”

Section: Band Pass Filteringmentioning

confidence: 99%

“…This could be due to the fact that we considered oscillations away from spikes, in order to avoid mixture of the two types of activity (Bénar et al 2010), and such oscillations are more difficult to label as purely epileptiform. Further work is needed that will be based on techniques for disentangling spikes and oscillations overlapping in time (Jmail et al 2011).…”

Section: Comparison With Sozmentioning

confidence: 99%

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Comparison of Brain Networks During Interictal Oscillations and Spikes on Magnetoencephalography and Intracerebral EEG

et al. 2016

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Electromagnetic source localization in electroencephalography (EEG) and magnetoencephalography (MEG) allows finding the generators of transient interictal epileptiform discharges ('interictal spikes'). In intracerebral EEG (iEEG), oscillatory activity (above 30 Hz) has also been shown to be a marker of neuronal dysfunction. Still, the difference between networks involved in transient and oscillatory activities remains largely unknown. Our goal was thus to extract and compare the networks involved in interictal oscillations and spikes, and to compare the non-invasive results to those obtained directly within the brain. In five patients with both MEG and iEEG recordings, we computed correlation graphs across regions, for (1) interictal spikes and (2) epileptic oscillations around 30 Hz. We show that the corresponding networks can involve a widespread set of regions (average of 10 per patient), with only partial overlap (38 % of the total number of regions in MEG, 50 % in iEEG). The non-invasive results were concordant with intracerebral recordings (79 % for the spikes and 50 % for the oscillations). We compared our interictal results to iEEG ictal data. The regions labeled as seizure onset zone (SOZ) belonged to interictal networks in a large proportion of cases: 75 % (resp. 58 %) for spikes and 58 % (resp. 33 %) for oscillations in iEEG (resp. MEG). A subset of SOZ regions were detected by one type of discharges but not the other (25 % for spikes and 8 % for oscillations). Our study suggests that spike and oscillatory activities involve overlapping but distinct networks, and are complementary for presurgical mapping.

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A comparison of methods for separation of transient and oscillatory signals in EEG

Cited by 38 publications

References 37 publications

Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy

Graph Measures of Node Strength for Characterizing Preictal Synchrony in Partial Epilepsy

Multivariate Analysis of Single-Sweep Evoked Brain Potentials for Pharmaco-Electroencephalography

Comparison of Brain Networks During Interictal Oscillations and Spikes on Magnetoencephalography and Intracerebral EEG

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