A novel tool for the removal of muscle artefacts from EEG: Improving data quality in the gamma frequency range

Liebisch, Alina Pauline; Eggert, Thomas; Shindy, Alina; Valentini, Elia; Irving, Stephanie; Stankewitz, Anne; Schulz, Enrico

doi:10.1016/j.jneumeth.2021.109217

Cited by 8 publications

(8 citation statements)

References 39 publications

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“…ICA data were then retransformed to EEG. A second ICA was performed and muscle artefacts were removed from the data ( Liebisch et al, 2021 ). The number of components was automatically adjusted by the software according to the removed components in the previous step.…”

Section: Methodsmentioning

confidence: 99%

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Investigation on how dynamic effective connectivity patterns encode the fluctuating pain intensity in chronic migraine

Bassez

Steen

Hackl

et al. 2022

Neurobiology of Pain

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Section: Methodsmentioning

confidence: 99%

“…Finally, the data were downsampled to 256 Hz and re-referenced to the common average reference. Please note our previous publication for more details on the rationale for these steps ( Liebisch et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Investigation on how dynamic effective connectivity patterns encode the fluctuating pain intensity in chronic migraine

Bassez

Steen

Hackl

et al. 2022

Neurobiology of Pain

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“…On the component data, 50 Hz power line noise was removed using CleanLine (Mullen, 2012) and artefactual components reflecting eye movements and other larger artefacts were removed from the data. A second ICA was performed and muscle artefacts were removed from the data (Liebisch et al, 2021). A third ICA was utilised to remove components with residual artefacts and spectrum interpolation (Leske & Dalal, 2019) eliminated residual power line noise.…”

Section: Pre-processingmentioning

confidence: 99%

Investigation on How Dynamic Effective Connectivity Patterns Encode the Fluctuating Pain Intensity in Chronic Migraine

Bassez

Steen

Hackl

et al. 2022

Preprint

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Chronic migraine is characterised by persistent headaches for more than 15 days per month; the intensity of the pain is fluctuating over time. Here, we explored the dynamic interplay of connectivity patterns between regions known to be related to pain processing and their relation to the ongoing dynamic pain experience. We recorded EEG from 80 sessions (20 chronic migraine patients in 4 separate sessions of 25 minutes). The patients were asked to continuously rate the intensity of their endogenous headache. On different time-windows, a dynamic causal model (DCM) of cross spectral responses was inverted to estimate connectivity strengths. For each patient and session, the evolving dynamics of effective connectivity were related to pain intensities and to pain intensity changes by using a Bayesian linear model. Hierarchical Bayesian modelling was further used to examine which connectivity-pain relations are consistent across sessions and across patients. The results reflect the multi-facetted clinical picture of the disease. Across all sessions, each patient with chronic migraine exhibited a distinct pattern of pain intensity-related cortical connectivity. The diversity of the individual findings are accompanied by inconsistent relations between the connectivity parameters and pain intensity or pain intensity changes at group level. This suggests a rejection of the idea of a common neuronal core problem for chronic migraine.

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“…Consequently, studies that do not sufficiently clear the data of artifacts will inevitably misinterpret high-frequency artifacts as cortical signals ( 24 ) or vice versa ( 20 , 27 ). This issue can be mitigated with the application of algorithms for the identification and removal of high-frequency EEG features of extracortical origin ( 26 , 28 , 29 ). As many of these approaches are using source separation methods ( 25 , 26 , 28 ), the use of a multielectrode array is imperative to provide sufficient independent input.…”

Section: Introductionmentioning

confidence: 99%

“…This issue can be mitigated with the application of algorithms for the identification and removal of high-frequency EEG features of extracortical origin ( 26 , 28 , 29 ). As many of these approaches are using source separation methods ( 25 , 26 , 28 ), the use of a multielectrode array is imperative to provide sufficient independent input. In addition, high-density coverage of the scalp helps to assess the plausibility of the neuronal effect by evaluating gamma topographies ( 22 ).…”

Section: Introductionmentioning

confidence: 99%

Interindividual variability and individual stability of pain- and touch-related neuronal gamma oscillations

Valentini

Shindy

Witkovský

et al. 2023

Journal of Neurophysiology

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Background. Brief painful laser and innocuous tactile stimuli have been associated with an increase of neuronal oscillations in the gamma range. However, it is indicated that event-related gamma oscillations may be highly variable across individuals, but no study has systematically studied interindividual variability and individual stability of induced gamma synchronisation. Here, we addressed this question using two datasets. Methods. The present study includes two EEG datasets. The first dataset contains two repeated sessions of tactile and painful stimulation from 22 participants. The second dataset contains a single session of painful stimulation from 48 participants. Results. In the first dataset, we observed gamma responses in the majority of the included participants. We found a broad interindividual variety of gamma magnitudes, time-frequency response patterns, and scalp topographies. Some participants showed a gamma response with individually unique time-frequency patterns, others did not exhibit any gamma response. This was reproducible and therefore stable; subjects with a large gamma magnitude in the first session showed a large gamma magnitude and a similar response pattern in the follow-up session. The second dataset confirmed the large between-subject variability, but only a fraction of the included participants exhibited laser-induced gamma synchronisation. Conclusions. Our results indicate that current EEG measures do not reflect the complex reality of the diverse individual response patterns to brief pain and touch experiences. The present findings question whether a similar phenomenon would be observed in other neuroscience domains. Group results may be replicable, but could be driven by a subgroup of the sample.

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A novel tool for the removal of muscle artefacts from EEG: Improving data quality in the gamma frequency range

Cited by 8 publications

References 39 publications

Investigation on how dynamic effective connectivity patterns encode the fluctuating pain intensity in chronic migraine

Investigation on how dynamic effective connectivity patterns encode the fluctuating pain intensity in chronic migraine

Investigation on How Dynamic Effective Connectivity Patterns Encode the Fluctuating Pain Intensity in Chronic Migraine

Interindividual variability and individual stability of pain- and touch-related neuronal gamma oscillations

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