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

Brief painful laser and innocuous tactile stimuli have been associated with an increase of neuronal oscillations in the gamma range. Although it is indicated that event-related gamma oscillations may be highly variable across individuals, to date no study has systematically investigated interindividual variability and individual stability of induced gamma synchronization. Here, we addressed this question using 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. 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 (TF) 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 synchronization. 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.NEW & NOTEWORTHY The interpretation of gamma activity in response to noxious and innocuous somatosensory stimuli has sparked controversy. Here, we show that participants' gamma oscillations measured through electroencephalography vary. Although some participants do not show a distinct gamma response, others exhibit stable and reliable response patterns in terms of time, frequency, and magnitude.

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

BACKGROUND: The past two decades have seen a particular focus towards high-frequency neural activity in the gamma band (>30 Hz). However, gamma band activity shares frequency range with unwanted artefacts from muscular activity. NEW METHOD: We developed a novel approach to remove muscle artefacts from neurophysiological data. We re-analysed existing EEG data that were decomposed by a blind source separation method (independent component analysis, ICA), which helped to better spatially and temporally separate single muscle spikes. We then applied an adapting algorithm that detects these singled-out muscle spikes. RESULTS: We obtained data almost free from muscle artefacts; we needed to remove significantly fewer artefact components from the ICA and we included more trials for the statistical analysis compared to standard ICA artefact removal. All pain-related cortical effects in the gamma band have been preserved, which underlines the high efficacy and precision of this algorithm. CONCLUSIONS: Our results show a significant improvement of data quality by preserving task-relevant gamma oscillations of presumed cortical origin. We were able to precisely detect, gauge, and carve out single muscle spikes from the time course of neurophysiological measures without perturbing cortical gamma. We advocate the application of the tool for studies investigating gamma activity that contain a rather low number of trials, as well as for data that are highly contaminated with muscle artefacts. This validation of our tool allows for the application on event-free continuous EEG, for which the artefact removal is more challenging.