Optimal referencing for stereo-electroencephalographic (SEEG) recordings

Abstract

Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity.

A fast and general method to empirically estimate the complexity of brain responses to transcranial and intracranial stimulations

“…Trials showing pathological activity [38] were detected by visual inspection and excluded from the analysis and SPES-evoked responses were computed by averaging the remaining trials. Data were referenced to a contact located entirely in the white matter, subjected to linear detrend and bandpass filtering (0.5e300 Hz) and bipolar montages were calculated by subtracting the signals from adjacent contacts of the same depth-electrode [39,40]. Stimulation artifact was reduced by applying a Tukey-windowed median filtering [41] between À5 and 5 ms. Data were segmented between À300 and 600 ms and the SEEG magnitude at each electrode was computed as a z-score relative to its baseline.…”

Electrocortical temporal complexity during wakefulness and sleep: an updated account

“…In the present study we show that the loss in temporal complexity during sleep is not a consequence of a common noise entering through our reference electrode. This was evidenced by generating bipolar recordings, thus severely reducing the background noise common to all ECoG electrodes (9)(10)(11)(12). This is particularly relevant because our reference electrode was closely located to the neck muscles and thus could be contaminated by the changes in muscle tone during sleep.…”

Does data cleaning improve brain state classification?

“…Other data-driven approaches such as independent component analysis (ICA) have also been shown to outperform bipolar referencing in simulations involving relatively small numbers of channels (Michelmann et al, 2018), suggesting an extension of our work that would compare bipolar and ICA-based referencing on a large dataset collected from human participants. Other transformations such as the Laplacian (Li et al, 2018) may help eliminate confounding effects of white matter electrodes in other referencing schemes (Mercier et al, 2017), thereby outperforming a standard bipolar montage. Although a comparison between the bipolar approach and these other methods is beyond the scope of this report, future work should focus on comparing these different approaches.…”