Advances in human intracranial electroencephalography research, guidelines and good practices

By M. Mercier, A. Dubarry, François Tadel, P. Avanzini, Nikolai Axmacher, Dillan Cellier, M. D. Vecchio, Liberty S. Hamilton, D. Hermes, Michael J. Kahana and 17 more. NeuroImage, 2022

171 citations recorded by the source corpus.

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual’s tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Subfields: Cognitive Neuroscience, Neurology

View Paper: https://doi.org/10.1016/j.neuroimage.2022.119438

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Citations in the corpus

Temporal filling-in reduces attentional fluctuations in sustained visual attention (2026, unknown): “Moreover, future work may leverage intracranial EEG (iEEG) to acquire cortical SSEPs with simultaneously high temporal and high spatial resolution (Mercier et al., 2022).”
Cortical encoding of probabilistic temporal predictions during speech perception (2026, unknown): “Recordings, interpretation and analysis of sEEG were performed following the French guidelines on stereoelectroencephalography (85) and recommendations on sEEG analysis (86).”
Neural mechanisms of time-forward predictions for naturalistic auditory tone sequences (2026, unknown): “We pooled electrodes across participants for group-level analyses, an approach supported by current iEEG recommendations 47 , as mixed-effects frameworks are not easily applied to the nested and unbalanced structure of iEEG data with variable electrode coverage across subjects.”
Quilting the Brain: Whole-Brain iEEG Reconstruction via Incomplete Observation Linear Mixed Models (2026, unknown): “…or the regular voxel grids of fMRI, iEEG sampling strategies are dictated entirely by clinical needs, typically used for presurgical evaluation in drug-resistant epilepsy patients to de(cid:28)ne the seizure onset zone (SOZ) and functional cortex (Parvizi and Kastner, 2018; Mercier et al., 2022).”
Design hybrid neural network for classifying the electroencephalography of schizophrenia patients (2026, unknown): “These signals have demonstrated efficacy due to their high temporal precision and straightforward setup [ 17, 18].”
Human forebrain neural synchronization and entrainment to breathing during wakefulness, sleep, and external mechanical ventilation (2026, unknown): “…with breathing is consistent with rodent studies 12,40,47,48,51 – 56, Because human iEEG allows for extensive recording across the forebrain 71,72 , we were able to identify distinct sites within known interoceptive brain regions that uniquely synchronized with the breathing rhythm during…”
Impact of Sampling Frequency on Skin Resistance Analysis for Comfort Evaluation in Vehicle Cabins (2026, unknown): “This research confirms that sampling frequency substantially affects the interpretability of EDA signals in vehicle comfort research (Mercier et al., 2022).”
Human intracranial correlates of dynamic coding in auditory working memory (2026, unknown): “Auditory tasks were utilized, not only because there are few neurophysiological studies relative to visual and tactile domains but also because auditory cortex is usually well sampled in sEEG implantations due to clinical considerations (Mercier et al., 2022).”
A consensus roadmap for post‐traumatic epilepsy: Clinical biomarkers, research priorities, policy barriers, and pathways to interventional trials (2026, unknown): “91 (iii) Standardization and har-monization efforts across clinical and preclinical research have the potential to improve translatability across these research domains.”