Advances in human intracranial electroencephalography research, guidelines and good practices.

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.

Electroencephalographic time-frequency patterns of braking and acceleration movement preparation in car driving simulation.

The objective of the present work was to identify electroencephalographic (EEG) components in order to distinguish between braking and accelerating intention in simulated car driving. To do so, we collected high-density EEG data from thirty participants while they were driving in a car simulator. The EEG was separated into independent components that were clustered across participants according to their scalp map topographies. For each component, time-frequency activity related to braking and acceleration events was determined through wavelet analysis, and the cortical generators were estimated through minimum norm source localisation. Comparisons of the time-frequency patterns of power and phase activations revealed that theta power synchronisation distinguishes braking from acceleration events 800 ms before the action and that phase-locked activity increases for braking 800 ms before foot movement in the theta-alpha frequency range. In addition, source reconstruction showed that the dorso-mesial part of the premotor cortex plays a key role in preparation of foot movement. Overall, the results illustrate that dorso-mesial premotor areas are involved in movement preparation while driving, and that low-frequency EEG rhythms could be exploited to predict drivers' intention to brake or accelerate.