AUTHOR=Gruenwald Johannes , Sieghartsleitner Sebastian , Kapeller Christoph , Scharinger Josef , Kamada Kyousuke , Brunner Peter , Guger Christoph 

TITLE=Characterization of High-Gamma Activity in Electrocorticographic Signals

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1206120

DOI=10.3389/fnins.2023.1206120

ISSN=1662-453X

ABSTRACT=Electrocorticographic (ECoG) high-gamma activity (HGA) is a widely recognized and robust neural correlate of cognition and behavior. However, fundamental signal properties of HGA, such as the high-gamma frequency band or temporal dynamics of HGA, have never been systematically characterized. As a result, HGA estimators are often poorly adjusted, such that they miss valuable physiological information.To address these issues, we conducted a thorough qualitative and quantitative characterization of HGA in ECoG signals. Our study is based on ECoG signals recorded from 18 epilepsy patients while performing motor control, listening, and visual perception tasks. In this study, we first categorize HGA into HGA types based on the cognitive/behavioral task. For each HGA type, we then systematically quantify three fundamental signal properties of HGA: the high-gamma frequency band, the HGA bandwidth, and the temporal dynamics of HGA.Our results showed, for the first time, that the high-gamma frequency band strongly varies across subjects and across cognitive/behavioral tasks. Furthermore, we found that the transients of HGA time courses are band-limited to 10 Hz. The task-related rise time and duration of these HGA time courses depend on the individual subject and cognitive/behavioral task. Interestingly, the task-related HGA amplitudes are comparable across the investigated tasks. We provide a concise summary and thorough discussion of these results, which are of high practical relevance for experiment design, ECoG acquisition and processing, and HGA estimation.