Back to Search Start Over

Characterization of High-Gamma Activity in Electrocorticographic Signals

Authors :
Johannes Gruenwald
Sebastian Sieghartsleitner
Christoph Kapeller
Josef Scharinger
Kyousuke Kamada
Peter Brunner
Christoph Guger
Source :
Frontiers in Neuroscience, Vol 17 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

IntroductionElectrocorticographic (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.MethodsTo 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.ResultsThe high-gamma frequency band strongly varies across subjects and across cognitive/behavioral tasks. In addition, HGA time courses have lowpass character, with transients 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. Task-related HGA amplitudes are comparable across the investigated tasks.DiscussionThis study is of high practical relevance because it provides a systematic basis for optimizing experiment design, ECoG acquisition and processing, and HGA estimation. Our results reveal previously unknown characteristics of HGA, the physiological principles of which need to be investigated in further studies.

Details

Language :
English
ISSN :
1662453X
Volume :
17
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neuroscience
Publication Type :
Academic Journal
Accession number :
edsdoj.74cb83c17af04beda15e68d944a145a9
Document Type :
article
Full Text :
https://doi.org/10.3389/fnins.2023.1206120