TY - JOUR
T1 - Characterization of High-Gamma Activity in Electrocorticographic signals
AU - Gruenwald, Johannes
AU - Sieghartsleitner, Sebastian
AU - Kapeller, Christoph
AU - Scharinger, Josef
AU - Kamada, Kyousuke
AU - Brunner, Peter
AU - Guger, Christoph
N1 - Publisher Copyright:
Copyright © 2023 Gruenwald, Sieghartsleitner, Kapeller, Scharinger, Kamada, Brunner and Guger.
PY - 2023
Y1 - 2023
N2 - Introduction: 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. Methods: 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. Results: The 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. Discussion: This 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.
AB - Introduction: 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. Methods: 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. Results: The 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. Discussion: This 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.
KW - biosignal processing
KW - brain-computer interface
KW - electrocorticography
KW - high-gamma activity
KW - high-gamma frequency band
UR - http://www.scopus.com/inward/record.url?scp=85168340491&partnerID=8YFLogxK
U2 - 10.3389/fnins.2023.1206120
DO - 10.3389/fnins.2023.1206120
M3 - Article
C2 - 37609450
AN - SCOPUS:85168340491
SN - 1662-4548
VL - 17
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 1206120
ER -