Evidence map›Paper›PMID 37609450›Full record

ArticleFrontiers in neuroscience2023

Characterization of High-Gamma Activity in Electrocorticographic Signals.

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

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Guideline
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  5. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 3 countries.

Johannes Gruenwaldg.tec medical engineering GmbH, Schiedlberg, Austria.
Sebastian Sieghartsleitnerg.tec medical engineering GmbH, Schiedlberg, Austria.
Christoph Kapellerg.tec medical engineering GmbH, Schiedlberg, Austria.
Josef ScharingerInstitute of Computational Perception, Johannes Kepler University, Linz, Austria.
Kyousuke KamadaDepartment for Neurosurgery, Asahikawa Medical University, Asahikawa, Japan.
Peter BrunnerNational Center for Adaptive Neurotechnologies, Albany, NY, United States.
Christoph Gugerg.tec medical engineering GmbH, Schiedlberg, Austria.
Guger Technologies (Austria) · ATAdaptive Biotechnologies (United States) · USAsahikawa Medical University · JPJohannes Kepler University of Linz · AT

Funding

Training and DisseminationP41EB018783 · NIBIB · WADSWORTH CENTER · PI Theresa M. Vaughan · 2014 to 2026
$15.4M
An Ecosystem of Technology and Protocols for Adaptive Neuromodulation Research in HumansU01NS128612 · NINDS · MAYO CLINIC ROCHESTER · PI Peter Brunner, Kai Miller · 2022 to 2026
$4.5M
BCI2000: Software Resource for Adaptive Neurotechnology ResearchU24NS109103 · NINDS · WASHINGTON UNIVERSITY · PI BRUNNER, PETER · 2019 to 2024
$2.8M
BCI2000+: A Software Platform for Adaptive NeurotechnologiesR01EB026439 · NIBIB · WASHINGTON UNIVERSITY · PI BRUNNER, PETER · 2018 to 2022
$1.5M
Dynamics and Causal Functions of Large-Scale Cortical and Subcortical NetworksU01NS108916 · NINDS · WADSWORTH CENTER · PI BRUNNER, PETER, WOLPAW, JONATHAN RICKEL · 2018 to 2019
$1.4M
NIBIB NIH HHS P41 EB018783NIBIB NIH HHS R01 EB026439NINDS NIH HHS U01 NS108916NINDS NIH HHS U24 NS109103
6 · The paper itself

Abstract

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.

Indexed as

biosignal processingbrain-computer interfaceelectrocorticographyhigh-gamma activityhigh-gamma frequency band

Identifiers

PMID37609450
PMCPMC10440607
OpenAlexW4385617493

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.