ECoG Beta Suppression and Modulation During Finger Extension and Flexion

Julian Unterweger,Jeffrey G Ojemann,Stavros Zanos,Martin Seeber,Reinhold Scherer

doi:10.3389/fnins.2020.00035

Abstract

Neural oscillations originate predominantly from interacting cortical neurons and consequently reflect aspects of cortical information processing. However, their functional role is not yet fully understood and their interpretation is debatable. Amplitude modulations (AMs) in alpha (8–12 Hz), beta (13–30 Hz), and high gamma (70–150 Hz) band in invasive electrocorticogram (ECoG) and non-invasive electroencephalogram (EEG) signals change with behavior. Alpha and beta band AMs are typically suppressed (desynchronized) during motor behavior, while high gamma AMs highly correlate with the behavior. These two phenomena are successfully used for functional brain mapping and brain-computer interface (BCI) applications. Recent research found movement-phase related AMs (MPA) also in high beta/low gamma (24–40 Hz) EEG rhythms. These MPAs were found by separating the suppressed AMs into sustained and dynamic components. Sustained AM components are those with frequencies that are lower than the motor behavior. Dynamic components those with frequencies higher than the behavior. In this paper, we study ECoG beta/low gamma band (12–30 Hz/30–42 Hz) AM during repetitive finger movements addressing the question whether or not MPAs can be found in ECoG beta band. Indeed, MPA in the 12–18 Hz and 18–24 Hz band were found. This additional information may lead to further improvements in ECoG-based prediction and reconstruction of motor behavior by combining high gamma AM and beta band MPA.

Highlights

Functional brain mapping and brain-computer interface (BCI) technologies identify behavior—cognitive and motor—by interpretation of brain signal patterns
ECoG Beta Modulations online BCI performance. γ H can be found in the noninvasive electroencephalogram (EEG) (Ball et al, 2008; Darvas et al, 2010; Grosse-Wentrup et al, 2011; Seeber et al, 2015); the single-trial signal-to-noise ratio (SNR) is low in non-invasive EEG
To improve Functional brain mapping (fBM)/BCI performance, it is essential to deepen our understanding of signals recorded as local field potentials (LFP), ECoG, and EEG

Summary

Introduction

Functional brain mapping (fBM) and brain-computer interface (BCI) technologies identify behavior—cognitive and motor—by interpretation of brain signal patterns. The single-trial signal-to-noise ratio (SNR) of γ H is high, which is essential for robust and timely. Oscillations over sensorimotor areas in the μ (8–12 Hz) and β (13–30 Hz) frequency range are much more pronounced in EEG recordings on a single-trial level. Sensorimotor μ and β band suppression during motor behavior is characteristic for ECoG. Since these patterns are well described in the literature, they are commonly used in BCI. To improve fBM/BCI performance, it is essential to deepen our understanding of signals recorded as local field potentials (LFP), ECoG, and EEG

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