Characterization of High-Gamma Activity in Electrocorticographic Signals

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.

Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials

“…A target lead of 125 ms was also introduced (Benabid et al, 2019). It is common practice to predict the target a few decoder update steps (in this case, two update steps) in advance to compensate for potential latencies introduced by processing, communication or even stimulator ramp-up (Tarvainen et al, 2004;Gruenwald et al, 2017;Benabid et al, 2019). Finally, 10% of the length of each recording was set aside for testing after tuning.…”

“…Filter banks have been proposed as a computationally efficient solution to extract standard electrophysiological sub-bands (Zhu et al, 2020). The method proposed by Gruenwald et al (2017) was followed to construct a bank of fourth-order IIR Butterworth filters. Flow diagram summarizing the sequence of processes for decoding and the different parameters assigned to them.…”

“…] top ranked channels were selected. Features of the selected channels were further processed by applying a logarithm transform, normalization, and further smoothing using the featurewise Kalman filter proposed by Gruenwald et al (2017) (except for the ARMA extractor, where it could be adjusted through λ). The…”