Power Modulations of ECoG Alpha/Beta and Gamma Bands Correlate With Time-Derivative of Force During Hand Grasp

Abstract

It is well-known that motor cortical oscillatory components are modulated in their amplitude during voluntary and imagined movements. These patterns have been used to develop brain-machine interfaces (BMI) which focused mostly on movement kinematics. In contrast, there have been only a few studies on the relation between brain oscillatory activity and the control of force, in particular, grasping force, which is of primary importance for common daily activities. In this study, we recorded intraoperative high-density electrocorticography (ECoG) from the sensorimotor cortex of four patients while they executed a voluntary isometric hand grasp following verbal instruction. The grasp was held for 2 to 3 s before being instructed to relax. We studied the power modulations of neural oscillations during the whole time-course of the grasp (onset, hold, and offset phases). Phasic event-related desynchronization (ERD) in the low-frequency band (LFB) from 8 to 32 Hz and event-related synchronization (ERS) in the high-frequency band (HFB) from 60 to 200 Hz were observed at grasp onset and offset. However, during the grasp holding period, the magnitude of LFB-ERD and HFB-ERS decreased near or at the baseline level. Overall, LFB-ERD and HFB-ERS show phasic characteristics related to the changes of grasp force (onset/offset) in all four patients. More precisely, the fluctuations of HFB-ERS primarily, and of LFB-ERD to a lesser extent, correlated with the time-course of the first time-derivative of force (yank), rather than with force itself. To the best of our knowledge, this is the first study that establishes such a correlation. These results have fundamental implications for the decoding of grasp in brain oscillatory activity-based neuroprosthetics.

Spontaneous neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

“…Additionally, they are also consistent with numerous studies suggesting a negative relationship between low-frequency power, including the alpha band, and high-frequency power, including BHA, during sensory, motor and task processing. Specifically, increased high-frequency power co-occured with decreased low-frequency power after the presentation of visual (Lachaux et al, 2005;Martin et al, 2019;Nir et al, 2007Nir et al, , 2017Rodriguez et al, 2004;Fisch et al, 2009;Miller et al, 2014;Podvalny et al, 2015;Fries et al, 2001;Scheeringa et al, 2011;Hwang & Andersen, 2011;Rickert et al, 2005;Lundqvist et al, 2020), auditory (de Pesters et al, 2016;Potes et al, 2014), and somatosensory stimuli (Fontolan et al, 2014), and during motor responses (Crone et al, 1998;de Pesters et al, 2016;Jiang et al, 2020;Miller et al, 2007) and task/cognitive processing (Asher et al, 2007;Hwang & Andersen, 2011). Several M/EEG studies observed a negative relationship between low-frequency power decrease and, what is reported as, narrowband gamma increase (Bauer et al, 2006;Kloosterman et al, 2019;van Ede et al, 2014;Wyart & Tallon-Baudry, 2009); though it remains unclear whether this high-frequency signal reflects genuine oscillatory activity or broadband power.…”

Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)—A Novel ICA-Based Algorithm for Removing Myoelectric Artifacts From EEG

“…Therefore, any attenuation of the high-frequency band in EEG can be considered as removal of EMG artifacts. Also, μ desynchronization in EEG and ECoG signals is a well-known modulation underlying motor behavior (Pfurtscheller and Da Silva, 1999 ; Miller et al, 2007 ; Schalk et al, 2007 ; Jiang et al, 2020 ). Therefore, these two bands are used jointly as an indicator that our approach is effective at removing EMG artifacts while preserving the brain physiological features underlying human movement.…”

Refinement of High-Gamma EEG Features From TBI Patients With Hemicraniectomy Using an ICA Informed by Simulated Myoelectric Artifacts

“…In previous studies, high-γ band of brain activities was demonstrated to synchronize to movement (Flint et al, 2014(Flint et al, , 2016Wang et al, 2017;McCrimmon et al, 2018;Branco et al, 2019) while µ band was desynchronized (Miller et al, 2007a;Schalk et al, 2007;Jiang et al, 2020). To evaluate if these electrophysiological features and information content underlying movement were retained after ERASE, the signal-to-noise ratio (SNR) between movement and idle was calculated for electrodes expected to be related to thumb movements, including C3, C5, C1, FCC5h, FCC3h, CCP5h, and CCP3h for subjects with left-sided hemicraniectomy, or C4, C2, C6, FCC6h, FCC4h, CCP4h, and CCP6h for those with right-sided hemicraniectomy.…”