Functional cortical localization of tongue movements using corticokinematic coherence with a deep learning-assisted motion capture system

Abstract

Corticokinematic coherence (CKC) between magnetoencephalographic and movement signals using an accelerometer is useful for the functional localization of the primary sensorimotor cortex (SM1). However, it is difficult to determine the tongue CKC because an accelerometer yields excessive magnetic artifacts. Here, we introduce a novel approach for measuring the tongue CKC using a deep learning-assisted motion capture system with videography, and compare it with an accelerometer in a control task measuring finger movement. Twelve healthy volunteers performed rhythmical side-to-side tongue movements in the whole-head magnetoencephalographic system, which were simultaneously recorded using a video camera and examined using a deep learning-assisted motion capture system. In the control task, right finger CKC measurements were simultaneously evaluated via motion capture and an accelerometer. The right finger CKC with motion capture was significant at the movement frequency peaks or its harmonics over the contralateral hemisphere; the motion-captured CKC was 84.9% similar to that with the accelerometer. The tongue CKC was significant at the movement frequency peaks or its harmonics over both hemispheres. The CKC sources of the tongue were considerably lateral and inferior to those of the finger. Thus, the CKC with deep learning-assisted motion capture can evaluate the functional localization of the tongue SM1.

A review of combined functional neuroimaging and motion capture for motor rehabilitation

“…CKC seems to be a robust feature with potential for clinical rehabilitation; however, it was not used in this specific context [ 113 ]. The use of deep-learning-based motion capture to calculate CKC was successfully demonstrated by Maezawa et al [ 114 ] to map cortical areas involved in tongue movement. Another feature that can assist in evaluating somatosensory impairments based on the coherence between EEG and perturbed mechanical movement is position-cortical coherence (PCC).…”

Simões Network 2 (SN2): A special model for special needing

“…Since many decades it was known that the anterior region of the tongue has different receptive fields with a huge density of receptors. However, recent studies in humans showed perfectly how the touch on different regions elicit different muscle activity [9][10][11]. The sagittal part of the dorsal arch touches on the tongue lateral side and the anterior notch touches on the tongue tip, generating activation of styloglossus (as main muscle, considering that all tongue and related muscles will be also activated), having, as a result, tongue transversal size diminishing, dorsal elevation, and up-down movement of the tip [12][13][14].…”

Research on Dance Motion Capture Technology for Visualization Requirements

“…In order to better analyze the motion state of dance performers, a method for analyzing human motion posture using the principle of feature plane similarity matching is proposed. Tis method simplifes the traditional calculation of Euclidean distance based on multiple identifcation points to the calculation of a feature plane feature vector and its included angle [22]. In this paper, the identifcation points of 21 key parts are simplifed into 7 feature planes to calculate the motion diference and correlation.…”