Non-magnetic compliant finger sensor for continuous fine motor movement detection.
10.1007/s13534-017-0031-6
- Author:
Anterpal SANDHU
1
;
Yasong LI
;
Nicholas PEATFIELD
;
Xin Yi YONG
;
Ryan D'ARCY
;
Carlo MENON
;
Teresa P L CHEUNG
Author Information
1. Faculty of Applied Sciences, Simon Fraser University, Burnaby, Canada. tpc5@sfu.ca
- Publication Type:Original Article
- Keywords:
MEG compatible;
Somatotopy;
Sensorimotor;
Force regressed contrast;
Real-time motion data
- MeSH:
Brain;
Cues;
Fingers*;
Linear Models;
Machine Learning;
Motor Cortex
- From:
Biomedical Engineering Letters
2017;7(3):215-219
- CountryRepublic of Korea
- Language:English
-
Abstract:
A non-magnetic MEG compatible device has been developed that provides continuous force and velocity information. Combined with MEG, this device may find utility in characterizing brain regions associated with force and velocity relative to individual digits or movement pattern. 15 healthy right-handed participants were given visual cues to perform random finger movements on the prototype finger sensor for 21 s and then rest for 21 s (7 times). Respective finger flexion data were obtained, during 151-channel MEG brain scanning, by feeding the signal from finger sensor into four input Analog to Digital Converter (ADC) channels in the MEG hardware. The source activity was reconstructed in beta band using a Linearly Constrained Minimum Variance (LCMV) beamformer in the beta band. The ADC channels were used as regressors for a continuous time General Linear Model (GLM) and a Region of Interest (ROI) was identified to examine activity. MEG analysis showed bilateral activation in the primary motor cortex region. Because individual digits could be isolated in the ADC data, somatotopy of the fingers were observed consistent with the homunculus except pinky finger. The total span was calculated to be 5.5662 mm. The study confirms that the finger sensor is magnetically compatible with MEG measurements and may potentially provide a means to study complex sensorimotor functions. Improved isolation of individual digit information along with the use of machine learning algorithms can help retrieve more accurate results.
