Non-magnetic compliant finger sensor for continuous fine motor movement detection

Area of Science:

• Neuroscience
• Biomedical Engineering
• Sensor Technology

Background:

• Magnetoencephalography (MEG) is a non-invasive neuroimaging technique.
• Characterizing sensorimotor functions requires precise measurement of force and velocity.
• Existing methods may lack compatibility with MEG or detailed finger-specific data.

Purpose of the Study:

• To develop and validate a non-magnetic finger sensor for simultaneous use with MEG.
• To assess the device's ability to capture continuous force and velocity data.
• To investigate brain activity related to individual finger movements using MEG.

Main Methods:

• A prototype non-magnetic finger sensor was developed.
• 15 healthy participants performed cued finger movements during 151-channel MEG.
• Force/velocity data were fed into Analog to Digital Converter (ADC) channels for analysis.
• Source activity in the beta band was reconstructed using a Linearly Constrained Minimum Variance (LCMV) beamformer.
• A continuous time General Linear Model (GLM) with ADC channels as regressors identified active regions of interest (ROIs).

Main Results:

• The finger sensor was magnetically compatible with MEG.
• MEG analysis revealed bilateral activation in the primary motor cortex.
• Somatotopy of individual fingers was observed, consistent with the motor homunculus (except for the pinky finger).
• The device successfully provided continuous force and velocity information.

Conclusions:

• The developed finger sensor is suitable for MEG-based studies of sensorimotor function.
• The system allows for detailed characterization of brain regions involved in force and velocity control.
• Future improvements with machine learning could enhance accuracy and individual digit isolation.