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Related Experiment Video

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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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Convolutional neural networks decode finger movements in motor sequence learning from MEG data.

Aleksey Zabolotniy¹, Russell Weili Chan², Victoria Moiseeva¹

¹Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia.

Frontiers in Neuroscience

|September 25, 2025

Summary

Decoding individual finger movements using non-invasive magnetoencephalography (MEG) is now feasible. A compact convolutional neural network (CNN) offers fast, reliable finger movement classification from MEG, outperforming complex models.

Keywords:

MEG convolutional neural networks finger movement decoding motor learning serial reaction time task

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Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Non-invasive Brain-Computer Interfaces (BCIs) accurately classify hand lateralization.
Distinguishing individual finger movements is challenging due to overlapping motor cortex representations.

Purpose of the Study:

Validate a compact convolutional neural network (CNN) for decoding finger movements from magnetoencephalography (MEG).
Assess the performance and interpretability of the Linear Finite Impulse Response Convolutional Neural Network (LF-CNN) against other deep learning models.

Main Methods:

Recorded MEG data from participants performing a serial reaction time task (SRTT) involving index and middle finger presses.
Developed and compared LF-CNN with EEGNet, FBCSP-ShallowNet, and VGG19 for classifying hand and finger movements.

Main Results:

All models achieved >95% accuracy for hand laterality decoding.
Individual finger movement decoding accuracy ranged from 80-85%.
LF-CNN demonstrated superior computational efficiency and interpretability in spatial and spectral domains.

Conclusions:

A tailored CNN (LF-CNN) enables feasible and accurate decoding of individual finger movements from non-invasive MEG.
LF-CNN offers comparable performance to complex architectures with faster, interpretable results.
This approach has significant potential for investigating neural mechanisms in cognitive neuroscience.