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

ABSTRACT:

Objective

Non-invasive Brain-Computer Interfaces provide accurate classification of hand movement lateralization. However, distinguishing activation patterns of individual fingers within the same hand remains challenging due to their overlapping representations in the motor cortex. Here, we validated a compact convolutional neural network for fast and reliable decoding of finger movements from non-invasive magnetoencephalographic (MEG) recordings.

Approach

We recorded healthy participants in MEG performing a serial reaction time task (SRTT), with buttons pressed by left and right index and middle fingers. We devised classifiers to identify left vs. right hand movements and among four finger movements using a recently proposed decoding approach, Linear Finite Impulse Response

SUBMITTER: Zabolotniy A 

PROVIDER: S-EPMC12454436 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature