Recognition of brain activities via graph-based long short-term memory-convolutional neural network.

ABSTRACT:

Introduction

Human brain activities are always difficult to recognize due to its diversity and susceptibility to disturbance. With its unique capability of measuring brain activities, magnetoencephalography (MEG), as a high temporal and spatial resolution neuroimaging technique, has been used to identify multi-task brain activities. Accurately and robustly classifying motor imagery (MI) and cognitive imagery (CI) from MEG signals is a significant challenge in the field of brain-computer interface (BCI).

Methods

In this study, a graph-based long short-term memory-convolutional neural network (GLCNet) is proposed to classify the brain activities in MI and CI tasks. It was characterized by implementing three modules of graph convolutional network (GCN), spatial convolution and

SUBMITTER: Yang Y 

PROVIDER: S-EPMC11973346 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature