Work

A Self-Attention-Based Model for Automatic Magnetoencephalography Signal Reconstruction

Year: 2025

Type: conference-paper

Abstract: Magnetoencephalography (MEG) is a non-invasive neuroimaging technique with high spatiotemporal resolution, widely utilized in neuroscience and clinical research. However, noise interference and physiological activities often result in bad channels and segments of MEG data, adversely affecting subsequent analysis. Most common methods directly reject or interpolate to repair these channels and segments, which lead to data loss or significant signal distortion. Here, we propose a self-attention-based MEG reconstruction model capable of reconstructing signals in corrupted channels and segments that closely match the real data in both time and frequency domains, while enhancing the data quality without the need for manual feature design. Even in the presence of numerous bad channels and segments, our model consistently achieves high-quality signal reconstruction. Overall, we introduce a novel deep learning strategy for the automatic repair of MEG signals, demonstrating the tremendous potential of artificial intelligence in improving neuroimaging data analysis.

DOI: 10.1145/3733006.3733018

Authors: Jie Liang, Bingjiang Lyu, Jia-Hong Gao

Institutions: University of Science and Technology of China, Changping Laboratory, Peking University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Funder: National Natural Science Foundation of China

Awards: G1790048183, G4819896817