Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion

TL;DR

This paper introduces a novel self-supervised pre-training model that effectively fuses fMRI and EEG data across multiple domains, improving classification performance in neuroimaging for brain disorder analysis.

Contribution

It proposes a multi-modal cross-domain self-supervised pre-training approach that leverages domain-specific augmentations and contrastive loss to enhance neuroimaging data fusion.

Findings

Demonstrates superior classification accuracy across multiple tasks.

Shows improved generalizability over existing models.

Effectively captures complementary information from fMRI and EEG.

Abstract

Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting functional abnormalities in various brain disorders. However, existing studies often focus on a single domain or modality, neglecting the valuable complementary information offered by multiple domains from both fMRI and EEG, which is crucial for a comprehensive representation of disorder pathology. This limitation poses a challenge in effectively leveraging the synergistic information derived from these modalities. To address this, we propose a Multi-modal Cross-domain Self-supervised Pre-training Model (MCSP), a novel approach that leverages self-supervised learning to synergize multi-modal information across spatial, temporal, and spectral domains. Our model employs cross-domain self-supervised loss that bridges domain differences by implementing domain-specific data augmentation and contrastive loss, enhancing feature discrimination. Furthermore, MCSP introduces cross-modal self-supervised loss to capitalize on the complementary information of fMRI and EEG, facilitating knowledge distillation within domains and maximizing cross-modal feature convergence. We constructed a large-scale pre-training dataset and pretrained MCSP model by leveraging proposed self-supervised paradigms to fully harness multimodal neuroimaging data. Through comprehensive experiments, we have demonstrated the superior performance and generalizability of our model on multiple classification tasks. Our study contributes a significant advancement in the fusion of fMRI and EEG, marking a novel integration of cross-domain features, which enriches the existing landscape of neuroimaging research, particularly within the context of mental disorder studies.