Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning

TL;DR

This paper introduces a novel differentially private multimodal EEG learning method that combines language and vision models with adaptive Laplacian dropout, improving classification accuracy while ensuring privacy.

Contribution

It proposes a new multimodal EEG learning model with a privacy-preserving Laplacian dropout scheme that dynamically optimizes privacy and performance.

Findings

Achieved approximately 4% improvement in classification accuracy.

Demonstrated state-of-the-art performance in multimodal EEG learning under differential privacy.

Validated on an open-source Parkinson's Disease dataset.

Abstract

Recently, multimodal electroencephalogram (EEG) learning has shown great promise in disease detection. At the same time, ensuring privacy in clinical studies has become increasingly crucial due to legal and ethical concerns. One widely adopted scheme for privacy protection is differential privacy (DP) because of its clear interpretation and ease of implementation. Although numerous methods have been proposed under DP, it has not been extensively studied for multimodal EEG data due to the complexities of models and signal data considered there. In this paper, we propose a novel Differentially Private Multimodal Laplacian Dropout (DP-MLD) scheme for multimodal EEG learning. Our approach proposes a novel multimodal representative learning model that processes EEG data by language models as text and other modal data by vision transformers as images, incorporating well-designed cross-attention mechanisms to effectively extract and integrate cross-modal features. To achieve DP, we design a novel adaptive feature-level Laplacian dropout scheme, where randomness allocation and performance are dynamically optimized within given privacy budgets. In the experiment on an open-source multimodal dataset of Freezing of Gait (FoG) in Parkinson's Disease (PD), our proposed method demonstrates an approximate 4\% improvement in classification accuracy, and achieves state-of-the-art performance in multimodal EEG learning under DP.