Towards Unified Multi-task EEG Analysis with Low-Rank Adaptation

2026-04-27

AI Summary

This work addresses the challenges of signal heterogeneity and task interference in multi-task electroencephalography (EEG) analysis, which hinder effective sharing of a single pre-trained model across diverse tasks. To overcome these limitations, the authors propose MTEEG, a unified framework for multi-task EEG analysis that leverages a self-supervised pre-trained EEG backbone augmented with task-specific Low-Rank Adaptation (LoRA) modules. This design decouples task-specific parameter spaces while maintaining a shared encoder, thereby mitigating inter-task interference. Comprehensive experiments across six downstream tasks demonstrate that MTEEG consistently outperforms state-of-the-art single-task methods on most evaluation metrics, substantiating the efficacy and potential of joint multi-task optimization in EEG representation learning.

Abstract

Recent self-supervised pre-training methods for electroencephalogram (EEG) have shown promising results. However, the pre-trained models typically require full fine-tuning on each downstream task individually to achieve good performance. In practical applications involving multiple tasks, utilizing a separate model for each task is not ideal regarding computational and spatial cost. In this study, we go one step further and explore the simultaneous adaptation of a pre-trained model to multiple different tasks. The EEG signals exhibit significant heterogeneity due to their collection from various subjects using diverse devices and experimental setups, resulting in potential conflicts among different tasks that impede joint optimization. To tackle this challenge, we propose MTEEG, a multi-task EEG analysis framework which incorporates task-specific low-rank adaptation (LoRA) modules to disentangle the parameter space and alleviate task conflicts. To investigate the trade-off between task specification and interaction, we propose three variants of MTEEG that integrate the LoRA modules in different ways and evaluate them on six downstream tasks, demonstrating that MTEEG can surpass state-of-the-art single-task methods on the majority of metrics. MTEEG shows the potential of multi-task EEG analysis and promotes the development of general-purpose brain-computer interfaces in the future.

Problem

• multi-task EEG analysis
• task conflict
• model adaptation
• EEG heterogeneity
• computational cost

Innovation

• multi-task learning
• low-rank adaptation
• EEG analysis
• self-supervised pre-training
• brain-computer interface