Are foundation models useful feature extractors for electroencephalography analysis?

TL;DR

This paper evaluates the effectiveness of foundation models as feature extractors for EEG analysis, demonstrating their potential to reduce data requirements and aid clinical diagnostics.

Contribution

It provides the first comprehensive assessment of general-purpose time series models for EEG tasks, comparing them to specialized models in clinical applications.

Findings

General-purpose models are competitive with specialized EEG models.

They effectively capture features related to demographics and diseases.

Foundation models can reduce dependence on large, task-specific datasets.

Abstract

The success of foundation models in natural language processing and computer vision has motivated similar approaches in time series analysis. While foundational time series models have proven beneficial on a variety of tasks, their effectiveness in medical applications with limited data remains underexplored. In this work, we investigate this question in the context of electroencephalography (EEG) by evaluating general-purpose time series models on age prediction, seizure detection, and classification of clinically relevant EEG events. We compare their diagnostic performance against specialised EEG models and assess the quality of the extracted features. The results show that general-purpose models are competitive and capture features useful to localising demographic and disease-related biomarkers. These findings indicate that foundational time series models can reduce the reliance on large task-specific datasets and models, making them valuable in clinical practice.