Advancing Brainwave Modeling with a Codebook-Based Foundation Model

TL;DR

This paper introduces LaBraM++, an improved large-scale EEG model that leverages a codebook-based approach rooted in signal processing principles, leading to better performance and efficiency in brainwave analysis tasks.

Contribution

The paper presents LaBraM++, a novel large brainwave foundation model that enhances neural oscillation representation using a codebook-based architecture grounded in signal processing.

Findings

LaBraM++ outperforms previous models across multiple BCI tasks.

It achieves competitive results with other open-source large brainwave models.

The model demonstrates improved training efficiency and generalization.

Abstract

Recent advances in large-scale pre-trained Electroencephalogram (EEG) models have shown great promise, driving progress in Brain-Computer Interfaces (BCIs) and healthcare applications. However, despite their success, many existing pre-trained models have struggled to fully capture the rich information content of neural oscillations, a limitation that fundamentally constrains their performance and generalizability across diverse BCI tasks. This limitation is frequently rooted in suboptimal architectural design choices which constrain their representational capacity. In this work, we introduce LaBraM++, an enhanced Large Brainwave Foundation Model (LBM) that incorporates principled improvements grounded in robust signal processing foundations. LaBraM++ demonstrates substantial gains across a variety of tasks, consistently outperforming its originally-based architecture and achieving competitive results when compared to other open-source LBMs. Its superior performance and training efficiency highlight its potential as a strong foundation for future advancements in LBMs.