MultiDiffNet: A Multi-Objective Diffusion Framework for Generalizable Brain Decoding

TL;DR

MultiDiffNet introduces a diffusion-based framework that learns a compact latent space for EEG decoding, significantly improving generalization across subjects and sessions without synthetic data augmentation, and provides a comprehensive benchmark suite.

Contribution

It proposes a novel diffusion-based approach for EEG decoding that enhances cross-subject generalization and introduces a unified benchmark and evaluation protocol.

Findings

Achieves state-of-the-art generalization in EEG decoding tasks.

Provides a new benchmark suite and evaluation protocol for EEG research.

Develops a statistical reporting framework for low-trial EEG settings.

Abstract

Neural decoding from electroencephalography (EEG) remains fundamentally limited by poor generalization to unseen subjects, driven by high inter-subject variability and the lack of large-scale datasets to model it effectively. Existing methods often rely on synthetic subject generation or simplistic data augmentation, but these strategies fail to scale or generalize reliably. We introduce \textit{MultiDiffNet}, a diffusion-based framework that bypasses generative augmentation entirely by learning a compact latent space optimized for multiple objectives. We decode directly from this space and achieve state-of-the-art generalization across various neural decoding tasks using subject and session disjoint evaluation. We also curate and release a unified benchmark suite spanning four EEG decoding tasks of increasing complexity (SSVEP, Motor Imagery, P300, and Imagined Speech) and an evaluation protocol that addresses inconsistent split practices in prior EEG research. Finally, we develop a statistical reporting framework tailored for low-trial EEG settings. Our work provides a reproducible and open-source foundation for subject-agnostic EEG decoding in real-world BCI systems.