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Does EEG Foundation Models Transfer to Speech? A Benchmark on Overt and Imagined Speech Decoding

2026-07-29 10:06:01
Owais Mujtaba Khanday, Mohamed Baha Ben Ticha, Sanae Belfrouh, Marc Ouellet, Jose A. Gonzalez-Lopez
arXiv_SD
             
Abstract

EEG foundation models pretrained on thousands of hours have shown large gains over task-specific networks for motor imagery, seizure detection, sleep staging, and emotion recognition, but their transfer to speech decoding-arguably the most demanding non-invasive BCI application-remains untested. We present the first systematic benchmark of EEG foundation models against strong convolutional baselines for speech decoding, using two corpora: UGR-MINDVOICE (overt and covert Iberian Spanish) and BCI Competition 2020 Track 3 (imagined speech). We compare two foundation models (LaBraM, EEGMamba) against three established baselines (EEGNet, ShallowFBCSPNet, EEGConformer) under a unified preprocessing and fine-tuning protocol. Large-scale EEG pretraining yields no consistent advantage over a 16K-parameter CNN on speech tasks, indicating that current general-purpose EEG pretraining does not yet transfer to speech production and motivating speech-specific foundation models.

Abstract (translated)

URL

https://arxiv.org/abs/2607.27268

PDF

https://arxiv.org/pdf/2607.27268.pdf

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