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

Owais Mujtaba Khanday; Mohamed Baha Ben Ticha; Sanae Belfrouh; Marc Ouellet; Jose A. Gonzalez-Lopez

cs.SDJul 29, 2026

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

Authors: Owais Mujtaba Khanday, Mohamed Baha Ben Ticha, Sanae Belfrouh, Marc Ouellet, Jose A. Gonzalez-Lopez

Organizations: Dept. Signal Theory, Telematics and Communications, University of Granada, Spain · Research Centre for Information and Communication Technologies (CITIC-UGR), Spain · Laboratory of Information Technologies, University of Chouaib Doukkali, Morocco · Brain, Mind, and Behavior Research Center (CIMCYC), University of Granada, Spain

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.

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Aug 31, 2026 · cs.CEJ/K move · Enter open · S save

Lightweight Adaptation of EEG Foundation Models for Stroke Motor Imagery Decoding: Domain Shift and Subject-Level Robustness

Anh T. Nguyen, Zihua Sun, Michelle J. Johnson

Motor imagery (MI) electroencephalography (EEG) decoding could support post-stroke rehabilitation, but models developed on healthy cohorts may not transfer reliably to pathological EEG. We evaluated whether Low-Rank Adaptation (LoRA) can efficiently adapt three pretrained EEG foundation models (i.e., LaBraM-base, REVE-base, and REVE-large) for binary left- versus right-hand MI decoding. Frozen-backbone head-only baselines and LoRA adaptation were evaluated using subject-wise five-fold cross-validation on the PhysioNet EEG Motor Movement/Imagery Dataset and a binary subset of the UET175 dataset comprising 30 stroke participants. On EEGMMIDB, LoRA increased accuracy to 0.822 for LaBraM-base and 0.957 for REVE-base. On UET175, all head-only models performed near chance. With LoRA, LaBraM-base remained near chance (0.499

\pm

0.009), whereas REVE-base reached 0.847

\pm

0.194 and outperformed REVE-large (0.806

\pm

0.178), indicating that increased model capacity alone did not improve stroke-domain adaptation. The strongest stroke configuration, REVE-base LoRA, was further evaluated using within-cohort leave-one-subject-out cross-validation (LOOCV), showing 0.952 mean accuracy, but subject-wise accuracy ranged from 0.586 to 1.000, revealing a small low-performing tail. Zero-shot transfer from EEGMMIDB to UET175 remained near chance (0.464

\pm

0.072). These findings show that healthy-benchmark performance does not ensure transfer to stroke EEG. Translation of EEG foundation models to pseudo-online or real-time rehabilitation BCIs should therefore include target-domain adaptation and subject-level assessment of temporal informativeness, spatial sensitivity, and physiological discriminability.

Motor Imagery Electroencephalography Foundation Models Rehabilitation Low-Rank Adaptation Domain Shift

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Abstract

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