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arXiv Machine Learning

FRIST: FMRI Representation Informed Shared-space Training Improves EEG-only Individual-Finger BCI Decoding

The paper introduces FRIST, a two‑stage EEG decoding framework that uses fMRI data to learn spectral projections and class geometry, then refines EEG predictions. In experiments with 12 participants, FRIST improves finger‑level motor decoding accuracy across both movement execution and motor imagery tasks, outperforming EEG‑only baselines and generalizing across different EEG backbones. The method demonstrates that fMRI’s spatial resolution can enhance noninvasive EEG‑based BCI performance.

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arXiv Machine Learning
Sep 2

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

The study investigates whether Low‑Rank Adaptation (LoRA) can adapt three pretrained EEG foundation models—LaBraM‑base, REVE‑base, and REVE‑large—for binary left‑ vs. right‑hand motor imagery decoding in stroke patients. Using subject‑wise five‑fold cross‑validation on the PhysioNet EEG Motor Movement/Imagery Dataset and a binary subset of the UET175 stroke dataset, LoRA significantly improved accuracy for LaBraM‑base (0.822) and REVE‑base (0.957) on the healthy cohort, but only REVE‑base LoRA achieved high performance (0.847±0.194) on stroke data, with a best mean accuracy of 0.952 in leave‑one‑subject‑out evaluation. The results demonstrate that healthy‑benchmark performance does not guarantee transfer to stroke EEG, highlighting the need for target‑domain adaptation and subject‑level assessment in rehabilitation BCIs.

By Anh T. Nguyen, Zihua Sun, Michelle J. Johnson
fine-tuningbenchmarks
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arXiv AI
Jul 14

Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning

arXiv:2607. 11530v1 Announce Type: new Abstract: Decoding continuous three-dimensional (3D) motor imagery (MI) using non-invasive electroencephalography (EEG)-based brain--computer interfaces (BCIs) remains challenging due to signal variability and residual decoding errors.

By Jiamian Li, Niall McShane, Attila Korik, Naomi du Bois, Karl McCreadie, Leen Jabban, Benjamin Metcalfe, \"Ozg\"ur \c{S}im\c{s}ek, Damien Coyle
agentsreinforcement-learning
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Hugging Face Trending Papers
Jul 13

Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning

Decoding continuous three-dimensional (3D) motor imagery (MI) using non-invasive electroencephalography (EEG)-based brain--computer interfaces (BCIs) remains challenging due to signal variability and residual decoding errors. Deep learning architectures such as convolutional neural network--long short-term memory (CNN--LSTM) models can capture spatial and temporal dynamics for continuous kinematic decoding; however, systematic residual errors persist in predicted trajectories.

agentsreinforcement-learning
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arXiv Machine Learning
Sep 18

Beyond Flattened Tokens: Structure-Preserving EEG Decoding with Reusable TriDim Blocks

The paper introduces TriDim, a reusable block that preserves the three EEG axes—channel, short‑term temporal, and long‑term temporal—by applying feed‑forward transformations and cross‑axis attention. Stacking these blocks yields TriDimEEG, a standalone EEG decoder that outperforms fifteen other models on eight datasets, achieving a 4.3% relative accuracy gain. Replacing Transformer blocks in existing EEG foundation models with TriDim blocks improves downstream accuracy by 7.4% on average while reducing parameters by 17.0% to 47.3%.

By Shiyue Su, Song Wang, Zekai Zhan, Junjie Zeng, Ziling Lu, Zongsheng Li, Xinyuan Ye, Zhiyuan Ma, Xinke Shen, Quanying Liu
llms
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