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arXiv AI

A Multi-Objective Optimisation Framework for Corticomuscular EEG-EMG Pair Selection in Hybrid BCI

The paper presents a data‑driven framework that selects optimal EEG‑EMG channel pairs for hybrid brain‑computer interfaces by formulating the problem as a constrained bi‑objective optimisation. It maximises both the spatial relevance of EEG channels to motor cortex areas and the corticomuscular coupling strength, solved with NSGA‑II. Applied to motor‑imagery data from eight stroke patients, the method achieved an average classification accuracy of 89.6%, indicating improved capture of physiologically meaningful interactions.

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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
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arXiv Machine Learning
Sep 17

Sparse Bayesian Modeling of EEG Channel Interactions Improves P300 Brain-Computer Interface Performance

The paper introduces a sparse Bayesian time‑varying regression framework that models pairwise EEG channel interactions and performs temporal feature selection for P300 brain‑computer interfaces. Using a relaxed‑thresholded Gaussian process prior, the method achieves a median character‑level accuracy of 96.4% on a public P300 speller dataset and outperforms both statistical and deep learning baselines. It also yields subgroup‑specific gains, notably for participants who abstain from alcohol, and improves median BCI‑Utility by over 10%, reaching peak throughput after only six sequence repetitions.

By Guoxuan Ma, Yuan Zhong, Moyan Li, Yuxiao Nie, Jian Kang
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arXiv AI
Sep 18

A Hybrid Gaze-Motor Imagery BCI Framework for Effective Decision Communication

The study presents a hybrid brain‑computer interface that combines eye‑tracking and motor‑imagery (MI) to improve decision communication. By using visual fixation to stabilize neural responses, the authors developed an asynchronous paradigm that first selects a command via eye‑tracking and then confirms it with MI, reducing the number of operational steps. Experiments with 15 participants showed that this hybrid approach achieves up to 100% accuracy and outperforms conventional MI, even with limited EEG channels.

By Gowtham Reddy N, KongFatt Wong-Lin, Yogesh Kumar Meena
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arXiv AI
Sep 25

Decoding Imagined Speech: A Strictly Subject-Independent Approach Using EEG

This study evaluates imagined speech decoding from EEG using a strictly subject‑independent framework. Two pipelines—time‑domain statistical features and frequency‑domain spectral bandpower—were compared with a random forest classifier; the spectral approach achieved higher trial‑wise accuracy (49.03 % vs. 37.97 %). Feature selection revealed that only a few frequency bands carried most discriminative information, providing a solid baseline for future cross‑subject BCI research.

By Frederik M{\o}llskov Trier, Xiaopeng Mao, Sadasivan Puthusserypady
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