arXiv:2607. 16225v1 Announce Type: cross Abstract: Brain-Computer Interfaces (BCIs) face a severe calibration bottleneck due to cross-subject spatial covariance shifts and physiological artifacts.
arXiv:2606. 18816v1 Announce Type: cross Abstract: Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimensional neural decoding but typically exceed the computational limits of embedded hardware.
arXiv:2608. 05315v1 Announce Type: new Abstract: Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) often require unsupervised domain adaptation (UDA) to generalize across subjects and sessions.
arXiv:2607. 03094v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding for brain-computer interfaces (BCIs) faces a major challenge: substantial inter-subject variability limits effective cross-subject generalization.
arXiv:2510. 15371v2 Announce Type: replace-cross Abstract: Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including communication assistance and rehabilitation support for patients with motor impairments.
arXiv:2511. 18940v3 Announce Type: replace Abstract: Cross-subject motor imagery decoding remains a fundamental challenge in EEG-based brain-computer interfaces due to substantial inter-subject variability.
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.
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.
CortexBridge is a lightweight adapter that aligns arbitrary EEG electrode montages into a shared cortical latent space by combining EEG features with electrode and atlas coordinates. When evaluated on five BCI datasets from MOABB with three frozen foundation models, it improves performance in 13 of 15 tasks, achieving average balanced‑accuracy gains of 0.80% (EEGPT), 0.70% (LaBraM), and 3.26% (CBraMod), with a maximum 13.02% improvement on a 12‑class SSVEP task. Visualizations show task‑dependent spatial patterns, such as a concentrated representation in the Yeo Visual network for SSVEP versus auditory P300.
arXiv:2601. 07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints.
arXiv:2609.23924v1 Announce Type: new Abstract: Pretrained EEG foundation models are increasingly proposed as general-purpose encoders for brain-computer interfaces, yet recent benchmarks disagree ab...
arXiv:2608. 04389v1 Announce Type: new Abstract: Decoding continuous motor trajectories from neural activity is essential for developing practical brain-computer interfaces (BCIs).
arXiv:2607. 22980v1 Announce Type: new Abstract: Brain-computer interfaces (BCIs) have long sought calibration-free operation, but classifiers are typically benchmarked by discrimination alone, blind to whether predicted probabilities are well calibrated - a meaningful gap given nonstationary electroencephalogram (EEG) signals and the risk of overconfident point-estimate classifiers under distribution shift.