A rigorous leave-one-subject-out benchmark on public auditory EEG data shows five-vowel decoding accuracy of 25.5 percent (chance 20 percent) using differential entropy features and LightGBM, with vowel information present but weak and localized to early auditory transients.
MOABB: trustworthy algorithm benchmarking for BCIs.Journal of Neural Engineering, 15(6):066011, December2018
6 Pith papers cite this work. Polarity classification is still indexing.
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Introduces intrinsic barycentric projection via conditional Fréchet means as the optimal deterministic map under squared geodesic loss for OT couplings on Riemannian manifolds, plus a tangential log-exp projection with Euclidean exactness and Monge compatibility.
Brain-OF is a multimodal foundation model for fMRI, EEG and MEG using any-resolution sampling, DINT attention with sparse MoE, and masked temporal-frequency pretraining on ~40 datasets to achieve superior downstream performance.
STAMP adapter enables general time series foundation models to match specialized EEG foundation models on clinical classification tasks across 8 benchmarks while using few trainable parameters.
IMU-based gestures enable voluntary initiation of grasp and release in shared-autonomous prosthetic hands, with elbow flap preferred by 66% and achieving 95% success rate in a study of 15 participants.
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
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Barycentric Projections of Optimal Transport Plans on Riemannian Manifolds
Introduces intrinsic barycentric projection via conditional Fréchet means as the optimal deterministic map under squared geodesic loss for OT couplings on Riemannian manifolds, plus a tangential log-exp projection with Euclidean exactness and Monge compatibility.