Subject identity variance dominates frozen representations in three EEG foundation models by 13-89x over null, and erasing the linear subject axis improves label decoding where within-subject label variation exists.
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A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
4 Pith papers cite this work, alongside 2,118 external citations. Polarity classification is still indexing.
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A methodological framework for BCIs that separates speed and accuracy with Gain and Conservation measures combined into an alpha-controlled balance for tunable operating points.
TRR combines multi-band Riemannian features with a GRU to decode high-dimensional finger kinematics from EMG, achieving 9.79° intra-subject and 16.71° cross-subject average absolute error while running at ~10 Hz on a Raspberry Pi.
No performance difference was found between neuro-adaptive and fixed-difficulty VR flight training, yet pilots preferred the adaptive version after briefing.
citing papers explorer
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The Identity Trap in EEG Foundation Models: A Diagnostic Audit
Subject identity variance dominates frozen representations in three EEG foundation models by 13-89x over null, and erasing the linear subject axis improves label decoding where within-subject label variation exists.
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A Methodological Framework for Explicit Control of the Speed-Accuracy Trade-off in Brain-Computer Interfaces
A methodological framework for BCIs that separates speed and accuracy with Gain and Conservation measures combined into an alpha-controlled balance for tunable operating points.
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Decoding High-Dimensional Finger Motion from EMG Using Riemannian Features and RNNs
TRR combines multi-band Riemannian features with a GRU to decode high-dimensional finger kinematics from EMG, achieving 9.79° intra-subject and 16.71° cross-subject average absolute error while running at ~10 Hz on a Raspberry Pi.
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Prototyping and Evaluating a Real-time Neuro-Adaptive Virtual Reality Flight Training System
No performance difference was found between neuro-adaptive and fixed-difficulty VR flight training, yet pilots preferred the adaptive version after briefing.