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.
A review of classification algorith ms for eeg-based brain–computer interfaces: a 10 year update
2 Pith papers cite this work. Polarity classification is still indexing.
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No performance difference was found between neuro-adaptive and fixed-difficulty VR flight training, yet pilots preferred the adaptive version after briefing.
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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.