Training data

Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions from Limited Data
Author(s)3: Marko Angjelichinoski, Mohammadreza Soltani, John Choi, Bijan Pesaran, Vahid Tarokh
Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions from Limited Data 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Non-parametric regression has been shown to be useful in extracting relevant features from Local Field Potential (LFP) signals for decoding motor intentions. Yet, in many instances, brain-computer interfaces (BCIs) rely…

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Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters

Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters
Author(s)3: Andreas Meinel, Henrich Kolkhorst, Michael Tangermann
Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

   Data-driven spatial filtering algorithms optimize scores, such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for the filter estimation, however, disregard…

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TNSRE serves the community of biomedical engineers and clinical researchers who work at the intersection of neuroscience and physical medicine. We publish novel approaches and technologies for better understanding neural systems, human movement, and the relationships between them, with a focus on assistive devices that improve life for patients, for practicing clinicians, and for everyday use.

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