decoding

Brain Network Evaluation by Functional-Guided Effective Connectivity Reinforcement Learning Method Indicates Therapeutic Effect for Tinnitus
Brain Network Evaluation by Functional-Guided Effective Connectivity Reinforcement Learning Method Indicates Therapeutic Effect for Tinnitus 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Using functional connectivity (FC) or effective connectivity (EC) alone cannot effectively delineate brain networks based on functional magnetic resonance imaging (fMRI) data, limiting the understanding of the mechanism of tinnitus… read more

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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Neural Decoding for Macaque’s Finger Position: Convolutional Space Model

Neural Decoding for Macaque’s Finger Position: Convolutional Space Model
Author(s)3: Haifeng Wu, Jingyi Feng, Yu Zeng
Neural Decoding for Macaque’s Finger Position: Convolutional Space Model 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

   In this paper, we study how to use the number of spike signals in a macaque’s motor cortex to estimate the position of its finger movement. First, we analyze…

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Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography

Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography
Author(s)3: Tessy M. Thomas, Daniel N. Candrea, Matthew S. Fifer, David P. McMullen, W. S. Anderson, Nitish V. Thakor, Nathan E. Crone
Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

       Brain–machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide the control of neurally driven prosthetic arms. Most previous research has focused on achieving an…

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Spatial and Functional Selectivity of Peripheral Nerve Signal Recording With the Transversal Intrafascicular Multichannel Electrode (TIME)
Author(s)3: Jordi Badia, Stanisa Raspopovic, Jacopo Carpaneto, Silvestro Micera, Xavier Navarro
Spatial and Functional Selectivity of Peripheral Nerve Signal Recording With the Transversal Intrafascicular Multichannel Electrode (TIME) 780 560 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

The selection of suitable peripheral nerve electrodes for biomedical applications implies a trade-off between invasiveness and selectivity. The optimal design should provide the highest selectivity for targeting a large number…

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Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces
Author(s)3: Yiwen Wang, Fang Wang, Kai Xu, Qiaosheng Zhang, Shaomin Zhang, Xiaoxiang Zheng
Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces 556 235 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Abstract Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited…

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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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