decoding

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…

read more
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…

read more

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…

read more

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…

read more