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Dec 26, 2023 · In this paper, we proposed a TQRNN neural decoder. It achieved a 3.45% improvement in performance compared to the current state-of-the-art on ...
Dec 26, 2023 · A QRNN network integrated with a temporal attention module was proposed to decode movement kinematics from neural populations.
Offline decoding of movement trajectories from invasive brain-machine interface (iBMI) is a crucial issue of achieving cortical movement control.
May 8, 2020 · We evaluated the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex ...
This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs.
Robust and accurate decoding of hand kinematics from entire spiking ...
pubmed.ncbi.nlm.nih.gov › ...
We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded.
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A Robust and High Accurate Method for Hand Kinematics Decoding from Neural Populations. https://doi.org/10.1007/978-981-99-8546-3_20 ·.
Offline decoding of movement trajectories from invasive brain-machine interface (iBMI) is a crucial issue of achieving cortical movement control.
This study developed a robust and efficient neural decoding method that can predict joint angles of individual finger, which is promising for real-time ...
Jan 22, 2021 · We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded ...