Results 31 to 40 of about 46,112 (290)
fMRI Brain Decoding and Its Applications in Brain–Computer Interface: A Survey
Brain Sciences, 2022 Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain–computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and recognize ...
Bing Du +3 more
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Neural decoding based on probabilistic neural network [PDF]
Journal of Zhejiang University SCIENCE B, 2010 Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices, such as robot arms, computer cursors, and paralyzed muscles.
Yi, Yu +6 more
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Multivariate neural decoding performance.
, 2022 Multivariate neural decoding performance.
Clinton D. Kilts (228932) +1 more
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Polar codes decoding algorithm based on convolutional neural network
Dianxin kexue, 2020 In order to solve the problem that the existing Polar code decoding algorithm based on neural network can only decode short codewords (codewords length N≤64),a new decoding algorithm using convolution neural network for long codewords (N≥512) was put ...
Rui GUO, Fanchun RAN
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Neural Decoding with Hierarchical Generative Models [PDF]
Neural Computation, 2010 Recent research has shown that reconstruction of perceived images based on hemodynamic response as measured with functional magnetic resonance imaging (fMRI) is starting to become feasible. In this letter, we explore reconstruction based on a learned hierarchy of features by employing a hierarchical generative model that consists of conditional ...
Marcel van Gerven +2 more
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Deep adversarial neural decoding
CoRR, 2017 Added appendix and updated ...
Yagmur Güçlütürk +5 more
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Adaptive HD-sEMG decomposition: towards robust real-time decoding of neural drive [PDF]
Publisher Copyright: © 2024 The Author(s). Published by IOP Publishing LtdObjective. Neural interfacing via decomposition of high-density surface electromyography (HD-sEMG) should be robust to signal non-stationarities incurred by changes in joint pose ...
Negro, Francesco +2 more
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Neural offset min-sum decoding [PDF]
2017 IEEE International Symposium on Information Theory (ISIT), 2017 Published as a conference paper at the 2017 International Symposium on Information Theory (ISIT)
Loren Lugosch, Warren J. Gross
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Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning [PDF]
, 2021 Objective. Brain–machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts.
Ahmadi, Nur +2 more
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De Bruijn cycles for neural decoding [PDF]
Journal of Vision, 2011 Stimulus counterbalance is critical for studies of neural habituation, bias, anticipation, and (more generally) the effect of stimulus history and context. We introduce de Bruijn cycles, a class of combinatorial objects, as the ideal source of pseudo-random stimulus sequences with arbitrary levels of counterbalance. Neuro-vascular imaging studies (such
Geoffrey Karl Aguirre +2 more
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