Мобильная версия

Доступно журналов:

3 288

Доступно статей:

3 891 637

 

Скрыть метаданые

Автор Kim, S-P
Автор Sanchez, J C
Автор Rao, Y N
Автор Erdogmus, D
Автор Carmena, J M
Автор Lebedev, M A
Автор Nicolelis, M A L
Автор Principe, J C
Дата выпуска 2006-06-01
dc.description The field of brain–machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100–200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.
Формат application.pdf
Издатель Institute of Physics Publishing
Копирайт 2006 IOP Publishing Ltd
Название A comparison of optimal MIMO linear and nonlinear models for brain–machine interfaces
Тип paper
DOI 10.1088/1741-2560/3/2/009
Electronic ISSN 1741-2552
Print ISSN 1741-2560
Журнал Journal of Neural Engineering
Том 3
Первая страница 145
Последняя страница 161
Аффилиация Kim, S-P; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
Аффилиация Sanchez, J C; Department of Pediatrics, Division of Neurology, University of Florida, Gainesville, FL 32611, USA
Аффилиация Rao, Y N; Motorola Inc. Plantation, FL 33322, USA
Аффилиация Erdogmus, D; Departments of Computer Science and Biomedical Engineering, Oregon Health & Science University, Beaverton, OR 97006, USA
Аффилиация Carmena, J M; Department of Neurobiology and the Center for Neuroengineering, Duke University, Durham, NC 27710, USA
Аффилиация Lebedev, M A; Department of Neurobiology and the Center for Neuroengineering, Duke University, Durham, NC 27710, USA
Аффилиация Nicolelis, M A L; Department of Neurobiology and the Center for Neuroengineering, Duke University, Durham, NC 27710, USA
Аффилиация Principe, J C; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
Выпуск 2

Скрыть метаданые