High-Performance Brain Machine Interfaces with Adaptive Neural Decoding for Prediction of the Rat Forelimb Movement

碩士 === 國立陽明大學 === 生物醫學工程學系 === 105 === Neuroscience research has been paid more and more attention in recent years, and one of the research is the brain machine interfaces (BMIs). In BMIs, in addition to solve the curse of dimensionality, the accuracy and stability of the decoding algorithm is also...

Full description

Bibliographic Details
Main Authors: Hsuan-Ho Chuang, 莊璿禾
Other Authors: You-Yin Chen
Format: Others
Language:en_US
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/xq2yug
id ndltd-TW-105YM005530022
record_format oai_dc
spelling ndltd-TW-105YM0055300222019-05-15T23:39:47Z http://ndltd.ncl.edu.tw/handle/xq2yug High-Performance Brain Machine Interfaces with Adaptive Neural Decoding for Prediction of the Rat Forelimb Movement 大鼠前肢軌跡之自適應神經解碼用於高性能腦機介面 Hsuan-Ho Chuang 莊璿禾 碩士 國立陽明大學 生物醫學工程學系 105 Neuroscience research has been paid more and more attention in recent years, and one of the research is the brain machine interfaces (BMIs). In BMIs, in addition to solve the curse of dimensionality, the accuracy and stability of the decoding algorithm is also important. The architecture of the signal acquisition in BMIs could be divided into invasive and non-invasive. For the invasive BMIs, researches are based on intracranial EEG (iEEG), electrocorticography (ECoG), intracortical local field potentials (LFPs), or neuronal spiking activity (AP). Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithm (PVA), optimal linear estimator (OLE), principal component analysis (PCA), partial least squares (PLS), Wiener filter (WF), Kalman filter (KF), Bayesian filter (BF), neural network (NN), and so on. Furthermore, depending on the type of the input, there are spike-driven and LFP-driven decoders. Both of them have high accuracy, and therefore are frequently used. This study adopts kernel sliced inverse regression (kSIR) to predict intended forelimb movement trajectories according to the recorded neurons from primary motor (M1) cortex. kSIR is a novel decoding algorithm, and is useful even when signals obtained from a smaller number of neurons. The LFPs was adopted to improve the performance, and hoped to improve the accuracy and stability. Results showed that the stability and accuracy were significantly improved, where the accuracy (R squared, Mean ± SEM) was improved from ("0.88"±"0.059" ) to ("0.93"±"0.061)" for x-axis and ("0.90"±"0.022)" to ("0.97"±"0.024)" for y-axis. And the adjustment of kernel bandwidth is necessary due to the deviation of observed distribution from Gaussian. Therefore, an adaptive architecture was applied to adjust the parameter using in kSIR. The superiority of this multi-input kSIR was obviously. To sum up, simultaneously using both spikes and LFPs would make the decoder more robust and accurate, even when met conditions with sparse neural information. You-Yin Chen 陳右穎 2017 學位論文 ; thesis 42 en_US
collection NDLTD
language en_US
format Others
sources NDLTD
description 碩士 === 國立陽明大學 === 生物醫學工程學系 === 105 === Neuroscience research has been paid more and more attention in recent years, and one of the research is the brain machine interfaces (BMIs). In BMIs, in addition to solve the curse of dimensionality, the accuracy and stability of the decoding algorithm is also important. The architecture of the signal acquisition in BMIs could be divided into invasive and non-invasive. For the invasive BMIs, researches are based on intracranial EEG (iEEG), electrocorticography (ECoG), intracortical local field potentials (LFPs), or neuronal spiking activity (AP). Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithm (PVA), optimal linear estimator (OLE), principal component analysis (PCA), partial least squares (PLS), Wiener filter (WF), Kalman filter (KF), Bayesian filter (BF), neural network (NN), and so on. Furthermore, depending on the type of the input, there are spike-driven and LFP-driven decoders. Both of them have high accuracy, and therefore are frequently used. This study adopts kernel sliced inverse regression (kSIR) to predict intended forelimb movement trajectories according to the recorded neurons from primary motor (M1) cortex. kSIR is a novel decoding algorithm, and is useful even when signals obtained from a smaller number of neurons. The LFPs was adopted to improve the performance, and hoped to improve the accuracy and stability. Results showed that the stability and accuracy were significantly improved, where the accuracy (R squared, Mean ± SEM) was improved from ("0.88"±"0.059" ) to ("0.93"±"0.061)" for x-axis and ("0.90"±"0.022)" to ("0.97"±"0.024)" for y-axis. And the adjustment of kernel bandwidth is necessary due to the deviation of observed distribution from Gaussian. Therefore, an adaptive architecture was applied to adjust the parameter using in kSIR. The superiority of this multi-input kSIR was obviously. To sum up, simultaneously using both spikes and LFPs would make the decoder more robust and accurate, even when met conditions with sparse neural information.
author2 You-Yin Chen
author_facet You-Yin Chen
Hsuan-Ho Chuang
莊璿禾
author Hsuan-Ho Chuang
莊璿禾
spellingShingle Hsuan-Ho Chuang
莊璿禾
High-Performance Brain Machine Interfaces with Adaptive Neural Decoding for Prediction of the Rat Forelimb Movement
author_sort Hsuan-Ho Chuang
title High-Performance Brain Machine Interfaces with Adaptive Neural Decoding for Prediction of the Rat Forelimb Movement
title_short High-Performance Brain Machine Interfaces with Adaptive Neural Decoding for Prediction of the Rat Forelimb Movement
title_full High-Performance Brain Machine Interfaces with Adaptive Neural Decoding for Prediction of the Rat Forelimb Movement
title_fullStr High-Performance Brain Machine Interfaces with Adaptive Neural Decoding for Prediction of the Rat Forelimb Movement
title_full_unstemmed High-Performance Brain Machine Interfaces with Adaptive Neural Decoding for Prediction of the Rat Forelimb Movement
title_sort high-performance brain machine interfaces with adaptive neural decoding for prediction of the rat forelimb movement
publishDate 2017
url http://ndltd.ncl.edu.tw/handle/xq2yug
work_keys_str_mv AT hsuanhochuang highperformancebrainmachineinterfaceswithadaptiveneuraldecodingforpredictionoftheratforelimbmovement
AT zhuāngxuánhé highperformancebrainmachineinterfaceswithadaptiveneuraldecodingforpredictionoftheratforelimbmovement
AT hsuanhochuang dàshǔqiánzhīguǐjīzhīzìshìyīngshénjīngjiěmǎyòngyúgāoxìngnéngnǎojījièmiàn
AT zhuāngxuánhé dàshǔqiánzhīguǐjīzhīzìshìyīngshénjīngjiěmǎyòngyúgāoxìngnéngnǎojījièmiàn
_version_ 1719152515414491136