Motor Imagery BCI System Using Bidirectional Long-Short Term Memory and Discrete Wavelet Transform
碩士 === 國立勤益科技大學 === 資訊工程系 === 107 === Motor Imagery is a method to generate EEG signals by using imaging proper activities such as moving left- or right- hand, legs and so on. These EEG signals can be used to control peripheral devices through a Brain Computer Interface (BCI) system. How to classify...
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ndltd-TW-107NCIT03920172019-11-16T05:27:41Z http://ndltd.ncl.edu.tw/handle/sw5a3f Motor Imagery BCI System Using Bidirectional Long-Short Term Memory and Discrete Wavelet Transform 使用離散小波轉換與雙向長短期記憶神經網路 實現運動想像腦機介面系統 SHE,PING-HUNG 佘秉宏 碩士 國立勤益科技大學 資訊工程系 107 Motor Imagery is a method to generate EEG signals by using imaging proper activities such as moving left- or right- hand, legs and so on. These EEG signals can be used to control peripheral devices through a Brain Computer Interface (BCI) system. How to classify signals after imaging is an important issue. In order to make full use of the time-frequency characteristics of sequence signals in EEG, a deep learning model modified from Long Short-Term Memory (LSTM) is applied. The Bidirectional LSTM is used to classify the signals. This article recorded four kinds of brainwave signals, which are imagined left-handed, imagined right-handed, imagined tongue and blinking. This article combined the g.SAHARAsys system with Emotiv EPOC IC to extract brainwave signals and then wirelessly transmit them to a computer. According to the international 10-20 system, we use three electrode points, C3, C4 and Cz, to intercept the brain wave signal, and then use the Discrete Wavelet Transform (DWT) to extract the time-frequency characteristics of the motional imaginary brain wave. Finally, the motion imaginary signals were identified through the Bidirectional LSTM. LIN,JZAU-SHENG 林灶生 2019 學位論文 ; thesis 45 zh-TW |
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碩士 === 國立勤益科技大學 === 資訊工程系 === 107 === Motor Imagery is a method to generate EEG signals by using imaging proper activities such as moving left- or right- hand, legs and so on. These EEG signals can be used to control peripheral devices through a Brain Computer Interface (BCI) system. How to classify signals after imaging is an important issue. In order to make full use of the time-frequency characteristics of sequence signals in EEG, a deep learning model modified from Long Short-Term Memory (LSTM) is applied. The Bidirectional LSTM is used to classify the signals. This article recorded four kinds of brainwave signals, which are imagined left-handed, imagined right-handed, imagined tongue and blinking. This article combined the g.SAHARAsys system with Emotiv EPOC IC to extract brainwave signals and then wirelessly transmit them to a computer. According to the international 10-20 system, we use three electrode points, C3, C4 and Cz, to intercept the brain wave signal, and then use the Discrete Wavelet Transform (DWT) to extract the time-frequency characteristics of the motional imaginary brain wave. Finally, the motion imaginary signals were identified through the Bidirectional LSTM.
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LIN,JZAU-SHENG |
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LIN,JZAU-SHENG SHE,PING-HUNG 佘秉宏 |
author |
SHE,PING-HUNG 佘秉宏 |
spellingShingle |
SHE,PING-HUNG 佘秉宏 Motor Imagery BCI System Using Bidirectional Long-Short Term Memory and Discrete Wavelet Transform |
author_sort |
SHE,PING-HUNG |
title |
Motor Imagery BCI System Using Bidirectional Long-Short Term Memory and Discrete Wavelet Transform |
title_short |
Motor Imagery BCI System Using Bidirectional Long-Short Term Memory and Discrete Wavelet Transform |
title_full |
Motor Imagery BCI System Using Bidirectional Long-Short Term Memory and Discrete Wavelet Transform |
title_fullStr |
Motor Imagery BCI System Using Bidirectional Long-Short Term Memory and Discrete Wavelet Transform |
title_full_unstemmed |
Motor Imagery BCI System Using Bidirectional Long-Short Term Memory and Discrete Wavelet Transform |
title_sort |
motor imagery bci system using bidirectional long-short term memory and discrete wavelet transform |
publishDate |
2019 |
url |
http://ndltd.ncl.edu.tw/handle/sw5a3f |
work_keys_str_mv |
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