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研究生:王俊德
研究生(外文):Chun-Te Wang
論文名稱:以粒子群最佳化之類神經網路實現心智工作腦波分類
論文名稱(外文):Classification of Mental Task from EEG Data Using Neural Networks Based on Particle Swarm Optimization
指導教授:林正堅林正堅引用關係劉啓東
指導教授(外文):Cheng-Jian LinChii-Tung Liu
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊工程系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:46
中文關鍵詞:倒傳遞演算法類神經網路主成份分析功率頻譜密度腦電波粒子群最佳化分類
外文關鍵詞:power spectral density (PSD)particle swarm optimization(PSO)principle component analysis (PCA)Electroencephalogram (EEG)classificationback-propagation algorithm(BP)neural networks (NNs)
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人腦-電腦介面是一種能夠將人在不同心智下工作所產生的腦部行為轉換為控制信號的系統。此系統能夠提供一些肢體重度障礙的病患一個輔助的通訊管道。在本篇論文中我們提出一個方法分類想像左手動作、想像右手動作及產生單字的心智工作腦電波,並且期望將來能用來實現人腦-電腦介面系統。首先,我們採用主成份分析取出腦波功率頻譜密度的特徵,同時也降低資料的維度以利於分析。接下來使用三層式前饋類神經網路做為分類器,並使用粒子群最佳化演算法訓練網路參數。使用粒子群最佳化演算法能夠避免掉傳統的倒傳遞演算法過早收斂的缺點,其效能的比較也在結果中得到驗證。
The BCI (Brain-Computer Interface) is a system which transforms the brain activity made by different mental task to produce the control signal. The system provides an augmentative communication method to those patients with severe motor disabilities. In this thesis, a method used to classify the electroencephalogram (EEG) of mental task for left hand movement imagination, right hand movement imagination, and word generation is proposed. And we expect the classifying could be used to realize the BCI system. First, the EEG pattern is reduced in a lower dimension and fetched the feature by principle component analysis (PCA). Then, a three layer feed-forward neural network trained by particle swarm optimization (PSO) is used to realize a classifier. The PSO algorithm training the parameters of neural network can avoid some drawbacks of the back-propagation (BP) algorithm like premature converge. The performance demonstration is shown in the result.
摘要 I
Abstract II
Contents V
List of Tables VI
List of Figures VI
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Literature Survey 4
1.3 Motivation 5
1.4 Thesis Organization 6
Chapter 2 EEG dataset 7
2.1 Experiment 7
2.2 Format of Data 8
Chapter 3 Analysis Method 15
3.1 Data Preprocessing 16
3.2 Feature Fetch using Principle Component Analysis 16
3.3 Classification 18
3.3.1 Neural Networks 18
3.3.2 Particle Swarm Optimization 23
Chapter 4 Simulation Results 29
Chapter 5 Conclusion 40
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