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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳良基(Liang-Gee Chen) | |
| dc.contributor.author | Yun-Yu Chen | en |
| dc.contributor.author | 陳韻宇 | zh_TW |
| dc.date.accessioned | 2021-06-08T05:13:00Z | - |
| dc.date.copyright | 2011-08-23 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-21 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/23953 | - |
| dc.description.abstract | 腦機介面可以用於控制外部儀器，而且極可能成為下一個世代的使用者與電腦的操作介面。應用於腦電訊號暨動作相關腦機介面是腦機介面的主要關鍵，並會於接下來的論文中做深入探討。應用於腦電訊號暨動作相關腦機介面通常用於溝通和控制。然而，這個系統需要大量的資料來做演算法的訓練。蒐集訓練所需要的資料會耗費大量的時間，會減少在日常生活中使用此系統的可能性。
在此篇論文中，蒐集訓練資料所耗費的時間和演算法的準確度之間的權衡首先被分析，接著並重複使用通用且事先錄製好的訓練資料來增進兩者之間的權衡。根據知識轉移理論，重複使用事先錄製好的訓練資料可以補償因樣本之間的差異性造成的資訊不足，並克服維度傷害的問題。此外，我們提出了一個信心模型，利用不同的特徵和回饋來對分類結果做信賴程度的評估，用以支援線上學習系統。根據模擬的結果，蒐集訓練所花的時間中，有93.4%可以被省略且不會造成準確度的下降。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-08T05:13:00Z (GMT). No. of bitstreams: 1 ntu-100-R98943047-1.pdf: 4759746 bytes, checksum: 2dda9e543c6ad22f7350181716aeda1b (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 iii Abstract xiii 1 Introduction 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Brain Computer Interface . . . . . . . . . . . . . . . . . . . 5 1.3 Characteristics of EEG Signal Processing . . . . . . . . . . . 6 1.3.1 EEG Formation . . . . . . . . . . . . . . . . . . . . . 6 1.3.2 EEG Signal Characteristics . . . . . . . . . . . . . . 7 1.3.3 Brain Rhythms . . . . . . . . . . . . . . . . . . . . . 7 1.3.4 EEG Recording and Measurement . . . . . . . . . . . 8 1.4 Neuron Signal and Spike Sorting . . . . . . . . . . . . . . . . 9 1.4.1 Neural Recording . . . . . . . . . . . . . . . . . . . . 9 1.4.2 Spike . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.4.3 Spike Sorting . . . . . . . . . . . . . . . . . . . . . . 10 1.5 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . 11 2 EEG-based Motion-related Brain Computer Interface 13 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.1 Stimulus-Driven BCI . . . . . . . . . . . . . . . . . . 14 2.1.2 User-Driven BCI . . . . . . . . . . . . . . . . . . . . 15 2.2 Design Challenges . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.1 Reduction of Training Data Collection Time . . . . . 19 2.2.2 Evolution of Training Data Collection Time . . . . . 20 2.2.3 The Problem of Existing System . . . . . . . . . . . 20 2.3 Proposed Unsupervised On-line Learning System for EEG- based Motion-related BCI . . . . . . . . . . . . . . . . . . . 22 2.3.1 The Foundation of the Proposed System and Simula- tion Environment . . . . . . . . . . . . . . . . . . . . 22 2.3.2 Exploration of Reusing the Pre-recorded Training Data Set to Improve the Supervised Classi‾er for EEG- based Motor-related BCI . . . . . . . . . . . . . . . . 24 2.3.3 Unsupervised On-line Learning Scheme . . . . . . . . 30 2.3.4 Simulation Results . . . . . . . . . . . . . . . . . . . 34 2.4 Demo of EEG-based Motion-related Brain Computer Interface 36 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3 Analysis and Implementation of Cubic Spline Interpolation for Spike Sorting Microsystems 45 3.1 Cortically-Controlled BCI and Spike Sorting Microsystem . . 45 3.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.1 Sampling Skew and Waveform Distortion . . . . . . . 47 3.2.2 Power and Accuracy TradeoR . . . . . . . . . . . . . 48 3.3 The Proposed Spike Sorting Microsystems With Interpolation 50 3.3.1 Interpolation . . . . . . . . . . . . . . . . . . . . . . 50 3.3.2 Proposed OR-site Spike Sorter with Interpolation . . 50 3.3.3 Proposed On-chip Spike Sorter with Interpolation . . 51 3.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 53 3.4.1 Simulation Environment . . . . . . . . . . . . . . . . 53 3.4.2 Simulation Results . . . . . . . . . . . . . . . . . . . 54 3.5 Hardware Architecture Design and Implementation . . . . . 57 3.5.1 Review of Cubic Spline Interpolation Algorithm . . . 57 3.5.2 Proposed Architecture . . . . . . . . . . . . . . . . . 58 3.5.3 Implementation Results . . . . . . . . . . . . . . . . 65 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4 Conclusion 69 Bibliography 71 | |
| dc.language.iso | en | |
| dc.subject | 腦機介面 | zh_TW |
| dc.subject | 線上學習系統 | zh_TW |
| dc.subject | 腦電訊號 | zh_TW |
| dc.subject | 動作相關 | zh_TW |
| dc.subject | On-line Learning System | en |
| dc.subject | Electroencephalography (EEG) | en |
| dc.subject | Brain Computer Interface | en |
| dc.subject | Motion-related | en |
| dc.title | 應用於腦電訊號暨動作相關腦機介面之非監督式線上學習系統 | zh_TW |
| dc.title | Unsupervised On-line Learning System of Motion-related Brain Computer Interface Based on Electroencephalography (EEG) | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林志隆,林啟萬,陳建中,黃聖傑 | |
| dc.subject.keyword | 腦電訊號,動作相關,腦機介面,線上學習系統, | zh_TW |
| dc.subject.keyword | On-line Learning System,Motion-related,Brain Computer Interface,Electroencephalography (EEG), | en |
| dc.relation.page | 77 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2011-08-21 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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