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研究生:朱璿瑾
研究生(外文):Hsuan-Chin Chu
論文名稱:運用腦波識別專注狀態
論文名稱(外文):Human Attention Recognition Using EEG Signal
指導教授:劉寧漢劉寧漢引用關係
指導教授(外文):Ning-Han Liu
學位類別:碩士
校院名稱:國立屏東科技大學
系所名稱:資訊管理系所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:87
中文關鍵詞:腦波專注狀態辨識支援向量機
外文關鍵詞:ElectroencephalogramAttention StatusRecognitionSupport Vector Machine
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自西元1929年首度發現人類的腦波(Electroencephalogram, EEG)開始,關於腦波的相關研究不斷累積,目前腦波已被廣泛地運用在各個研究領域中,專家、學者皆希望能從中獲得更多新的知識。
人們透過多元的方式獲取知識與經驗,但大部分的人在成長過程中,都是經由就學的方式吸收新知,而學習的過程中,專心(Attention)的意識對於學習的成效有一定程度的影響,因此,學生能不能專心於課堂學習之上,為其學習成功與否的依據,倘若老師能即時知道學生是否專心,將可以適時地提醒學生、改善學習情況。
因此本研究希望透過觀測腦波的方式,辨識出學生在課堂上的專心與非專心(Inattention)狀態。首先,運用可攜性高的科學儀器進行腦波偵測並予以紀錄,接著將收集到腦波數據值,利用人工的方式過濾無效數據,再結合Support Vector Machine(支援向量機, SVM)分類器進行運算、分析,便能辨識出二類型腦波數據值(專心 v.s. 非專心)。
根據本研究之目的,設計了二項實驗,試圖找出受測者專心時的腦波,第一項是利用聆聽英文句子並作答的方式,模擬出專心的情境,另外一項實驗也是利用聆聽英文句子並作答的方式,再加干擾因子,模擬出非專心的情境;最後經過一系列計算和分類,在本研究中正確的辨識率最高可達71.17%。

The research has accumulated continuously when scientist found the EEG (Electroencephalogram) signal since 1929. The expert and researcher expect to obtain more new knowledge from EEG measure in each research domain.
Human got the knowledge mostly from school. They can utterly absorb the contents of course if people pay attention to class i.e., attention is a basis of learning well. If a teacher can find immediately that a student is not concentrative, then the teacher can alert the student to improve the learning attitude at the right moment.
Therefore, this research is expected to recognize the EEG signal as attention or inattention through EEG measure. First, we used the wireless apparatus to measure and record EEG signal. And all the useless data were filtered manually. Finally, we used Support Vector Machine (SVM) classifier to calculate and analyze the EEG data. The classifier can recognize data as two classes i.e., attention and inattention.
According to the aim of this research, we design two experiments and expect to find out the attention signal of EEG from subjects. The first one simulates attention environment that let subject listen to English sentences and answer questions. The other one is under the inattention environment that similar to previous setting, but added an interference with two people talk. According to our experiment results, the attention recognition rate in this research is 71.17%.

摘要 II
Abstract IV
謝誌 VI
表目錄 X
圖目錄 XIV
第一章、緒論 1
1.1 研究動機 1
1.2 研究目的 3
1.3 研究架構 4
第二章、文獻探討 5
2.1 腦波生理資訊 5
2.2 腦波與專注狀態、學習相關之研究 8
2.3 Support Vector Machine(支援向量機,簡稱SVM) 9
第三章、研究方法 12
3.1 簡介 12
3.2 腦波偵測及資料處理 13
3.3 腦波資料運算及辨識 18
第四章、實驗 29
4.1 實驗相關資訊 29
4.2 實驗說明 32
4.3 實驗結果呈現 34
第五章、結論 73
5.1 研究成果 73
5.2 研究貢獻及未來展望 77
參考文獻 78
附錄一 81
附錄二 86
作者簡介 87

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