評估注意力偵測之穿戴式腦電電極放置有效性

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姓名

王俊隆(Jun-Long Wang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

評估注意力偵測之穿戴式腦電電極放置有效性
(Evaluating the effectiveness of wearable EEG electrode placement for attention detection)

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摘要(中)

多動症的問題在世界越來越嚴重。注意力檢測問題與對多動症患者的評
估密切相關，它決定了受試者的集中程度。人腦的額葉和前額葉決定受試者
的注意力。其中，反應抑制是注意力的關鍵。它可以控制衝動的反應。在本
實驗中，我們進行了與反應抑制有關的任務，並收集了正常成年人的數據，
使用可穿戴式腦電設備OpenBCI Ganglion，集成並結合VR 一體機進行實
驗，並結合之前研究的腦電（Looxid）電極。根據國際10-20 系統的腦電定
位電極，對額頭的AF4、FP7、FP8、AF7、AF8 進行了比較，通過機器學習
驗證了目前應用於特定三點的可穿戴腦電設備FCz、FC3、Pz 在注意力檢測
方面的有效性。此外，我們還通過特徵探索了EEG 和眼球追踪之間的相關
性。最後，我們成功地確定了我們的EEG 電極位置的更好的有效性。

摘要(英)

The problem of ADHD is getting more and more serious in the world. The
attention detection problem is closely related to the assessment of ADHD patients
that determines how concentrated the subject is. The frontal and prefrontal of the
human brain decide the subjects’ attention. Among them, response inhibition is
the key to attention. It can control impulsive responses. In this experiment, we
conduct a task related to response inhibition and we collected the data from
normal adults, and use the wearable EEG device OpenBCI Ganglion, integrated
and combined with the VR all-in-one machine to conduct the experiment, and
combined with the EEG (Looxid) electrode of the previous study. According to
the international 10-20 system for EEG location electrodes, AF4, FP7, FP8, AF7,
and AF8 in the forehead are compared, and we validate the effectiveness of the
current wearable EEG devices that applied to specific three points are FCz, FC3,
and Pz in attention detection through Machine Learning. Moreover, the
correlation between EEG and eye-tracking is explored through features. Finally,
we successfully determine the better effectiveness of our EEG electrode
placement.

關鍵字(中)

★ 腦電圖
★ 數據融合
★ 多模態數據
★ 虛擬現實
★ 機器學習
★ 眼睛追蹤

關鍵字(英)

★ Electroencephalography
★ Data Fusion
★ Multimodal Data
★ Virtual reality
★ Machine Learning
★ Eye-tracking

論文目次

Table of Contents
摘要
I
Table of Contents IV
List of Figures V
List of Tables VII
Introduction 1
Related Works 8
Go/NoGO task 8
EEG/Eye correlation 8
Attention Detection 9
Methodologies 11
A. Experiment 11
B. Data Preprocess and Features Extraction 15
C. Correlation Coefficient 18
D. Machine Learning Method 18
Results 22
Conclusion and Discussions 27
Reference 28

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指導教授

葉士青吳曉光(Shih-Ching Yeh Hsiao-Kuang Wu)

審核日期

2022-8-22

推文