基於深度學習的虛擬現實腦震盪檢測與融合方法

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謝兆峰(Chao-Feng Hsieh) 查詢紙本館藏

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資訊工程學系

論文名稱

基於深度學習的虛擬現實腦震盪檢測與融合方法
(Deep-Learning-based Virtual Reality Concussion Detection with Fusion Method)

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摘要(中)

在職業球賽中，碰撞和衝突是不可避免的，這很容易導致創傷性腦損
傷，甚至危及生命。這些腦震盪症狀可能會嚴重影響運動員的職業生涯
和發展，而後遺症可能導致永久性損傷。因此，一種即時且有效的測試
或檢測方法是必要的工具。過去大多數的測試方法相對主觀，通常需要
運動員自行報告，這可能易於操縱。過去可以使用視覺誘發電位（VEP）
測試人腦的視覺功能，但需要一個安靜和無干擾的環境來收集數據。腦
震盪患者通常在某些腦部區域有頭部受傷。隨著虛擬現實（VR）和機器
學習（ML）的快速發展，我們利用VR 頭戴式設備創造出一個沉浸式環
境，這個環境可以創造一個無干擾的封閉環境，以更可靠地從患者收集
數據。多模態融合方法可以檢測腦震盪症狀並對應不同的神經功能進行
全面評估，使用腦電圖（EEG）和眼動追蹤傳感器。最近，機器學習和
深度學習方法在發現腦震盪症狀方面應用較少，因此我們進一步研究這
些先進技術。我們提出了一種新穎的腦震盪檢測系統，結果在我們的模
擬數據集中實現了0.95 的準確率。

摘要(英)

In professional ball games, collisions and conflicts are inevitable, which
can easily cause traumatic brain injuries and even life-threatening injuries.
These concussion symptoms can seriously impact a player’s career and
development, and the sequelae can cause permanent damage. Therefore,
an immediate and effective testing or detection method is a necessary tool.
Most of the testing methods in the past were relatively subjective, and they
usually need a self-report for players themselves, which may cause easily
be manipulated. In the past, a visual evoked potential (VEP) can be used
to test the visual function of the human brain but needed a silent and
interference-free environment to collect data. Concussion patients often
have head injuries in some brain sections. With the rapid development of
Virtual Reality (VR) and Machine Learning (ML), we utilize a VR headset
to create an immersive environment that creates an enclosed environment
that is interference-free in order to collect data more reliably from patients.
Multi-modal fusion methods can detect concussion symptoms and make a
comprehensive assessment for different brain sections corresponding with
different neurological functions with EEG and eye-tracking sensors. Recently,
ML and DL methods are less applied to find concussion symptoms,
then we further investigate these with advanced techniques. We propose a novel concussion detection system and the result can achieve an accuracy
of 0.95 in our simulation dataset.

關鍵字(中)

★ 腦震盪
★ 機器學習
★ 融合方法
★ 虛擬現實
★ 輕度創傷性腦損傷
★ 穩態視覺誘發電位
★ 眼動追蹤

關鍵字(英)

★ Concussion
★ Machine Learning
★ Fusion strategy
★ Virtual Reality
★ Mild Traumatic Brain Injury(mTBI)
★ SSVEP
★ Eye Tracking

論文目次

摘要………………………………………………………………………………………………….iii
Abstract ……………………………………………………………………………………….v
誌謝……………………………………………………………………………………………..vii
目錄………………………………………………………………………………………………ix
圖目錄…………………………………………………………………………………………..xi
表目錄…………………………………………………………………………………………xiii
使用符號與定義…………………………………………………………………………..xv
一、Introduction ………………………………………………………………………..1
二、Related work ………………………………………………………………………7
三、Scenario …………………………………………………………………….………11
四、Materials and Methods …………………………………………..………13
4.0.1 System Implementation............................................................13
4.0.2 Hardware ......................................................................................18
4.0.3 Participants..................................................................................18
4.0.4 EEG Data Preprocessing .........................................................19
4.0.5 Eye Data Preprocessing...........................................................21
4.0.6 System Design.............................................................................22
五、Result………………………………………..............................................25
六、Conclusion..............................................................................31
參考文獻.............................................. .............................................33

參考文獻

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指導教授

葉士青吳曉光(Shih-Ching Yeh Hsiao-Kuang Wu)

審核日期

2023-7-26

推文