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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

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Now showing 1 - 10 of 11
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    Augmented Wire-Embedded Silicon-Based Dry-Contact Sensors for Electroencephalography Signal Measurements
    (2020-04-01)
    Lin, Chin-Teng
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    Yu, Yi-Hsin
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    King, Jung-Tai
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    Liu, Chi-Hsien
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    Liao, Lun-De
    The aim of this study was to develop a novel dry electroencePhalography (EEG) sensor with a soft, pliable pad that conforms to the contours of the skin and skull providing a suitable surface contact area for collecting elec: trical potential signals and ensuring a reliable connection. In this study, based on our experience in developing flexible silver/silicon-based dry-contact sensors (SBDSs) for biognal measurements, we proposed a new, augmented weirle-embedded silicon-based dry-contact sensor (WSBDS) with a long lifespan and better performance in EEG measurements. The following two augmentation concepts were proposed in this design and implemented in fabrication: 1) the addition of a metal stud and 2) the embedding of copper wires into the fingers of an acicular SBDS. The forehead sensor is suitable for forehead EEG measurements, and the acicular sensor is designed for application to hair-covered sites, where it can overcome hair interference to achieve satisfactory scalp contact while maintaining low impedance at the skin-electrode interface. Finally, thisi faugm!nted WSBDS performed well in human EEG recording in a designed brain-computer interface (BCI) experiment and is easiblefor practical applications.
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    Classification of Brain Activities under Response Inhibition using Functional Connectivity
    (2019-01-01)
    Chikara, Rupesh Kumar
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    Su, Kai-Hisang
    ;
    Ko, Li-Wei
    ;
    交大名義發表
    ;
    生物科技學系
    Human response inhibition, the ability to suppress pre-potent behavior that is inappropriate or that is no longer required, is essential for behavior aimed at everyday life goals. In recent decades, researchers have shown a greater interest in inhibiting the response. Response inhibition is considered an operationalization of certain aspects of impulsivity and compulsivity. Impulsivity is commonly defined as a tendency to act by impulses, acts performed immediately and without voluntary control, while compulsivity is the tendency to repeat a specific behavior and to be unable to inhibit the behavior even when it is no longer appropriate. Due to the importance of inhibiting the response in everyday life, many neuropsychological models have been developed to test inhibitory performance. In this experiment, subjects were asked to respond to a visual stimulus (i.e., a square or circular figure), but withheld this response to irrelevant stimuli (i.e., alarm sound). Therefore, in this study, we developed a new classification model of brain activities under response inhibition using functional connectivity. In this proposed model, we classified the electroencephalography (EEG) signals of successful-stop and failed-stop trials. In this study, we used parametric (LDA, QDA) and non-parametric (PARZENDC, KNNC) classification algorithms to investigate the accuracy of this system. First, the EEG signals acquired during the successful-stop and failed-stop trials, after that, we measured the functional connectivity between seven brain regions, included F3-F4, F4-O1, F4-T8, T7-O1, T7-T8, C3-CZ and C4CZ by phase-locked value (PLV) method. These seven regions of interest covered the entire human brain. These functional connectivity values were used as an input feature for the proposed classification model. Our novel classification system achieved 88% accuracy with QDA. These new findings revealed that this model can be considered to classify the EEG signals of successful-stop and failed-stop trials during response inhibition.
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    Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients
    (2020-01-01)
    Cao, Zehong
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    Lin, Chin-Teng
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    Lai, Kuan-Lin
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    Ko, Li-Wei
    ;
    King, Jung-Tai
    Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity reflecting the robustness of brain systems. In this study, we present a novel application of multiscale relative inherent fuzzy entropy using repetitive steady-state visual evoked potentials (SSVEPs) to investigate EEG complexity change between two migraine phases, i.e., interictal (baseline) and preictal (before migraine attacks) phases. We used a wearable headband EEG device with O1, Oz, O2, and Fpz electrodes to collect EEG signals from 80 participants [40 migraine patients and 40 healthy controls (HCs)] under the following two conditions: During resting state and SSVEPs with five 15-Hz photic stimuli. We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the interictal phase, but a reverse trend in patients in the preictal phase. In the 1st SSVEP, occipital EEG entropy of the HCs was significantly lower than that of patents in the preictal phase (FDR-adjusted p < 0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the preictal phase exhibited significantly lower values than patients in the interictal phase (FDR-adjusted p < 0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81 $ \pm $ 6 & x0025; and area under the curve of 0.87 for classifying interictal and preictal phases. In contrast, there were no differences in EEG entropy among groups or sessions by using other competing entropy models, including approximate entropy, sample entropy, and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy offers novel applications in visual stimulus environments and may have the potential to provide a preictal alert to migraine patients.
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    EEG Dynamics in Inhibition of Left-hand and Right-hand Responses during Auditory Stop-Signal Task
    (2014)
    Chikara, Rupesh Kumar
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    Perumal, Ramesh
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    Ko, Li-Wei
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    Chen, Hsin
    ;
    生物科技學系
    An experimental design is programmed using the presentation tool to investigate the global response inhibition process by quantifying the parameters such as inhibition efficiency, stop-signal delay (SSD) and stop-signal reaction time (SSRT) in the stop-signal paradigm. The aim of this study is to explore the response inhibition mechanisms in the left-hand and right-hand responses by using ERP and ERSP results obtained from the EEG data of different subjects. The inhibition efficiency of the right-hand response and left-hand response appears to be independent of each other as there is no significant difference between them. From these results, the inhibition mechanisms corresponding to these two regions of the brain may be viewed as statistically independent processes. Further, we inferred that the response inhibition mechanisms for both left-hand and right-hand responses have approximately the same spectral power observation analysis and we conclude that these processes are statistically independent of each other.
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    A Motor Imagery Based Brain-Computer Interface System via Swarm-Optimized Fuzzy Integral and Its Application
    (2016)
    Wu, Shang-Lin
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    Liu, Yu-Ting
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    Chou, Kuang-Pen
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    Lin, Yang-Yin
    ;
    Lu, Jie
    A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.
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    Driving Fatigue Prediction with Pre-Event Electroencephalography (EEG) via a Recurrent Fuzzy Neural Network
    (2016)
    Liu, Yu-Ting
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    Wu, Shang-Lin
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    Chou, Kuang-Pen
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    Lin, Yang-Yin
    ;
    Lu, Jie
    We propose an electroencephalography (EEG) prediction system based on a recurrent fuzzy neural network (RFNN) architecture to assess drivers\' fatigue degrees during a virtual-reality (VR) dynamic driving environment. Prediction of fatigue degrees is a crucial and arduous biomedical issue for driving safety, which has attracted growing attention of the research community in the recent past. Meanwhile, combined with the benefits of measuring EEG signals facilitates, many EEG-based brain-computer interfaces (BCIs) have been developed for use in real-time mental assessment. In the literature, EEG signals are severely blended with stochastic noise; therefore, the performance of BCIs is constrained by low resolution in recognition tasks. For this rationale, independent component analysis (ICA) is usually used to find a source mapping from original data that has been blended with unrelated artificial noise. However, the mechanism of ICA cannot be used in real-time BCI design. To overcome this bottleneck, the proposed system in this paper utilizes a recurrent self-evolving fuzzy neural work (RSEFNN) to increase memory capability for adaptive noise cancellation when assessing drivers\' mental states during a car driving task. The experimental results without the use of ICA procedure indicate that the proposed RSEFNN model remains superior performance compared with the state-of-thearts models.
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    EEG-based Driving Fatigue Prediction System Using Functional-link-based Fuzzy Neural Network
    (2014)
    Liu, Yu-Ting
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    Lin, Yang-Yin
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    Wu, Shang-Lin
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    Chuang, Chun-Hsiang
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    Prasad, Mukesh
    This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a non-linear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers\' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.
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    Assessment of Mental Fatigue: An EEG-based Forecasting System for Driving Safety
    (2015-01-01)
    Liu, Yu-Ting
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    Lin, Yang-Yin
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    Wu, Shang-Lin
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    Hsieh, Tsung-Yu
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    Lin, Chin-Teng
    This study proposes an EEG-based forecasting system based on a functional-link recurrent self-evolving fuzzy neural network (FL-RSEFNN) for assessing mental fatigue during a highway driving task. Drivers\' cognitive states significantly affect driving safety, especially for fatigue or drowsy driving which is one of common factors to endanger individuals and the public safety. In this study, a FL-RSEFNN employs an on-line gradient descent (GD) learning rule to address the EEG regression problem in brain dynamics for estimation of driving fatigue. We analyze brain dynamics in a car driving task, which is constructed in a simulated virtual reality (VR) environment. The EEG-based forecasting system is evaluated using the generalized cross-subject approach, and the results indicate that the FL-RSEFNN is superior to state-of-the-art models regardless of the use of recurrent or non-recurrent structures.
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    次世代智慧型加護病房照護系統-子計畫四：應用於次世代智慧型ICU之低功耗多核生醫信號處理系統晶片平台設計( I )
    (2011)
    方偉騏
    ;
    Fang Wai-Chi
    ;
    國立交通大學電子工程學系及電子研究所
    本計畫將提出應用於次世代智慧型ICU 之低功耗多核生醫信號處理系統晶片平台設計，此計畫包含心電訊號處理器、腦訊號處理器、以及生醫影像處理器等關鍵技術。心電訊號處理器包含心律分析以及心肌梗塞偵測等12 導層信號處理引擎。而腦波屬於相當微弱且易受干擾的信號，因此本計畫將提出使用於64 通道腦波信號處理器之獨立成分分析系統。在擴散光學斷層掃描的部分，則是提出一種新式的光學掃描方式，透過此新式的掃描架構是一種可以達到在人體上移動偵測，並整合移動過程中掃描到的圖像進行綜合判讀的技術。而為達到極低功率消耗的目標，本計畫將對系統各部份進行功耗分析，並利用Cadence 工具及研發團隊自主設計之極低功率元件庫使晶片達到最低功耗的特性有效提升前端生醫感測系統的可攜性及可穿戴性，並且使用高階封裝技術技術有效減少晶片體積、改善可靠度及測試品質、並達到異質整合成果。最後，本計畫所產出的低功耗多核生醫信號處理系統晶片平台將與其他子計畫進行整合，並完成整體系統架構，並進行驗證。
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    以生理監測系統為基礎之合成化戰場作戰模擬訓練成效分析
    (2013)
    林進燈
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    Chin Teng, Lin
    ;
    國立交通大學電機與控制工程學系
    軍事作戰中，決策判斷是非常重要的能力，一個正確的決策可為團隊帶來極度正面的報酬與成就感，一個錯誤的決策可能帶來非常嚴重、萬劫不復的後果。因此每一個決策都是至關重要，除了對個體的影響外，也會影響到整個團隊的運作。決策同時會伴隨著壓力的產生，不論是處於好的或是壞的壓力，都會影響人在進行決策時的判斷。當處於不適合的高壓力之下，突如其來的決策問題常常會讓人下達不適合的決策，但有時候適當的壓力也會讓人判斷出適合的決策。根據研究發現當一個人在壓力之下，其相對應的認知表現和決策可能會受到不利的影響，特別是在某些條件的壓力之下，此個體可能會1. 屏除外在的刺激(Easterbrook, 1959; Janis & Mann, 1977; Staw, Sandelands & Dutton, 1981)，2. 藉由啟發式法則(也就是經驗法則)進行決策(Shaham, Singer, & Schaeffer, 1992; Klein, 1996)，3. 遭遇到表現的剛性或是狹隘的思想(Friedman and Mann, 1993; Keinan, 1987)，4. 失去個人分析複雜情況和處理訊息的能力(Larsen, 2001)。也有研究發現，在壓力之下任務完成的時間有可能會增加且完成的準確性則會降低(Idzikowski & Baddeley, 1983; McLeod, 1977)。 壓力除了對個體的影響外，也被證明會對團隊的運作造成負面的影響。當在壓力之下，個體可能會去失去自我掌控權並且讓權力更集中於階層的最上級，而且有效的溝通也將會減少(Driskell, Carson & Moskal, 1988)。壓力也可以引出群體思維(也就是群體中的成員會將會忽略掉對他們自我重要的訊息)強制所有的成員堅持一致的決策甚至是錯誤的決策，並且合理化不好的決策(Janis & Mann, 1977)。在人類的生活之中，為了適應環境所帶來的變化或是做出事件的決策，對動作進行抑制控制(inhibitory control)顯得非常重要，許多決策判斷都不是經常性的發生，面臨在短時間狀況下的壓力，將會影響人在進行策判斷\抑制控制的能力。在軍事作戰中，壓力、決策判斷\抑制控制是息息相關的，因為軍事作戰中，常常在關鍵事件發生後，必須要做出正確的決定，決策判斷卻會被壓力所影響，往往可能由於一瞬間的誤判，造成無法挽回的結果。例如:當有歹徒出現在面前時，是必須要馬上做出開槍反應，但是若是歹徒手邊握有人質時，開槍的動作就必須馬上停止，此就是所謂抑制控制。然而，若我們在此事件當中加入需要限制反應時間，無形中加入壓力的心理因素，那麼對於此事件反應中，是否可以適時的作出對的決策呢? 當人類執行決策判斷時，大腦的神經細胞會有同步(synchronization)活化的現象，此現象反映在腦波上會產生某頻帶腦波能量增強的結果，此外隨決策判斷反應時間的縮短，腦波的主要活化區域有逐漸提高的趨勢。因此，藉由腦波訊號的量測與擷取，經由訊號分析後可觀察腦波能量上的改變，便可評判決策判斷能力。在決策判斷/抑制控制研究中，發現在面臨短時間狀況下的壓力是影響決策判斷的一大因素。針對壓力的心理狀況，除了一般實驗中所設計的心理量表外，目前已有針對腦波與心電(ECG)訊號的方式來研究壓力等議題，就心電訊號心率變異（heart rate variability, HRV）的基本概念是認為人體的壓力主要有兩個部份，一個是生理的壓力，一個是心理的壓力；這兩種壓力是否處於放鬆且協調的狀態，可以從心跳速率與呼吸頻率窺知一二。就腦波而言，壓力和腦部前額(prefrontal)區的腦波(特別是α波)有很高的相關性，正向(positive)情緒(例如，喜悅、高興、有興趣)涉及大腦左前額的活耀，負向(negative)情緒(例如，恐懼、悲傷、厭惡)和右前額活耀有關。即便在「靜止(resting state)狀態」下(亦即，受試者不做任何作業或思考)，這種「前額α腦波不對稱性」仍然明顯存在(Davidson, 2000; Fox, 1991)。除了情緒，α腦波的腦前額不對稱性亦可反映出一個人的性格，例如，外向者的左前額腦波比內向者有較強的活躍量，內向、害羞者的右前額腦則有較強的活躍量。 目前對於決策判斷/抑制控制有關的研究，常使用傳統心理學實驗訊號終止任務(stop-signal task)作為典範，然而在傳統訊號終止任務場景中，為避免其他因素干擾，通常利用簡單符號做為刺激(例如：以圈作為反應訊號及以叉作為決策判斷/抑制訊號)，然而在實際生活中卻無法避免這些干擾因素，因此本計畫利用以虛擬場景(射擊訓練)來模擬訊號終止任務，以實際應用於現實生活中，並加入以時間作為壓力來源。此外，目前在神經科學中，常使用腦波與心電訊號技術作為研究方法，目前此技術發展均已臻至成熟，故本計畫將運用交通大學在訊號處理上的優勢，結合腦波與心電訊號技術的優點來探討在壓力生理狀況作業環境下大腦決策判斷與心電訊號的變化。 在此計畫中，先是透過傳統研究型Neuroscan系統來量測的EEG和ECG訊號，並於國防部指定配合單位或是交通大學腦科學研究中心的虛擬動態實驗室進行虛擬場景(射擊訓練)實驗，探討壓力在腦波/心率變異的差異，探討決策表現好和不好之間在腦波EEG與心率變異度上或是決策上的差異，進而建立專屬決策判斷/抑制控制力腦波與心電模組(model)，故不論是在訓練和監測決策判斷/抑制控制能力，或是分析壓力指標等，未來都有很多市場應用的空間。在建立為該模組後，可結合交通大學腦中心所開發之具有高便利性與實用性的可攜式無線EEG 腦機/ECG心電介面裝置，將此研發系統運用於空軍飛行員獲陸軍射擊手，可幫助國軍分析受訓人員於空軍戰機模擬器(如M2000或F16或IDF)及陸軍裝甲兵學校戰車訓練模擬器進行國軍合成化戰場環境下，作戰訓練優異人員之身、心狀態，進而分析易失事學員之生、心狀態與作戰反應即時訊號分析，深入瞭解作戰成敗之生理狀況，以精進整體合成化戰場環境訓練成效。