Browsing by Author "Lu, Yi-Chen"

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A method of layer-by-layer gold nanoparticle hybridization in a quartz crystal microbalance DNA sensing system used to detect dengue virus
(2009-05-27)
Chen, Sz-Hau
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Chuang, Yao-Chen
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Lu, Yi-Chen
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Lin, Hsiu-Chao
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Yang, Yun-Liang
Dengue virus (DENV) is nowadays the most important arthropod-spread virus affecting humans existing in more than 100 countries worldwide. A rapid and sensitive detection method for the early diagnosis of infectious dengue virus urgently needs to be developed. In the present study, a circulating-flow quartz crystal microbalance (QCM) biosensing method combining oligonucleotide-functionalized gold nanoparticles (i.e. AuNP probes) used to detect DENV has been established. In the DNA-QCM method, two kinds of specific AuNP probes were linked by the target sequences onto the QCM chip to amplify the detection signal, i.e. oscillatory frequency change (Delta F) of the QCM sensor. The target sequences amplified from the DENV genome act as a bridge for the layer-by-layer AuNP probes' hybridization in the method. Besides being amplifiers of the detection signal, the specific AuNP probes used in the DNA-QCM method also play the role of verifiers to specifically recognize their target sequences in the detection. The effect of four AuNP sizes on the layer-by-layer hybridization has been evaluated and it is found that 13 nm AuNPs collocated with 13 nm AuNPs showed the best hybridization efficiency. According to the nanoparticle application, the DNA-QCM biosensing method was able to detect dengue viral RNA in virus-contaminated serum as plaque titers being 2 PFU ml(-1) and a linear correlation (R(2) = 0.987) of Delta F versus virus titration from 2 x 10(0) to 2 x 10(6) PFU ml(-1) was found. The sensitivity and specificity of the present DNA-QCM method with nanoparticle technology showed it to be comparable to the fluorescent real-time PCR methods. Moreover, the method described herein was shown to not require expensive equipment, was label-free and highly sensitive.
An Energy-Efficient Multicast Protocol for ZigBee-based Networks
(2016)
Hu, Shu-Chiung
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Tsai, Chia-Hung
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Lu, Yi-Chen
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Pan, Meng-Shiuan
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Tseng, Yu-Chee
Recently, many Internet of Things (IoT) or wireless sensor network (WSN) applications adopt ZigBee as their communication protocol. In these applications, messages may need to be disseminated to some specific objects or nodes using multicast transmissions. However, we observe that the original ZigBee multicast protocol causes extremely high packet overhead and energy consumption. In this work, we propose a ZigBee-compatible energy efficient multicast protocol, which allows nodes to execute the designed procedures in a distributed manner. By our scheme, each node uses the designed maintenance module to manage its neighbors. When multicasting, a node utilizes the designed multicasting module to compete for being a relay node by the proposed backoff mechanism. In addition, each node overhears its neighbors\' broadcasts to avoid unnecessary transmissions and to ensure that the transmitted multicast packets are successfully received. Our results indicate that the proposed protocol can indeed lengthen network lifetime, reduce redundant packets, and preserve network reliability.
Disposable amperometric immunosensing strips fabricated by Au nanoparticles-modified screen-printed carbon electrodes for the detection of foodborne pathogen Escherichia coli O157 : H7
(2008-07-15)
Lin, Yueh-Hui
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Chen, Sz-Hau
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Chuang, Yao-Chen
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Lu, Yi-Chen
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Shen, Thomas Y.
A disposable amperometric immunosensing strip was fabricated for rapid detection of Escherichia coli O157:H7. The method uses an indirect sandwich enzyme-linked immunoassay with double antibodies. Screen-printed carbon electrodes (SPCEs) were framed by commercial silver and carbon inks. For electrochemical characterization the carbon electrodes were coupled with the first E. coli O157:H7-specific antibody, E. coli O157:H7 intact cells and the second E. coli O157:H7-specific antibody conjugated with horseradish peroxidase (HRP). Hydrogen peroxide and ferrocenedicarboxylic acid (FeDC) were used as the substrate for HRP and mediator, respectively, at a potential +300 mV vs. counter/reference electrode. The response current (RC) of the immunosensing strips could be amplified significantly by 13-nm diameter Au nanoparticles (AuNPs) attached to the working electrode. The results show that the combined effects of AuNPs and FeDC enhanced RC by 13.1-fold. The SPCE immunosensing strips were used to detect E. coli O157:H7 specifically. Concentrations of E. coli O157:H7 from 10(2) to 10(7) CFU/ml could be detected. The detection limit was approximately 6 CFU/strip in PBS buffer and 50 CFU/strip in milk. The SPCE modified with AuNPs and FeDC has the potential for further applications and provides the basis for incorporating the method into an integrated system for rapid pathogen detection. (c) 2008 Elsevier B.V. All rights reserved.
Monetary Reward and Punishment to Response Inhibition Modulate Activation and Synchronization Within the Inhibitory Brain Network
(2018-03-01)
Chikara, Rupesh K.
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Chang, Erik C.
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Lu, Yi-Chen
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Lin, Dar-Shong
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Lin, Chin-Teng
A reward or punishment can modulate motivation and emotions, which in turn affect cognitive processing. The present simultaneous functional magnetic resonance imaging-electroencephalography study examines neural mechanisms of response inhibition under the influence of a monetary reward or punishment by implementing a modified stop-signal task in a virtual battlefield scenario. The participants were instructed to play as snipers who open fire at a terrorist target but withhold shooting in the presence of a hostage. The participants performed the task under three different feedback conditions in counterbalanced order: a reward condition where each successfully withheld response added a bonus (i.e., positive feedback) to the startup credit, a punishment condition where each failure in stopping deduced a penalty (i.e., negative feedback), and a no-feedback condition where response outcome had no consequences and served as a control setting. Behaviorally both reward and punishment conditions led to significantly down-regulated inhibitory function in terms of the critical stop-signal delay. As for the neuroimaging results, increased activities were found for the no-feedback condition in regions previously reported to be associated with response inhibition, including the right inferior frontal gyrus and the pre-supplementary motor area. Moreover, higher activation of the lingual gyrus, posterior cingulate gyrus (PCG) and inferior parietal lobule were found in the reward condition, while stronger activation of the precuneus gyrus was found in the punishment condition. The positive feedback was also associated with stronger changes of delta, theta, and alpha synchronization in the PCG than were the negative or no-feedback conditions. These findings depicted the intertwining relationship between response inhibition and motivation networks.
Multimodal Fuzzy Fusion for Enhancing the Motor-Imagery-Based Brain Computer Interface
(2019-02-01)
Ko, Li-Wei
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Lu, Yi-Chen
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Chang, Yang
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Bustince, Humberto
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Fernandez, Javier
Brain-computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain-computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain-computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain-computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain-computer interface commands.
Novel Moisture Retention Sponge Electrodes for Developing a Wireless EEG SSVEP-based BCI System
(2018-01-01)
Ko, Li-Wei
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Chang, Yang
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Wu, Pei-Lun
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Lu, Yi-Chen
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Yeh, Chia-Lung
Brain computer interface (BCI) is a method of communication between human brain and external device. Among the existing BCIs, steady-state visual evoked potential (SSVEP) is a common BCI technique due to its excellent signal-to-noise ratio (SNR) and relative immunity against artifacts. However, it remains some limitations regarding applying such technique in practical scenarios; one of the limitations is the trade-off between wet and dry EEG electrodes. In this study, we proposed a novel moisture retention sponge electrode and an innovative differentiable power algorithm for developing SSVEP-based BCI applications. The test of impedance suggested that the sponge electrode exhibited lower impedance compared with metal electrodes, which is adequate when hair is present. The proposed algorithm also demonstrated better accuracy regarding SSVEP-based BCI application. Adopting both innovations could strengthen the feasibility of the real-world applications of BCI.
Single Channel Wireless EEG Device for Real-Time Fatigue Level Detection
(2015)
Ko, Li-Wei
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Lai, Wei-Kai
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Liang, Wei-Gang
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Chuang, Chun-Hsiang
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Lu, Shao-Wei
Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver\'s drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver\'s fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments.