學術出版;;Publications

Permanent URI for this communityhdl:11536/1

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.

Browse

🏛️國立陽明交通大學機構典藏

Search Results

Now showing 1 - 10 of 301

How Does Gender Stereotype Affect the Memory of Advertisements? A Behavioral and Electroencephalography Study
(2020-07-14)
Lo, Shih-Yu
;
King, Jung-Tai
;
Lin, Chin-Teng
;
傳播研究所
;
通識教育中心
Previous studies have shown equivocal results about whether atypical or unusual events, compared with typical ones, facilitate or inhibit memory. We suspect that the indefinite findings could be partly due to the recall task used in these studies, as the participants might have used inference instead of recall in their responses. In the present study, we tested the recognition memory for real (Experiment 1) and fabricated (Experiment 2) advertisements, which could be congruent or incongruent with gender stereotypes. In congruent advertisements, a female endorser presented a traditionally considered feminine product or a male endorser presented a traditionally considered masculine product, whereas the gender-product type matching reversed in incongruent advertisements. The results of both behavioral experiments revealed that the participants' memory performance for stereotype-incongruent advertisements was higher than for congruent ones. In the event-related potential (ERP) recordings in Experiment 3, larger positive amplitudes were found for stereotype-incongruent advertisements than for congruent advertisements on the left parietal sites, suggesting a deeper encoding process for stereotype-incongruent information than for stereotype-congruent information.
Long-term ambient hydrocarbons exposure and incidence of ischemic stroke
(2019-12-04)
Zhang, Han-Wei
;
Kok, Victor C.
;
Chuang, Shu-Chun
;
Tseng, Chun-Hung
;
Lin, Chin-Teng
Exposure to air pollutants is known to have adverse effects on human health; however, little is known about the association between hydrocarbons in air and an ischemic stroke (IS) event. We investigated whether long-term exposure to airborne hydrocarbons, including volatile organic compounds, increased IS risk. This retrospective cohort study included 283,666 people aged 40 years or older in Taiwan. Cox proportional hazards regression analysis was used to fit single- and multiple-pollutant models for two targeted pollutants, total hydrocarbons (THC) and nonmethane hydrocarbons (NMHC), and estimated the risk of IS. Before controlling for multiple pollutants, hazard ratios (HRs) of IS with 95% confidence intervals for the overall population were 2.69 (2.64-2.74) at 0.16-ppm increase in THC and 1.62 (1.59-1.66) at 0.11-ppm increase in NMHC. For the multiple-pollutant models controlling for PM2.5, the adjusted HR was 3.64 (3.56-3.72) for THC and 2.21 (2.16-2.26) for NMHC. Our findings suggest that long-term exposure to THC and NMHC may be a risk factor for IS development.
Augmented Wire-Embedded Silicon-Based Dry-Contact Sensors for Electroencephalography Signal Measurements
(2020-04-01)
Lin, Chin-Teng
;
Yu, Yi-Hsin
;
King, Jung-Tai
;
Liu, Chi-Hsien
;
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.
Long-Term Exposure to Ambient Hydrocarbons Increases Dementia Risk in People Aged 50 Years and above in Taiwan
(2019-01-01)
Zhang, Han-Wei
;
Kok, Victor C.
;
Chuang, Shu-Chun
;
Tseng, Chun-Hung
;
Lin, Chin-Teng
Background: Alzheimer's disease, the most common cause of dementia among the elderly, is a progressive and irreversible neurodegenerative disease. Exposure to air pollutants is known to have adverse effects on human health, however, little is known about hydrocarbons in the air that can trigger a dementia event. Objective: We aimed to investigate whether long-term exposure to airborne hydrocarbons increases the risk of developing dementia. Method: The present cohort study included 178,085 people aged 50 years and older in Taiwan. Cox proportional hazards regression analysis was used to fit the multiple pollutant models for two targeted pollutants, including total hydrocarbons and non-methane hydrocarbons, and estimated the risk of dementia. Results: Before controlling for multiple pollutants, hazard ratios with 95% confidence intervals for the overall population were 7.63 (7.28-7.99, p <0.001) at a 0.51 -ppm increases in total hydrocarbons, and 2.94 (2.82-3.05, p <0.001) at a 0.32-ppm increases in non-methane hydrocarbons. The highest adjusted hazard ratios for different multiple-pollutant models of each targeted pollutant were statistically significant (p <0.001) for all patients: 11.52 (10.86-12.24) for total hydrocarbons and 9.73 (9.18-10.32) for non-methane hydrocarbons. Conclusion: Our findings suggest that total hydrocarbons and non-methane hydrocarbons may be contributing to dementia development.
Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements
(2020-01-01)
Lin, Chin-Teng
;
King, Jung-Tai
;
Chuang, Chun-Hsiang
;
Ding, Weiping
;
Chuang, Wei-Yu
Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO(2)) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO(2) decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO(2) oscillations suggest that activations in HbO(2) are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving.
Temporal EEG Imaging for Drowsy Driving Prediction
(2019-12-01)
Cheng, Eric Juwei
;
Young, Ku-Young
;
Lin, Chin-Teng
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
As a major cause of vehicle accidents, the prevention of drowsy driving has received increasing public attention. Precisely identifying the drowsy state of drivers is difficult since it is an ambiguous event that does not occur at a single point in time. In this paper, we use an electroencephalography (EEG) image-based method to estimate the drowsiness state of drivers. The driver's EEG measurement is transformed into an RGB image that contains the spatial knowledge of the EEG. Moreover, for considering the temporal behavior of the data, we generate these images using the EEG data over a sequence of time points. The generated EEG images are passed into a convolutional neural network (CNN) to perform the prediction task. In the experiment, the proposed method is compared with an EEG image generated from a single data time point, and the results indicate that the approach of combining EEG images in multiple time points is able to improve the performance for drowsiness prediction.
A self-adaptive artificial bee colony algorithm with local search for TSK-type neuro-fuzzy system training
(2019-01-01)
Chou, Kuang-Pen
;
Lin, Chin-Teng
;
Lin, Wen-Chieh
;
資訊工程學系
;
Department of Computer Science
In this paper, we introduce a self-adaptive artificial bee colony (ABC) algorithm for learning the parameters of a Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy system (NFS). The proposed NFS learns fuzzy rules for the premise part of the fuzzy system using an adaptive clustering method according to the input-output data at hand for establishing the network structure. All the free parameters in the NFS, including the premise and the following TSK-type consequent parameters, are optimized by the modified ABC (MABC) algorithm. Experiments involve two parts, including numerical optimization problems and dynamic system identification problems. In the first part of investigations, the proposed MABC compares to the standard ABC on mathematical optimization problems. In the remaining experiments, the performance of the proposed method is verified with other metaheuristic methods, including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and standard ABC, to evaluate the effectiveness and feasibility of the system. The simulation results show that the proposed method provides better approximation results than those obtained by competitors methods.
Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients
(2020-01-01)
Cao, Zehong
;
Lin, Chin-Teng
;
Lai, Kuan-Lin
;
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.
A fast fused part-based model with new deep feature for pedestrian detection and security monitoring
(2020-02-01)
Cheng, Eric Juwei
;
Prasad, Mukesh
;
Yang, Jie
;
Khanna, Pritee
;
Chen, Bing-Hong
In recent years, pedestrian detection based on computer vision has been widely used in intelligent transportation, security monitoring, assistance driving and other related applications. However, one of the remaining open challenges is that pedestrians are partially obscured and their posture changes. To address this problem, deformable part model (DPM) uses a mixture of part filters to capture variation in view point and appearance and achieves success for challenging datasets. Nevertheless, the expensive computation cost of DPM limits its ability in the real-time application. This study propose a fast fused part-based model (FFPM) for pedestrian detection to detect the pedestrians efficiently and accurately in the crowded environment. The first step of the proposed method trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. These six response feature maps are combined with full-body model to produce spatial deep features. The second step of the proposed method uses the deep features as an input to support vector machine (SVM) to detect pedestrian. A variety of strategies is introduced in the proposed model, including part-based to full-body method, spatial filtering, and multi-ratios combination. Experiment results show that the proposed FFPM method improves the computation speed of DPM and maintains the performance in detection. (C) 2019 Elsevier Ltd. All rights reserved.
Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems
(2019-11-01)
Lin, Chin-Teng
;
Liu, Chi-Hsien
;
Wang, Po-Sheng
;
King, Jung-Tai
;
Liao, Lun-De
A brain-computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.

學術出版;;Publications

Browse

Filters

Settings

Sort By

Results per page

🏛️國立陽明交通大學機構典藏

Search Results