Browsing by Author "Wang, Yu-Te"

Now showing 1 - 15 of 15

A Wearable Mobile Electrocardiogram Measurement Device with Novel Dry Polymer-based Electrodes
(2010)
Wang, I-Jan
;
Liao, Lun-De
;
Wang, Yu-Te
;
Chen, Chi-Yu
;
Lin, Bor-Shyh
A Wearable Mobile Electrocardiogram Monitoring System (WMEMS), which mainly consists of a wearable Electrocardiogram (ECG) acquisition device, a mobile phone with global positioning system, and a healthcare server, was developed in this study. Most of telemedicine systems for long-term ECG monitoring focus on the application of communication techniques. However, how to monitor long-term ECG state more comfortably in daily life is also an important issue. In this study, a novel dry foam electrode was designed and applied for the wearable ECG acquisition device in our WMEMS. These novel dry foam electrodes without conduction gels can provide good conductivity to acquire ECG signal effectively, and can adapt to irregular skin surface to maintain low skin-electrode impedance and reduce motion artifacts under movement. Therefore, the wearable ECG acquisition device is suitable for long-term ECG monitoring in daily life. Moreover, by combining with wireless communication technique, our WMEMS can monitor patient's heart rate continuously anywhere in the globe if they are under the coverage of GSM cellular network. Experiment results showed that our WMEMS really provides a good system prototype for ECG telemedicine applications.
Augmenting VR/AR Applications with EEG/EOG Monitoring and Oculo-Vestibular Recoupling
(2016-01-01)
Zao, John K.
;
Jung, Tzyy-Ping
;
Chang, Hung-Ming
;
Gan, Tchin-Tze
;
Wang, Yu-Te
Head-mounted virtual reality and augmented reality displays (a. k. a. VR/AR goggles) created a revolutionary multimedia genre that is seeking ever-broadening applications and novel natural human interfaces. Adding neuromonitoring and neurofeedback to this genre is expected to introduce a new dimension to user interaction with the cyber-world. This study presents the development of a Neuromonitoring VR/AR Goggle armed with electroencephalo- gram and electrooculogram sensors, programmable milli-Ampere current stimulators and wireless fog/cloud computing support. Beside of its potential use in mitigating cybersickness, this device may have potential applications in augmented cognition ranging from feedback-controlled perceptual training to on-line learning and virtual social interactions. A prototype of the device has been made from a Samsung Gear VR for S6. This study explains its technical design to ensure precision data sampling, synchronous event marking, real-time signal processing and big data cloud computing support. This study also demonstrates the effective-ness in measuring the event-related potentials during a visual oddball experiment.
Cell-phone Based Drowsiness Monitoring and Management System
(2012)
Wang, Yu-Te
;
Cheng, Chung-Kuan
;
Huang, Kuan-Chih
;
Lin, Chin-Teng
;
Wang, Yijun
In recent years, driving fatigue and cognitive lapses have gained increasing attentions in the fields of public security, especially regarding the safe manipulation of vehicles. Studies have explored the relation between electroencephalogram (EEG) and driving behavior and several of them further proposed effective approaches (e. g., auditory feedback) to arouse subjects from drowsiness. However, these studies were performed under laboratory-oriented configurations using tethered, ponderous EEG equipment. In real environments, the setup of bulky cognitive monitoring equipment with a long-prep time is not feasible. Therefore, this study extends previous laboratory work by developing an on-line Drowsiness Monitoring and Management (DMM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. The DMM system can continuously observe EEG dynamics, deliver arousing feedback to users experiencing momentary cognitive lapses, and assess the efficacy of the feedback in near real-time.
Comparison between the nGoggle and Optical Coherence Tomography for Detecting Glaucoma
(2017-06-01)
Daga, Fabio Bernardi
;
Nakanishi, Masaki
;
Wang, Yu-Te
;
Jung, Tzyy-Ping
;
Zao, John
Detecting Glaucoma With a Portable Brain-Computer Interface for Objective Assessment of Visual Function Loss
(2017-06-01)
Nakanishi, Masaki
;
Wang, Yu-Te
;
Jung, Tzyy-Ping
;
Zao, John K.
;
Chien, Yu-Yi
IMPORTANCE The current assessment of visual field loss in diseases such as glaucoma is affected by the subjectivity of patient responses and the lack of portability of standard perimeters. OBJECTIVE To describe the development and initial validation of a portable brain-computer interface (BCI) for objectively assessing visual function loss. DESIGN, SETTING, AND PARTICIPANTS This case-control study involved 62 eyes of 33 patients with glaucoma and 30 eyes of 17 healthy participants. Glaucoma was diagnosed based on a masked grading of optic disc stereophotographs. All participants underwent testing with a BCI device and standard automated perimetry (SAP) within 3 months. The BCI device integrates wearable, wireless, dry electroencephalogram and electrooculogram systems and a cellphone-based head-mounted display to enable the detection of multifocal steady state visual-evoked potentials associated with visual field stimulation. The performances of global and sectoral multifocal steady state visual-evoked potentials metrics to discriminate glaucomatous from healthy eyes were compared with global and sectoral SAP parameters. The repeatability of the BCI device measurements was assessed by collecting results of repeated testing in 20 eyes of 10 participants with glaucoma for 3 sessions of measurements separated by weekly intervals. MAIN OUTCOMES AND MEASURES Receiver operating characteristic curves summarizing diagnostic accuracy. Intraclass correlation coefficients and coefficients of variation for assessing repeatability. RESULTS Among the 33 participants with glaucoma, 19 (58%) were white, 12 (36%) were black, and 2 (6%) were Asian, while among the 17 participants with healthy eyes, 9 (53%) were white, 8 (47%) were black, and none were Asian. The receiver operating characteristic curve area for the global BCI multifocal steady state visual-evoked potentials parameter was 0.92 (95% CI, 0.86-0.96), which was larger than for SAP mean deviation (area under the curve, 0.81; 95% CI, 0.72-0.90), SAP mean sensitivity (area under the curve, 0.80; 95% CI, 0.69-0.88; P = .03), and SAP pattern standard deviation (area under the curve, 0.77; 95% CI, 0.66-0.87; P = .01). No statistically significant differences were seen for the sectoral measurements between the BCI and SAP. Intraclass coefficients for global and sectoral parameters ranged from 0.74 to 0.92, and mean coefficients of variation ranged from 3.03% to 7.45%. CONCLUSIONS AND RELEVANCE The BCI device may be useful for assessing the electrical brain responses associated with visual field stimulation. The device discriminated eyes with glaucomatous neuropathy from healthy eyes in a clinically based setting. Further studies should investigate the feasibility of the BCI device for home-based testing as well as for detecting visual function loss over time.
Detecting Preperimetric Glaucoma with the nGoggle, a Portable Brain-Computer Interface for Assessing Neural Damage
(2017-06-01)
Nakanishi, Masaki
;
Wang, Yu-Te
;
Daga, Fabio B.
;
Jung, Tzyy-Ping
;
Zao, John
Developing an EEG-based on-line closed-loop lapse detection and mitigation system
(2014-10-13)
Wang, Yu-Te
;
Huang, Kuan-Chih
;
Wei, Chun-Shu
;
Huang, Teng-Yi
;
Ke, Li-Wei
In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects\' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.
Development of a Wearable Mobile Electrocardiogram Monitoring System by Using Novel Dry Foam Electrodes
(2014-09-01)
Tseng, Kevin C.
;
Lin, Bor-Shyh
;
Liao, Lun-De
;
Wang, Yu-Te
;
Wang, Yu-Lin
The development of many telemedicine systems focus on the utility of communication techniques. However, the comfort of long-term Electrocardiogram (ECG) monitoring in daily life is necessary and is also an important unresolved issue. A wearable mobile electrocardiogram monitoring system (WMEMS) is proposed to monitor ECG in this study. It mainly consists of a wearable ECG acquisition device, a mobile phone platform, and a healthcare server. A novel dry foam electrode is also designed and embedded in the wearable ECG acquisition device. The novel dry foam electrode can provide good conductivity to acquire ECG effectively without conduction gels. By using the adapting property of the dry foam electrode, the skin-electrode impedance and the motion artifacts under movement can also be reduced by adapting to irregular skin surface. Moreover, by using the mobile phone as the system platform, the proposed WMEMS can monitor the user\'s ECG state continuously and anywhere in the globe if they are under the coverage of Global System for Mobile communications (GSM) cellular network. Finally, WMEMS is tested for detecting atrial fibrillation in the hospital. It really provides a good performance and is a good prototype for ECG telemedicine applications.
Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology
(2014-06-03)
Zao, John K.
;
Gan, Tchin-Tze
;
You, Chun-Kai
;
Chung, Cheng-En
;
Wang, Yu-Te
EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users\' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson\'s disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.
Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology
(2014-06-03)
Zao, John K.
;
Gan, Tchin-Tze
;
You, Chun-Kai
;
Chung, Cheng-En
;
Wang, Yu-Te
EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.
Pervasive Neuroimaging with Fog Computing and Linked Data
(2016-01-01)
Zao, John K.
;
Gan, Tchin-Tze
;
You, Chun-Kai
;
Mendez, Sergio Jose Rodriguez
;
You, Chieh
A pilot system for performing real-time brain imaging in naturalistic environments have been developed using wireless EEG headsets, motion sensors, smart telephones and ubiquitous computing servers. This paper described its pervasive architecture and introduced its enabling technologies, which include machine-to-machine publish/subscribe protocols, interoperable data/meta-data formats, multi-tier fog/cloud computing infrastructure and semantic linked data web. A live demonstration of this system was first performed at the US Army Research Lab meeting in March 2013. An expanded system capable of supporting real-time brain state classification and continuous model calibration will soon be made available as a web services.
Selective Transfer Learning for EEG-Based Drowsiness Detection
(2015-01-01)
Wei, Chun-Shu
;
Lin, Yuan-Pin
;
Wang, Yu-Te
;
Jung, Tzyy-Ping
;
Bigdely-Shamlo, Nima
On the pathway from laboratory settings to real world environment, a major challenge on the development of a robust electroencephalogram (EEG)-based brain-computer interface (BCI) is to collect a significant amount of informative training data from each individual, which is labor intensive and time-consuming and thereby significantly hinders the applications of BCIs in real-world settings. A possible remedy for this problem is to leverage existing data from other subjects. However, substantial inter-subject variability of human EEG data could deteriorate more than improve the BCI performance. This study proposes a new transfer learning (TL)-based method that exploits a subject\'s pilot data to select auxiliary data from other subjects to enhance the performance of an EEG-based BCI for drowsiness detection. This method is based on our previous findings that the EEG correlates of drowsiness were stable within individuals across sessions and an individual\'s pilot data could be used as calibration/training data to build a robust drowsiness detector. Empirical results of this study suggested that the feasibility of leveraging existing BCI models built by other subjects\' data and a relatively small amount of subject-specific pilot data to develop a BCI that can outperform the BCI based solely on the pilot data of the subject.
Toward Non-Hair-Bearing Brain-Computer Interfaces for Neurocognitive Lapse Detection
(2015)
Wei, Chun-Shu
;
Wang, Yu-Te
;
Lin, Chin-Teng
;
Jung, Tzyy-Ping
;
電子工程學系及電子研究所
Recent advances in mobile electroencephalogram (EEG) acquisition based on dry electrodes have started moving Brain-Computer Interface (BCI) applications from well-controlled laboratory settings to real-world environments. However, the application mechanisms and high impedance of dry electrodes over the hair-covered areas remain challenging for everyday use of BCI. In addition, whole-scalp recordings are not always necessary or applicable due to various practical constrains. Therefore, alternative montages for EEG recordings to meet the everyday needs are in-demand. Inspired by our previous work on measuring non-hair-bearing steady state visual evoked potentials for BCI applications, this study explores the feasibility and efficacy of detecting cognitive lapses of participants based on EEG signals collected from the non-hair-bearing areas. Study results suggest that informative EEG features associated with lapses could be assessed from non-hair-bearing areas with comparable accuracy obtained from the whole-scalp EEG. The design principles, validation processes and promising findings reported in this study may enable and/or facilitate numerous BCI applications in real-world environments.
Transfer Learning with Large-Scale Data in Brain-Computer Interfaces
(2016-01-01)
Wei, Chun-Shu
;
Lin, Yuan-Pin
;
Wang, Yu-Te
;
Lin, Chin-Teng
;
Jung, Tzyy-Ping
Human variability in electroencephalogram (EEG) poses significant challenges for developing practical real-world applications of brain-computer interfaces (BCIs). The intuitive solution of collecting sufficient user-specific training/calibration data can be very labor-intensive and time-consuming, hindering the practicability of BCIs. To address this problem, transfer learning (TL), which leverages existing data from other sessions or subjects, has recently been adopted by the BCI community to build a BCI for a new user with limited calibration data. However, current TL approaches still require training/calibration data from each of conditions, which might be difficult or expensive to obtain. This study proposed a novel TL framework that could nearly eliminate requirement of subject-specific calibration data by leveraging large-scale data from other subjects. The efficacy of this method was validated in a passive BCI that was designed to detect neurocognitive lapses during driving. With the help of large-scale data, the proposed TL approach outperformed the within-subject approach while considerably reducing the amount of calibration data required for each individual (similar to 1.5 min of data from each individual as opposed to a 90 min pilot session used in a standard within-subject approach). This demonstration might considerably facilitate the real-world applications of BCIs.
Wearable and Wireless Brain-Computer Interface and Its Applications
(2009)
Lin, Chin-Teng
;
Ko, Li-Wei
;
Chang, Che-Jui
;
Wang, Yu-Te
;
Chung, Chia-Hsin
This study extends our previous work on mobile & wireless EEG acquisition to a truly wearable and wireless human-machine interface, NCTU Brain-Computer-Interface-headband,(BCI-headband), featuring: (1) dry Micro-Electro-Mechanical System (MEMS) EEG electrodes with 400 ganged contacts for acquiring signals from non-hairy sites without use Of gel or skin preparation; (2) a miniature data acquisition circuitry, (3) wireless telemetry and (4) online signal processing on a commercially available cell phone or a lightweight, wearable digital signal processing module. The applicability of the NCTU BCI headband to EEG monitoring in real-world environments was demonstrated in a sample study: cognitive-state monitoring and management of participants performing normal tasks.