Browsing by Author "Liao, Lun-De"

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A Miniaturized Electromagnetic Generator With Planar Coils and Its Energy Harvest Circuit
(2009-10-01)
Liao, Lun-De
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Chao, Paul C. -P.
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Chen, Jian-Ting
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Chen, Wei-Dar
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Hsu, Wei-Hsuan
This study presents design, analysis and experiment of a miniaturized rotary generator in size of 10 10 2 mm and its compact energy harvest circuit chip. The designed generator consists of patterned planar copper coils and a multipolar hard magnet ring made of NdFeB. To perform modeling, a harmonic-like magnetic field model along the circumferential path of each magnetic pole is assumed with the assistance from measured peak magnetic flux densities. This is followed by the application of Faraday's law to predict generated electromotive forces (EMFs) in terms of the relative rotational speed between the magnet ring and coils. The genetic algorithm (GA) is next applied to optimize the critical dimensions of the miniaturized generator. The theoretical model of this power microgenerator is evaluated and compared with experimental results, and it is found that the analytical simulation shows a good agreement with the experimental results. The optimized generator offers 4.5 V and 7.23m W in root mean square (rms) at 10 000 r/min. With microgenerator successfully fabricated, a novel energy harvest circuit employing Dickson charge pump is designed and fabricated via the 0.35-um process offered by National Chip Implementation Center (CIC) of Taiwan. This charge pump circuit owns the merit of almost-zero thresholds of employed metal-oxide-semiconductor (MOS) transistors, enabling the conversion of low-power alternating current (ac) signals by the microgenerator to direct current (dc) ones.
A Novel 16-Channel Wireless System for Electroencephalography Measurements With Dry Spring-Loaded Sensors
(2014-06-01)
Liao, Lun-De
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Wu, Shang-Lin
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Liou, Chang-Hong
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Lu, Shao-Wei
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Chen, Shi-An
Understanding brain function using electroencephalography (EEG) is an important issue for cerebral nervous system diseases, especially for epilepsy and Alzheimer\'s disease. Many EEG measurement systems are used reliably to study these diseases, but their bulky size and the use of wet sensors make them uncomfortable and inconvenient for users. To overcome the limitations of conventional EEG measurement systems, a wireless and wearable multichannel EEG measurement system is proposed in this paper. This system includes a wireless data acquisition device, dry spring-loaded sensors, and a size-adjustable soft cap. We compared the performance of the proposed system using dry versus conventional wet sensors. A significant positive correlation between readings from wet and dry sensors was achieved, thus demonstrating the performance of the system. Moreover, four different features of EEG signals (i.e., normal, eye-blinking, closed-eyes, and teeth-clenching signals) were measured by 16 dry sensors to ensure that they could be detected in real-life cognitive neuroscience applications. Thus, we have shown that it is possible to reliably measure EEG signals using the proposed system. This paper presents novel insights into the field of cognitive neuroscience, showing the possibility of studying brain function under real-life conditions.
A Sliced Inverse Regression (SIR) Decoding the Forelimb Movement from Neuronal Spikes in the Rat Motor Cortex
(2016-12-09)
Yang, Shih-Hung
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Chen, You-Yin
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Lin, Sheng-Huang
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Liao, Lun-De
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Lu, Henry Horng-Shing
Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE), and neural networks (NN), are effective in predicting movement kinematics, including movement direction, speed and trajectory but usually require a large number of neurons to achieve desirable performance. This study proposed a novel decoding algorithm even with signals obtained from a smaller numbers of neurons. We adopted sliced inverse regression (SIR) to predict forelimb movement from single-unit activities recorded in the rat primary motor (M1) cortex in a water-reward lever-pressing task. SIR performed weighted principal component analysis (RCA) to achieve effective dimension reduction for nonlinear regression. To demonstrate the decoding performance, SIR was compared to PVA, OLE, and NN. Furthermore, RCA and sequential feature selection (SFS) which are popular feature selection techniques were implemented for comparison of feature selection effectiveness. Among SIR, PVA, OLE, PCA, SFS, and NN decoding methods, the trajectories predicted by SIR (with a root mean square error, RMSE, of 8.47 +/- 1.32 mm) was closer to the actual trajectories compared with those predicted by PVA (30.41 +/- 11.73 mm), OLE (20.17 +/- 6.43 mm), PCA (19.13 +/- 0.75 mm), SFS (22.75 +/- 2.01 mm), and NN (16.75 +/- 2.02 mm). The superiority of SIR was most obvious when the sample size of neurons was small. We concluded that SIR sorted the input data to obtain the effective transform matrices for movement prediction, making it a robust decoding method for conditions with sparse neuronal information.
A Wearable Mobile Electrocardiogram Measurement Device with Novel Dry Polymer-based Electrodes
(2010)
Wang, I-Jan
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Liao, Lun-De
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Wang, Yu-Te
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Chen, Chi-Yu
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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.
Achieving High Directionality and Extraction of Light Emission from Polymer Light Emitting Diodes via ITO/Organic Photonic Crystals
(2009)
Liao, Lun-De
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Chao, Paul C. -P.
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Chen, In-Chen
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Meng, Hsin-Fei
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Chai, Che-Hong
The objective of this study is to achieve high directionality and extraction of light emission from polymer light-emitting diodes via optimizing photonic crystals (PCs). Genetic Algorithm is employed to search the optimal PC period, depth and filling factor. The large-area PCs are fabricated by Focus Ion Beams for PLED, which is commercially viable, even applicable to active matrix PLED displays.
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.
BIOMEDICAL SENSOR DEVICE
(2010-11-11)
Chen, Yu-Han
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Liao, Lun-De
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Chao, Paul C.-P.
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Lin, C.T.
A biomedical sensor device includes a light source, a probe array, and a photo detector. The light source is configured for emitting infrared radiation. The probe array is contacted to a user's skin to detect an electric wave signal transmitted through the probe array from the skin. The probe array includes a substrate and a plurality of probes mounted on the substrate, wherein the substrate and the probes are non-opaque so that the infrared radiation may be transmitted through the probe array into the skin. The photo detector is configured to detect an infrared signal by measuring the infrared radiation absorption by the skin.
BIOSENSOR AND ELECTRODE STRUCTURE THEREOF
(2011-03-31)
Liao, Lun-De
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Chen, Yu-Han
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Chao, Paul C.-P.
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Lin, Chin-Teng
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Sun, Arch
An electrode structure of a biosensor includes a flexible conductive substrate and a plurality of conductive probes protruding from the conductive substrate and configured for contacting a subject and receiving a physiological electric wave signal therefrom. The present invention improves disadvantages of wet electrodes and microstructure electrodes and provides more stable signals that may less decay with time so as to achieve real-time and long-acting measurement for physiological electric wave signal. A biosensor using the electrode structure is also disclosed.
Biosensor Technologies for Augmented Brain-Computer Interfaces in the Next Decades
(2012-05-01)
Liao, Lun-De
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Lin, Chin-Teng
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McDowell, Kaleb
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Wickenden, Alma E.
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Gramann, Klaus
The study of brain-computer interfaces (BCIs) has undergone 30 years of intense development and has grown into a rich and diverse field. BCIs are technologies that enable direct communication between the human brain and external devices. Conventionally, wet electrodes have been employed to obtain unprecedented sensitivity to high-temporal-resolution brain activity; recently, the growing availability of various sensors that can be used to detect high-quality brain signals in a wide range of clinical and everyday environments is being exploited. This development of biosensing neurotechnologies and the desire to implement them in real-world applications have led to the opportunity to develop augmented BCIs (ABCIs) in the upcoming decades. An ABCI is similar to a BCI in that it relies on biosensors that record signals from the brain in everyday environments; the signals are then processed in real time to monitor the behavior of the human. To use an ABCI as a mobile brain imaging technique for everyday, real-life applications, the sensors and the corresponding device must be lightweight and the equipment response time must be short. This study presents an overview of the wide range of biosensor approaches currently being applied to ABCIs, from their use in the laboratory to their application in clinical and everyday use. The basic principles of each technique are described along with examples of current applications of cutting-edge neuroscience research. In summary, we show that ABCI techniques continue to grow and evolve, incorporating new technologies and advances to address ever more complex and important neuroscience issues, with advancements that are envisioned to lead to a wide range of real-life applications.
Characterization of a Fiber Bundle-Based Real-Time Ultrasound/Photoacoustic Imaging System and Its In Vivo Functional Imaging Applications
(2019-12-01)
Leng, He
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Wang, Yuhling
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Jhang, De-Fu
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Chu, Tsung-Sheng
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Tsao, Chia-Hui
Photoacoustic (PA) imaging is an attractive technology for imaging biological tissues because it can capture both functional and structural information with satisfactory spatial resolution. Current commercially available PA imaging systems are limited by their bulky size or inflexible user interface. We present a new handheld real-time ultrasound/photoacoustic imaging system (HARP) consisting of a detachable, high-numerical-aperture (NA) fiber bundle-based illumination system integrated with an array-based ultrasound (US) transducer and a data acquisition platform. In this system, different PA probes can be used for different imaging applications by switching the transducers and the corresponding jackets to combine the fiber pads and transducer into a single probe. The intuitive user interface is a completely programmable MATLAB-based platform. In vitro phantom experiments were conducted to test the imaging performance of the developed PA system. Furthermore, we demonstrated (1) in vivo brain vasculature imaging, (2) in vivo imaging of real-time stimulus-evoked cortical hemodynamic changes during forepaw electrical stimulation, and (3) in vivo imaging of real-time cerebral pharmacokinetics in rats using the developed PA system. The overall purpose of this design concept for a customizable US/PA imaging system is to help overcome the diverse challenges faced by medical researchers performing both preclinical and clinical PA studies.
Cognition in action: imaging brain/body dynamics in mobile humans
(2011-12-01)
Gramann, Klaus
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Gwin, Joseph T.
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Ferris, Daniel P.
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Oie, Kelvin
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Jung, Tzyy-Ping
We have recently developed a mobile brain imaging method (MoBI), that allows for simultaneous recording of brain and body dynamics of humans actively behaving in and interacting with their environment. A mobile imaging approach was needed to study cognitive processes that are inherently based on the use of human physical structure to obtain behavioral goals. This review gives examples of the tight coupling between human physical structure with cognitive processing and the role of supraspinal activity during control of human stance and locomotion. Existing brain imaging methods for actively behaving participants are described and new sensor technology allowing for mobile recordings of different behavioral states in humans is introduced. Finally, we review recent work demonstrating the feasibility of a MoBI system that was developed at the Swartz Center for Computational Neuroscience at the University of California, San Diego, demonstrating the range of behavior that can be investigated with this method.
Controlling a Human-Computer Interface System With a Novel Classification Method that Uses Electrooculography Signals
(2013-08-01)
Wu, Shang-Lin
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Liao, Lun-De
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Lu, Shao-Wei
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Jiang, Wei-Ling
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Chen, Shi-An
Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.
dc dynamic and static pull-in predictions and analysis for electrostatically actuated clamped circular micro-plates based on a continuous model
(2010-02-01)
Liao, Lun-De
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Chao, Paul C-P
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Huang, Chih-Wei
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Chiu, Chi-Wei
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影像與生醫光電研究所
This study develops a continuous model to analyze the 'pull-in' effect in the circular micro-plates used in capacitive-type micro-electro-mechanical systems (MEMS) sensors, actuators and microphones. In developing the model, the governing equation of motion of the deformed plate is established in the form of a partial different equation (PDE) which is then decomposed using the Galerkin method to create a coupled set of modal ordinary differential equations. By considering the first-order deflection mode only and using a fifth-order Taylor series expansion of the electrostatic force, closed-form solutions are obtained for both the position and the voltage of the static pull-in event. Applying an energy balance method and a finite-order approximation method, the solutions are then obtained for the position and voltage of the dynamic pull-in event. The theoretical results obtained for the pull-in phenomena are verified based on the comparison to available experimental data, and also numerically using a finite element analysis (FEA) approach. In general, the results indicate that the ratio of the dynamic to static pull-in voltages is approximately 92%. However, when the squeezed-film effect induced by the air gap between the two plates is taken into account, the value of this ratio increases slightly as a result of considering a higher dynamic pull-in voltage.
Design and experimental validation of the complex luminance model for the cold cathode fluorescent lamps backlight systems
(2007)
Chao, Paul C. -P.
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Liao, Lun-De
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Chiu, Chi-Wei
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電控工程研究所
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Institute of Electrical and Control Engineering
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
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Liu, Chi-Hsien
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Wang, Po-Sheng
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King, Jung-Tai
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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.
Design of the Multi-channel Electroencephalography-based Brain-Computer Interface with Novel Dry Sensors
(2012)
Wu, Shang-Lin
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Liao, Lun-De
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Liou, Chang-Hong
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Chen, Shi-An
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Ko, Li-Wei
The traditional brain-computer interface (BCI) system measures the electroencephalography (EEG) signals by the wet sensors with the conductive gel and skin preparation processes. To overcome the limitations of traditional BCI system with conventional wet sensors, a wireless and wearable multi-channel EEG-based BCI system is proposed in this study, including the wireless EEG data acquisition device, dry spring-loaded sensors, a size-adjustable soft cap. The dry spring-loaded sensors are made of metal conductors, which can measure the EEG signals without skin preparation and conductive gel. In addition, the proposed system provides a size-adjustable soft cap that can be used to fit user's head properly. Indeed, the results are shown that the proposed system can properly and effectively measure the EEG signals with the developed cap and sensors, even under movement. In words, the developed wireless and wearable BCI system is able to be used in cognitive neuroscience applications.
Design, Fabrication and Experimental Validation of a Novel Dry-Contact Sensor for Measuring Electroencephalography Signals without Skin Preparation
(2011-06-01)
Liao, Lun-De
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Wang, I-Jan
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Chen, Sheng-Fu
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Chang, Jyh-Yeong
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Lin, Chin-Teng
In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain low skin-sensor interface impedance. Additionally, the flexible substrate also initiates a sensor buffer effect, eliminating pain when force is applied. The proposed dry EEG sensor was reliable in measuring EEG signals without any skin preparation or conductive gel usage, as compared with the conventional wet electrodes.
Design, simulation and experimental validation of a novel flexible neural probe for deep brain stimulation and multichannel recording
(2012-06-01)
Lai, Hsin-Yi
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Liao, Lun-De
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Lin, Chin-Teng
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Hsu, Jui-Hsiang
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He, Xin
An implantable micromachined neural probe with multichannel electrode arrays for both neural signal recording and electrical stimulation was designed, simulated and experimentally validated for deep brain stimulation (DBS) applications. The developed probe has a rough three-dimensional microstructure on the electrode surface to maximize the electrode-tissue contact area. The flexible, polyimide-based microelectrode arrays were each composed of a long shaft (14.9 mm in length) and 16 electrodes (5 mu m thick and with a diameter of 16 mu m). The ability of these arrays to record and stimulate specific areas in a rat brain was evaluated. Moreover, we have developed a finite element model (FEM) applied to an electric field to evaluate the volume of tissue activated (VTA) by DBS as a function of the stimulation parameters. The signal-to-noise ratio ranged from 4.4 to 5 over a 50 day recording period, indicating that the laboratory-designed neural probe is reliable and may be used successfully for long-term recordings. The somatosensory evoked potential (SSEP) obtained by thalamic stimulations and in vivo electrode-electrolyte interface impedance measurements was stable for 50 days and demonstrated that the neural probe is feasible for long-term stimulation. A strongly linear (positive correlation) relationship was observed among the simulated VTA, the absolute value of the SSEP during the 200 ms post-stimulus period (Sigma SSEP) and c-Fos expression, indicating that the simulated VTA has perfect sensitivity to predict the evoked responses (c-Fos expression). This laboratory-designed neural probe and its FEM simulation represent a simple, functionally effective technique for studying DBS and neural recordings in animal models.
Development of a Wearable Mobile Electrocardiogram Monitoring System by Using Novel Dry Foam Electrodes
(2014-09-01)
Tseng, Kevin C.
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Lin, Bor-Shyh
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Liao, Lun-De
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Wang, Yu-Te
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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.
Editorial Message: Special Issue on Fuzzy Brain-Computer Interface Systems
(2017-04-01)
Liu, Yi-Hung
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Jung, Tzyy-Ping
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Lin, Chin-Teng
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Liao, Lun-De
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