Browsing by Author "Duann, Jeng-Ren"

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A 64-CHANNEL WIRELESS NEURAL SENSING MICROSYSTEM WITH TSV-EMBEDDED MICRO-PROBE ARRAY FOR NEURAL SIGNAL ACQUISITION
(2017-01-01)
Huang, Yu-Chieh
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Huang, Po-Tsang
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Hu, Yu-Chen
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Wu, Shang-Lin
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You, Yan-Huei
To enhance the signal integrity of high-density neural-sensing signals, this work presents an implantable high spatial resolution mu-probe array with through-silicon via (TSV) 2.5D integration technology that realizes a miniaturized implantable device on flexible printed circuit (FPC) interposer. The proposed microsystem was composed of two 32-channel neural sensing chips and one radio frequency chip for neural signal processing. The mu-probe array can achieve better signal-to-noise ratio with neural-signal acquisition and processing circuit composed of a pseudo-resistor-based analog front-end amplifier. Moreover, a receiving antenna is also implemented on the backside of FPC for wireless data and power transmission. The feasibility of the proposed mu-probe array, Tx and Rx antenna, 32-channel neural sensing circuits in the 64-channel wireless microsystem have been successfully demonstrated for future integration and animal experiments.
A Comparison of Independent Event-Related Desynchronization Responses in Motor-Related Brain Areas to Movement Execution, Movement Imagery, and Movement Observation
(2016-09-16)
Duann, Jeng-Ren
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Chiou, Jin-Chern
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
Electroencephalographic (EEG) event-related desynchronization (ERD) induced by movement imagery or by observing biological movements performed by someone else has recently been used extensively for brain-computer interface-based applications, such as applications used in stroke rehabilitation training and motor skill learning. However, the ERD responses induced by the movement imagery and observation might not be as reliable as the ERD responses induced by movement execution. Given that studies on the reliability of the EEG ERD responses induced by these activities are still lacking, here we conducted an EEG experiment with movement imagery, movement observation, and movement execution, performed multiple times each in a pseudorandomized order in the same experimental runs. Then, independent component analysis (ICA) was applied to the EEG data to find the common motor-related EEG source activity shared by the three motor tasks. Finally, conditional EEG ERD responses associated with the three movement conditions were computed and compared. Among the three motor conditions, the EEG ERD responses induced by motor execution revealed the alpha power suppression with highest strengths and longest durations. The ERD responses of the movement imagery and movement observation only partially resembled the ERD pattern of the movement execution condition, with slightly better detectability for the ERD responses associated with the movement imagery and faster ERD responses for movement observation. This may indicate different levels of involvement in the same motor-related brain circuits during different movement conditions. In addition, because the resulting conditional EEG ERD responses from the ICA preprocessing came with minimal contamination from the non-related and/or artifactual noisy components, this result can play a role of the reference for devising a brain-computer interface using the EEG ERD features of movement imagery or observation.
Alpha modulation in parietal and retrosplenial cortex correlates with navigation performance
(2012-01-01)
Chiu, Te-Cheng
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Gramann, Klaus
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Ko, Li-Wei
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Duann, Jeng-Ren
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Jung, Tzyy-Ping
The present study investigated the brain dynamics accompanying spatial navigation based on distinct reference frames. Participants preferentially using an allocentric or an egocentric reference frame navigated through virtual tunnels and reported their homing direction at the end of each trial based on their spatial representation of the passage. Task-related electroencephalographic (EEG) dynamics were analyzed based on independent component analysis (ICA) and subsequent clustering of independent components. Parietal alpha desynchronization during encoding of spatial information predicted homing performance for participants using an egocentric reference frame. In contrast, retrosplenial and occipital alpha desynchronization during retrieval covaried with homing performance of participants using an allocentric reference frame. These results support the assumption of distinct neural networks underlying the computation of distinct reference frames and reveal a direct relationship of alpha modulation in parietal and retrosplenial areas with encoding and retrieval of spatial information for homing behavior.
An Advanced 2.5-D Heterogeneous Integration Packaging for High-Density Neural Sensing Microsystem
(2017-04-01)
Hu, Yu-Chen
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Huang, Yu-Chieh
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Huang, Po-Tsang
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Wu, Shang-Lin
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Chang, Hsiao-Chun
In the traditional neural sensing microstructure, the limited metal line pitch and the metal layer numbers restrict the neural signal routing ability from electrodes to circuit chips. Miniature packaging and excessive noise interference bottlenecks are some of the challenges faced by the electrodes and circuit chips integration with traditional wire bonding. This paper proposes a 2.5-D heterogeneous integration neural sensing microsystem based on the silicon substrate to overcome these issues. With standard semiconductor and 3-D integration processes, high-channel-density (256 channels at 25 mm(2)) neural sensing microsystem is achieved. Through silicon via provides the shortest vertical interconnection and dramatically minimizes the packaging. Furthermore, the interposer can carry multiple chips to enhance the function of the biosensor. Electrical characteristics and reliability examinations reveal its high quality and great performance as compared to traditional approaches. This novel highly integrated neural sensing microsystem is expected to contribute to the biomedical field for exploring and solving unknown biological mysteries.
An Implantable 128-Channel Wireless Neural-Sensing Microsystem using TSV-Embedded Dissolvable mu-Needle Array and Flexible Interposer
(2017-01-01)
Huang, Po-Tsang
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Huang, Yu-Chieh
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Wu, Shang-Lin
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Hu, Yu-Chen
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Lu, Ming-Wei
For implanted neural-sensing devices, one of the remaining challenges is to transmit stable power/data (P/D) transmission for high spatiotemporal resolution neural data. This paper presents a miniaturized implantable 128-channel wireless neural-sensing microsystem using TSV-embedded dissolvable mu-needle array, a flexible interposer and 4 dies by 2.5D/3D TSV heterogeneous SiP technology. The 4 dies are 2 neural-signal acquisition ICs implemented by 90nm CMOS, 1 neural-signal processor by 40nm CMOS and 1 wireless P/D transmission circuitry by 0.18 mu m CMOS. Thus, the proposed wireless microsystem realizes 128-channel neural-signal sensing within the area of 5mm x 5mm, neural feature extraction and wireless P/D transmission using an on-interposer inductor. The overall average power of the circuits in this microsystem is only 9.85mW.
An Ultra-High-Density 256-channel/25mm(2) Neural Sensing Microsystem using TSV-embedded Neural Probes
(2016)
Huang, Yu-Chieh
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Huang, Po-Tsang
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Wu, Shang-Lin
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Hui, Yu-Chen
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You, Yan-Huei
Highly integrated neural sensing microsystems are crucial to capture accurate signals for brain function investigations. In this paper, a 256-channel/25 mm(2) neural sensing microsystem is presented based on through-silicon-via (TSV) 2.5D integration. This microsystem composes of dissolvable mu-needles, TSV-embedded mu-probes, 256-channel neural amplifiers, 11-bit area-power-efficient SAR ADCs and serializers. Based on the dissolvable mu-needles and TSV 2.5D integration, this microsystem can detect 256 ECoG/LFP signals within the small area of 5mm x 5mm. Additionally, the neural amplifier realizes 57.8dB gain with only 9.8 mu W for each channel, and the 9.7-bit ENOB of the SAR ADC at 32kS/s can be achieved with 0.42 mu W and 0.036 mm(2). The overall power of this microsystem is only 3.79mW for 256-channel neural sensing.
Analyze fluorescent characteristic of cancer cell using hyperspectroscopic imaging system (HIS)
(2012-01-01)
Hsieh, Yao-Fang
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Chen, Chih-Hsien
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Mang, Ou-Yang
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Duann, Jeng-Ren
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Chiou, Jin-Chern
Currently, the cancer was examined by diagnosing the pathological changes of tumor. If the examination of cancer can diagnose the tumor before the cell occur the pathological changes, the cure rate of cancer will increase. This research develops a human-machine interface for hyper-spectral microscope. The hyper-spectral microscope can scan the specific area of cell and records the data of spectrum and intensity. These data is helpful to diagnose tumor. This study finds the hyper-spectral imaging have two higher intensity points at 550nm and 700nm, and one lower point at 640nm between the two higher points. For analyzing the hyper-spectral imaging, the intensity at the 550nm peak divided by the intensity at 700nm peak. Finally, we determine the accuracy of detection by Gaussian distribution. The accuracy of detecting normal cells achieves 89%, and the accuracy of cancer cells achieves 81%.
Anatomic Differences between Parkinson\'s Disease and Essential Tremor Using ICA-based Brain Morphometry
(2013)
Duann, Jeng-Ren
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Lin, Ching-Hung
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Chen, Chun-Min
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Lu, Ming-Kuei
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Tsai, Chon-Haw
This study used independent component analysis (ICA) to decompose the surrogate image data by concatenating in space multiple subjects\' anatomical images after spatial normalization and smoothing to make all images in the same space. The multiple-subject anatomical image data included three different subject/patient populations, namely the Parkinson\'s disease (PD) and essential tremor (ET) as well as age-matched normal control (NC) subjects. In so doing, we were to extract the independent components showing significant group differences caused by the morphometric changes due to the neurological diseases. Such an independent component-based morphometry (ICBM) applied to both the grouped gray-matter and white-matter image data could be used to successfully reveal the brain areas within the gray and white matters showing significant group differences. In gray matter, we found a majority of basal ganglia brain areas associated with NC > PD, which well represented the causes of PD reported in the literature. In addition, the ICBM also found the brain areas of right superior temporal gyrus, right angular gyrus (BA 39), right inferior temporal gyrus, and left middle temporal gyrus (BA 37) associated with NC > ET. On the other hand, a more complicated patterns were found in the white matter associated with PD > ET and ET > PD conditions.
Brain oscillation and connectivity during a chemistry visual working memory task
(2013-11-01)
Huang, Li-Yu
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She, Hsiao-Ching
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Chou, Wen-Chi
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Chuang, Ming-Hua
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Duann, Jeng-Ren
Many studies have reported that frontal theta and posterior alpha activities are associated with working memory tasks. However, fewer studies have focused on examining whether or not the frontal alpha or posterior theta can play a role in the working memory task. This study investigates electroencephalography (EEG) dynamics and connectivity among different brain regions' theta and alpha oscillations. The EEG was collected from undergraduate students (n = 64) while they were performing a Sternberg-like working memory task involving chemistry concepts. The results showed that the frontal midline cluster exhibited sustained theta augmentation across the periods of stimulus presentations, maintenance, and probe presentation, suggesting that the frontal midline theta might associate with facilitating the central execute function to maintain information in the working memory. Study of the central parietal and the occipital clusters revealed a sequence of theta augmentation followed by alpha suppression at constant intervals after the onset of stimulus and probe presentations, suggesting that the posterior theta might be associated with sensory processing, theta gating, or stimulus selection. It further suggests that the posterior alpha event-related de-synchronization (ERD) might be linked to direct information flow into and out of the long-term memory (LTM) and precede stimulus recognition. An alternating phasic alpha event-related synchronization (ERS) and ERD following the 1st stimulus and probe presentations were observed at the occipital cluster, in which alpha ERS might be linked to the inhibition of irrelevant information. (C) 2013 Elsevier B.V. All rights reserved.
Detecting Frontal EEG Activities with Forehead Electrodes
(2009)
Duann, Jeng-Ren
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Chen, Po-Chuan
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Ko, Li-Wei
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Huang, Ruey-Song
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Jung, Tzyy-Ping
This study demonstrates the acquisitions of EEG signals from non-hairy forehead sites and tested the feasibility of using the forehead EEG in detecting drowsiness-related brain activities. A custom-made 15-channel forehead EEG-electrode patch and 28 scalp electrodes placed according to the International 10-20 system were used to simultaneously record EEG signals from the forehead and whole-head regions, respectively. A total of five Subjects were instructed to perform a night-time long-haul driving task for all hour in a virtual-reality based driving simulator comprising a real car mounted oil a 6 degree-of-freedom Steward motion platform and a immersive VR environment with 360 degree projection scenes. Separate independent component analyses were applied to the forehead and whole-head EEG data for each individual subject. For the whole-head independent component (IC) set, the frontal central midline (FCM) IC with an equivalent dipole source located in the interior cingulate cortex was selected for further analysis. For the forehead IC set, the IC with its theta power changes highly correlated with Subject\'s driving performance was selected. The EEG power changes of the selected forehead ICs were then used to predict driving performance based on a linear regression model. The results of this study showed that it is feasible to accurately estimate quantitatively the changing level of driving performance using the EEG features obtained from the forehead non-hairy channels, and the estimation accuracy was comparable to that using the EEG features of the whole-head recordings.
Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning
(2008-05-01)
Lin, Chin-Teng
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Chen, Yu-Chieh
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Huang, Teng-Yi
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Chiu, Tien-Ting
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Ko, Li-Wei
Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.
Dynamic brain connectivity attuned to the complexity of relative clause sentences revealed by a single -trial analysis
(2020-08-15)
Xu, Kunyu
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Wu, Denise H.
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Duann, Jeng-Ren
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教育研究所
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Institute of Education
EEG-Based assessment of driver cognitive responses in a dynamic virtual-reality driving environment
(2007-07-01)
Lin, Chin-Teng
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Chung, I-Fang
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Ko, Li-Wei
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Chen, Yu-Chieh
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Liang, Sheng-Fu
Accidents caused by errors and failures in human performance among traffic fatalities have a high death rate and become an important issue in public security. They are mainly caused by the failures of the drivers to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for assessing driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. The VR technique allows subjects to interact directly with the moving virtual environment instead of monotonic auditory and visual stimuli, thereby provides interactive and realistic tasks without the risk of operating on an actual machine. Independent component analysis (ICA) is used to separate and extract noise-free ERP signals from the multi-channel EEG signals. A temporal filter is used to solve the time-alignment problem of ERP features and principle component analysis (PCA) is used to reduce feature dimensions. The dimension-reduced features are then input to a self-constructing neural fuzzy inference network (SONFIN) to recognize different brain potentials stimulated by red/green/yellow traffic events, the accuracy can be reached 87% in average eight subjects in this visual-stimuli ERP experiment. It demonstrates the feasibility of detecting and analyzing multiple streams of ERP signals that represent operators' cognitive states and responses to task events.
EEG-based Spatial Navigation Estimation in a Virtual Reality Driving Environment
(2009)
Lin, Chin-Teng
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Yang, Fu-Shu
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Chiou, Te-Chung
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Ko, Li-Wei
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Duann, Jeng-Ren
The aim of this study is to investigate the difference of EEC dynamics on navigation performance. A tunnel task was designed to classify subjects into allocentric or egocentric spatial representation users. Despite of the differences of mental spatial representation, behavioral performance in general were compatible between the two strategies subjects in the tunnel task. Task-related EEC dynamics in power changes were analyzed using independent component analysis (ICA), time-frequency and non-parametric statistic test. ERSP image results revealed navigation performance-predictive EEG activities which is is expressed in the parietal component by source reconstruction. For egocentric subjects, comparing to trails with well-estimation of homing angle, the power attenuation at the frequencies from 8 to 30 Hz (around alpha and beta band) was stronger when subjects overestimated homing directions, but the attenuated power was decreased when subjects were underestimated the homing angles. However, we did not found performance related brain activities for allocentric subjects, which may due to the functional dissociation between the use of allo- and egocentric reference frames.
Effect of Respiratory Modulation on Relationship between Heart Rate Variability and Motion Sickness
(2011)
Lin, Chin-Teng
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Lin, Chun-Ling
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Chiu, Tzai-Wen
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Duann, Jeng-Ren
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Jung, Tzyy-Ping
This study investigates the interplay among heart rate variability (HRV), respiration, and the severity of motion sickness (MS) in a realistic passive driving task. Although HRV is a commonly used metrically in physiological research or even believed to be a direct measure of sympathovagal activities, the results of MS-effected HRV remain mixed across studies. The goal of this study is to find the source of these contradicting results of HRV associated with MS. Experimental results of this study showed that the group trend of the low-frequency (LF) component and the LF/HF ratio increased and high-frequency (HF) component decreased significantly as self-reported MS level increased (p<0.001), consistent with a perception-driven autonomic response of the cardiovascular system. However, in one of the subjects, the relationship was reversed when individuals intentionally adjust themselves (deep breathing) to relieve the discomfort of MS during the experiments. It appears that the correlations between HRV and MS level were higher when individuals made fewer adjustments (the number of deep breathing) during the passive driving experiments.
Explore the Functional Connectivity between Brain Regions during a Chemistry Working Memory Task
(2015-06-03)
Chou, Wen-Chi
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Duann, Jeng-Ren
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She, Hsiao-Ching
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Huang, Li-Yu
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Jung, Tzyy-Ping
Previous studies have rarely examined how temporal dynamic patterns, event-related coherence, and phase-locking are related to each other. This study assessed reaction-time-sorted spectral perturbation and event-related spectral perturbation in order to examine the temporal dynamic patterns in the frontal midline (F), central parietal (CP), and occipital (O) regions during a chemistry working memory task at theta, alpha, and beta frequencies. Furthermore, the functional connectivity between F-CP, CP-O, and F-O were assessed by component event-related coherence (ERCoh) and component phase-locking (PL) at different frequency bands. In addition, this study examined whether the temporal dynamic patterns are consistent with the functional connectivity patterns across different frequencies and time courses. Component ERCoh/PL measured the interactions between different independent components decomposed from the scalp EEG, mixtures of time courses of activities arising from different brain, and artifactual sources. The results indicate that the O and CP regions' temporal dynamic patterns are similar to each other. Furthermore, pronounced component ERCoh/PL patterns were found to exist between the O and CP regions across each stimulus and probe presentation, in both theta and alpha frequencies. The consistent theta component ERCoh/PL between the F and O regions was found at the first stimulus and after probe presentation. These findings demonstrate that temporal dynamic patterns at different regions are in accordance with the functional connectivity patterns. Such coordinated and robust EEG temporal dynamics and component ERCoh/PL patterns suggest that these brain regions' neurons work together both to induce similar event-related spectral perturbation and to synchronize or desynchronize simultaneously in order to swiftly accomplish a particular goal. The possible mechanisms for such distinct component phase-locking and coherence patterns were also further discussed.
Eye fixation-related fronto-parietal neural network correlates of memory retrieval
(2019-04-01)
Tsai, Pei-Yi
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She, Hsiao-Ching
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Chen, Sheng-Chang
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Huang, Li-Yu
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Chou, Wen-Chi
Eye movements are considered to be informative with regard to the underlying cognitive processes of human beings. Previous studies have reported that eye movements are associated with which scientific concepts are retrieved correctly. Moreover, other studies have also suggested that eye movements involve the cooperative activity of the human brain's fronto-parietal circuits. Less research has been conducted to investigate whether fronto-parietal EEG oscillations are associated with the retrieval processing of scientific concepts. Our findings in this study demonstrated that the fronto-parietal network is indeed crucial for successful memory retrieval. In short, significantly lower theta augmentation in the frontal midline and lower alpha suppression in the right parietal region were observed at the 5th eye fixation for physics concepts that were correctly retrieved than for those that were incorrectly retrieved. Moreover, the visual cortex in the occipital lobe exhibits a significantly greater theta augmentation followed by an alpha suppression following each eye fixation, while a right frontoparietal asymmetry was also found for the successful retrieval of presentations of physics concepts. In particular, the study results showed that eye fixation-related frontal midline theta power and right parietal alpha power at the 5th eye fixation have the greatest predictive power regarding the correctness of the retrieval of physics concepts.
Generalizations of the Subject-independent Feature Set for Music-induced Emotion Recognition
(2011)
Lin, Yuan-Pin
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Chen, Jyh-Horng
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Duann, Jeng-Ren
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Lin, Chin-Teng
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Jung, Tzyy-Ping
Electroencephalogram (EEG)-based emotion recognition has been an intensely growing field. Yet, how to achieve acceptable accuracy on a practical system with as fewer electrodes as possible is less concerned. This study evaluates a set of subject-independent features, based on differential power asymmetry of symmetric electrode pairs [1], with emphasis on its applicability to subject variability in music-induced emotion classification problem. Results of this study have evidently validated the feasibility of using subject-independent EEG features to classify four emotional states with acceptable accuracy in second-scale temporal resolution. These features could be generalized across subjects to detect emotion induced by music excerpts not limited to the music database that was used to derive the emotion-specific features.
Integration of Neural Sensing Microsystem with TSV-embedded Dissolvable mu-Needles Array, Biocompatible Flexible Interposer, and Neural Recording Circuits
(2016)
Huang, Yu-Chieh
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Hu, Yu-Chen
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Huang, Po-Tsang
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Wu, Shang-Lin
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You, Yan-Huei
Local brain connectivity is expected to lead to new models for neurological diseases, which may in turn result in advanced understanding and better treatment. This paper presents a neural sensing microsystem integrated with TSV-embedded dissolvable mu-needles array, ENIG bonding technology, biocompatible Au-TSV flexible interposer and neural recording circuits, for neural sensing implantation. An ultra-thin film bonding approach is proposed for integration of interposer assembly. Removing bonding wire by proposed bonding technology, the dimension of neural sensing system can be minimized to reduce surgical area and promote implant success rate. The signal quality of neural recording can be significantly improved by eliminating complex signal paths with 2.5D TSV integration from neural sensing interface to neural recording circuits.
Miniaturized Cortex Cooling Device and System for Hypothermia Therapy Application on Freely Moving Rat
(2012)
Chang, Chih-Wei
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Hou, Kuan-Chou
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Chou, Lei-Chun
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Chiou, Jin-Chern
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Chiou, Jin-Chern
This paper presents a miniaturized brain cortex cooling device and system using thermoelectric (TE) cooler for hypothermia therapy application on freely moving rat. Two type of cooling electrodes including planar and micro-needle structures are fabricated and studied by EEG recording. Comparing to prior arts, our approach can be applied under freely moving behavior. The cooling temperature is designed as 2 degrees C to avoid permanent injury in brain. Packaged planar or needle electrode with TE cooler consumes only 2x2mm2. EEG recording electrodes and thermocouple are implanted for observation as well. Simulation result shows that TE-cooler with needle type electrode behaves deeper effective cooling depth and faster cooling response in rodent cortex range (similar to 2mm deep). Long term EEG monitoring on freely moving rat also presents no extra side effects with continuously cooling.