Browsing by Author "Liang, Sheng-Fu"

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A Fully Integrated 16-Channel Closed-Loop Neural-Prosthetic CMOS SoC With Wireless Power and Bidirectional Data Telemetry for Real-Time Efficient Human Epileptic Seizure Control
(2018-11-01)
Cheng, Cheng-Hsiang
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Tsai, Ping-Yuan
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Yang, Tzu-Yi
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Cheng, Wan-Hsueh
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Yen, Ting-Yang
A 16-channel closed-loop neuromodulation system-on-chip (SoC) for human epileptic seizure control is proposed and designed. In the proposed SoC, a 16-channel neural-signal acquisition unit (NSAU), a biosignal processor (BSP), a 16-channel high-voltage-tolerant stimulator (HVTS), and wireless power and bidirectional data telemetry are designed. In the NSAU, the input protection circuit is used to prevent MOSFET from overstressing by the high-voltage stimulations. Hence, NSAUs can share electrodes with stimulators. The auto-reset chopper-stabilized capacitive-coupled instrumentation amplifiers (AR-CSCCIAs) are designed with the chopper-stabilized technique with a new offset reduction loop. The measured input-referred noise is 2.09 mu V-rms and the noise-efficiency factor (NEF) is 3.78. The entropy-and-spectrum seizure detection algorithm is implemented in the BSP with 0.76-s seizure detection latency and 97.8% detection accuracy. When the seizure onset is detected by the BSP, the HVTS with adaptive supply control delivers 0.5-3-mA biphasic current stimulation to suppress the seizure onset. The proposed SoC is powered wirelessly, and the bidirectional data telemetry is realized through the same pair of coils in 13.56 MHz. The downlink data rate is 211 Kb/s with the binary phase-shift keying (BPSK) modulation and a new BPSK demodulator. The uplink data rate is 106 Kb/s with the load-shift keying (LSK) modulation. The proposed SoC is fabricated in a 0.18-mu m CMOS technology and occupies 25 mm(2). Electrical tests have been performed to characterize the SoC performance. In vivo animal experiments using mini-pigs have been performed to successfully verify the closed-loop neuromodulation functions on epileptic seizure suppression.
A Fully Integrated 8-Channel Closed-Loop Neural-Prosthetic CMOS SoC for Real-Time Epileptic Seizure Control
(2014-01-01)
Chen, Wei-Ming
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Chiueh, Herming
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Chen, Tsan-Jieh
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Ho, Chia-Lun
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Jeng, Chi
An 8-channel closed-loop neural-prosthetic SoC is presented for real-time intracranial EEG (iEEG) acquisition, seizure detection, and electrical stimulation in order to suppress epileptic seizures. The SoC is composed of eight energy-efficient analog front-end amplifiers (AFEAs), a 10-b delta-modulated SAR ADC (DMSAR ADC), a configurable bio-signal processor (BSP), and an adaptive high-voltage-tolerant stimulator. A wire-less power-and-data transmission system is also embedded. By leveraging T-connected pseudo-resistors, the high-pass (low-pass) cutoff frequency of the AFEAs can be adjusted from 0.1 to 10 Hz (0.8 to 7 kHz). The noise-efficiency factor (NEF) of the AFEA is 1.77, and the DMSAR ADC achieves an ENOB of 9.57 bits. The BSP extracts the epileptic features from time-domain entropy and frequency spectrum for seizure detection. A constant 30-mu A stimulus current is delivered by closed-loop control. The acquired signals are transmitted with on-off keying (OOK) modulation at 4 Mbps over the MedRadio band for monitoring. A multi-LDO topology is adopted to mitigate the interferences across different power domains. The proposed SoC is fabricated in 0.18-mu m CMOS and occupies 13.47 mm(2). Verified on Long Evans rats, the proposed SoC dissipates 2.8 mW and achieves high detection accuracy 92% within 0.8 s.
A Fully Integrated 8-Channel Closed-Loop Neural-Prosthetic SoC for Real-Time Epileptic Seizure Control
(2013-01-01)
Chen, Wei-Ming
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Chiueh, Herming
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Chen, Tsan-Jieh
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Ho, Chia-Lun
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Jeng, Chi
A Fully Integrated Closed-Loop Neuromodulation SoC with Wireless Power and Bidirectional Data Telemetry for Real-Time Human Epileptic Seizure Control
(2017-01-01)
Cheng, Cheng-Hsiang
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Tsai, Ping-Yuan
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Yang, Tzu-Yi
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Cheng, Wan-Hsueh
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Yen, Ting-Yang
This paper presents a 16-channel closed-loop neuromodulation SoC for human seizure control. The SoC includes a 16-ch signal acquisition unit, a bio-signal processor, a 16-ch adaptive stimulator, and wireless telemetry. The signal acquisition unit achieves 3.78 NEF and shares electrodes with stimulator. The seizure detection latency is 0.76s and delivered 0.5-3mA biphasic current stimulation. The SoC is powered wirelessly and bidirectional data telemetry is realized through the same pair of coils in 13.56MHz.
A Hardware Implementation of Real-Time Epileptic Seizure Detector on FPGA
(2011)
Chen, Tsan-Jieh
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Jeng, Chi
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Chang, Shun-Ting
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Chiueh, Herming
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Liang, Sheng-Fu
a considerable portion of epilepsy cannot be well treated by today\'s available therapies. Brain stimulation with closed-loop seizure control was recently proposed. In our previous work, high accuracy of seizure controller (92-99% during wake-sleep states) using a microcontroller has been proposed and implemented to verify functionality of seizure detection and suppression on a freely moving rat. In this paper, the soft intellectual property (IP) of epileptic seizure detector in the implantable system on chip (SoC) for epileptic seizure control is proposed to achieve continuous real-time processing and low power consumption. All the essential macros for epileptic seizure detector are implemented and integrated in field programmable gate array (FPGA) to verify functionality and capability. The evaluation results of the prototype supported possible application of a portable and continuous real-time seizure control.
A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings
(2013-08-01)
Liang, Sheng-Fu
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Chen, Yi-Chun
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Wang, Yu-Lin
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Chen, Pin-Tzu
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Yang, Chia-Hsiang
Objective. Around 1% of the world's population is affected by epilepsy, and nearly 25% of patients cannot be treated effectively by available therapies. The presence of closed-loop seizure-triggered stimulation provides a promising solution for these patients. Realization of fast, accurate, and energy-efficient seizure detection is the key to such implants. In this study, we propose a two-stage on-line seizure detection algorithm with low-energy consumption for temporal lobe epilepsy (TLE). Approach. Multi-channel signals are processed through independent component analysis and the most representative independent component (IC) is automatically selected to eliminate artifacts. Seizure-like intracranial electroencephalogram (iEEG) segments are fast detected in the first stage of the proposed method and these seizures are confirmed in the second stage. The conditional activation of the second-stage signal processing reduces the computational effort, and hence energy, since most of the non-seizure events are filtered out in the first stage. Main results. Long-term iEEG recordings of 11 patients who suffered from TLE were analyzed via leave-one-out cross validation. The proposed method has a detection accuracy of 95.24%, a false alarm rate of 0.09/h, and an average detection delay time of 9.2 s. For the six patients with mesial TLE, a detection accuracy of 100.0%, a false alarm rate of 0.06/h, and an average detection delay time of 4.8 s can be achieved. The hierarchical approach provides a 90% energy reduction, yielding effective and energy-efficient implementation for real-time epileptic seizure detection. Significance. An on-line seizure detection method that can be applied to monitor continuous iEEG signals of patients who suffered from TLE was developed. An IC selection strategy to automatically determine the most seizure-related IC for seizure detection was also proposed. The system has advantages of (1) high detection accuracy, (2) low false alarm, (3) short detection latency, and (4) energy-efficient design for hardware implementation.
A HVS-directed neural-network-based approach for salt-pepper impulse noise removal
(2006-07-01)
Lu, Shih-Mao
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Liang, Sheng-Fu
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Lin, Chin-Teng
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生物科技學系
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資訊工程學系
In this paper, a novel two-stage noise removal algorithm to deal with salt-pepper impulse noise is proposed. In the first stage, the decision-based recursive adaptive noise-exclusive median filter is applied to remove the noise cleanly and to keep the uncorrupted information as well as possible. In the second stage, the fuzzy decision rules inspired by human visual system (HVS) are proposed to classify image pixels into human perception sensitive class and non-sensitive class. A neural network is proposed to compensate the sensitive regions for image quality enhancement. According to the experimental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and the smoothness in edge regions of the resultant images.
A novel application of the S-transform in removing powerline interference from biomedical signals
(2009-01-01)
Huang, Chien-Chun
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Liang, Sheng-Fu
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Young, Ming-Shing
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Shaw, Fu-Zen
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智慧型仿生系統研究中心
Powerline interference always disturbs recordings of biomedical signals. Numerous methods have been developed to reduce powerline interference. However, most of these techniques not only reduce the interference but also attenuate the 60 Hz power of the biomedical signals themselves. In the present study, we applied the S-transform, which provides an absolute phase of each frequency in a multi-resolution time-frequency analysis, to reduce 60 Hz interference. According to results from an electrocardiogram (ECG) to which a simulated 60 Hz noise was added, the S-transform de-noising process restored a power spectrum identical to that of the original ECG coincident with a significant reduction in the 60 Hz interference. Moreover, the S-transform de-noised the signal in an intensity-independent manner when reducing the 60 Hz interference. In both a real ECG signal from the MIT database and natural brain activity contaminated with 60 Hz interference, the S-transform also displayed superior merit to a notch filter in the aspect of reducing noise and preserving the signal. Based on these data, a novel application of the S-transform for removing powerline interference is established.
A Novel Two-Stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision
(2008-08-01)
Liang, Sheng-Fu
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Lu, Shih-Mao
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Chang, Jyh-Yeong
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Lin, Chin-Teng (CT)
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交大名義發表
In this paper, a novel two-stage noise removal algorithm to deal with impulse noise is proposed. In the first stage, an adaptive two-level feedforward neural network (NN) with a back-propagation training algorithm was applied to remove the noise cleanly and keep the uncorrupted information well. In the second stage, the fuzzy decision rules inspired by the human visual system (HVS) are proposed to classify the image pixels into human perception sensitive class and nonsensitive class, and to compensate the blur of the edge and the destruction caused by the median filter. An NN is proposed to enhance the sensitive regions with higher visual quality. According to the experimental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and smoothness in edge regions.
A robust in-car digital image stabilization technique
(2007-03-01)
Hsu, Sheng-Che
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Liang, Sheng-Fu
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Fan, Kang-Wei
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Lin, Chin-Teng
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資訊工程學系
Machine vision is a key technology used in an intelligent transportation system (ITS) to augment human drivers' visual capabilities. For the in-car applications, additional motion components are usually induced by disturbances such as the bumpy ride of the vehicle or the steering effect, and they will affect the image interpretation processes that is required by the motion field (motion vector) detection in the image. In this paper, a novel robust in-car digital image stabilization (DIS) technique is proposed to stably remove the unwanted shaking phenomena in the image sequences captured by in-car video cameras without the influence caused by moving object (front vehicles) in the image or intentional motion of the car, etc. In the motion estimation process, the representative point matching (RPM) module combined with the inverse triangle method is used to determine and extract reliable motion vectors in plain images that lack features or contain a large low-contrast area to increase the robustness in different imaging conditions, since most of the images captured by in-car video cameras include large low-contrast sky areas. An adaptive background evaluation model is developed to deal with irregular images that contain large moving objects (front vehicles) or a low-contrast area above the skyline. In the motion compensation processing, a compensating motion vector (CMV) estimation method with an inner feedback-loop integrator is proposed to stably remove the unwanted shaking phenomena in the images without losing the effective area of the images with a constant motion condition. The proposed DIS technique was applied to the on-road captured video sequences with various irregular, conditions for performance demonstrations.
Adaptive EEG-based alertness estimation system by using ICA-based fuzzy neural networks
(2006-11-01)
Lin, Chin-Teng
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Ko, Li-Wei
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Chung, I-Fang
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Huang, Teng-Yi
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Chen, Yu-Chieh
Drivers' fatigue has been implicated as a causal factor in many accidents. The development of human cognitive state monitoring system for the drivers to prevent accidents behind the steering wheel has become a major focus in the field of safety driving. It requires a technique that can continuously monitor and estimate the alertness level of drivers. The difficulties in developing such a system are lack of significant index for detecting drowsiness and the interference of, the complicated noise in a realistic and dynamic driving environment. An adaptive alertness estimation methodology based on electroencephalogram, power spectrum analysis, independent component analysis (ICA), and fuzzy neural network (FNNs) models is proposed in this paper or continuously monitoring driver's drowsiness level with concurrent changes in the alertness level. A novel adaptive feature selection mechanism is developed for automatically selecting effective frequency bands of ICA components for realizing an on-line alertness monitoring system based on the, correlation analysis between the time-frequency power spectra of ICA components and the driving errors defined as the deviation between the center of the vehicle and the cruising lane in the virtual-reality driving environment. The mechanism also provides effective and efficient features that can be fed into ICA-mixture-model-based self-constructing FNN to indirectly estimate driver's drowsiness level expressed by approximately and predicting the driving error.
An EEG-based Subject- and Session-independent Drowsiness Detection
(2008-01-01)
Lin, Chin-Teng
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Pal, Nikhil R.
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Chuang, Chien-Yao
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Jung, Tzyy-Ping
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Ko, Li-Wei
Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface and cognitive neuroscience. Most previous psychophysiological studies of cognitive changes have attempted to use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness suggests that for many operators, group statistics cannot be used to accurately predict changes in cognitive states. Attempts have also been made to build a subject-dependent model for each individual based on his/her pilot data to account for individual variability. However, such methods assume the cross-session variability in EEG dynamics to be negligible, which could be problematic due to electrode displacements, environmental noises, and skin-electrode impedance. Here first we show that the EEG power in the alpha and theta bands are strongly correlated with changes in the subject's cognitive state reflected through his driving performance and hence his departure from alertness. Then under very mild and realistic assumptions we derive a model for the alert state of the person using EEG power in the alpha and theta bands. We demonstrate that deviations (computed by Mahalanobis distance) of the EEG power in the alpha and theta bands from the corresponding alert models are correlated to the changes in the driving performance. Finally, for detection of drowsiness we use a linear combination of deviations of the EEG power in the alpha band and theta band from the respective alert models that best correlates with subject's changing level of alertness, indexed by subject's behavioral response in the driving task. This approach could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.
An energy efficient real-time seizure detection method in rats with spontaneous temporal lobe epilepsy
(2013)
Wang, Yu-Lin
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Liang, Sheng-Fu
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Shaw, Fu-Zen
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Huang, Yu-Shin
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Chen, Yin-Lin
The presence of an on-line seizure detection system could drive an antiepileptic stimulator in real time to suppress seizure generation and to enhance the patients' safety and quality of life. In this paper, the continuous long-term EEGs of three Wistar rats with spontaneous temporal lobe seizure were analyzed. We proposed the development of an energy efficient real-time seizure detection method that employs a hierarchical architecture. The first stage was used to fast detect the seizure-like EEG segment, and a classifier was utilized in the second stage for final confirmation. Only when a suspected seizure segment is found, the second stage is activated. With 2-staged architecture, it saved about 99.4% computation energy in the experiment. Therefore, it is useful to improve the longevity of the closed-loop seizure control system. Three classifiers, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM), were applied for comparison. From the experimental results, three classifiers yielded the comparable performances. However, considering of the trade-off between detection performances and power consumption, LDA which yielded the 100% detection rate, 0.22 FP/hr, and 1.69 s detection latency is suggested for a portable closed-loop seizure controller.
An EOG-Based Automatic Sleep Scoring System and Its Related Application in Sleep Environmental Control
(2014-01-01)
Kuo, Chih-En
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Liang, Sheng-Fu
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Lee, Yi-Chieh
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Cherng, Fu-Yin
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Lin, Wen-Chieh
Human beings spend approximately one third of their lives sleeping. Conventionally, to evaluate a subjects sleep quality, all-night polysomnogram (PSG) readings are taken and scored by a well-trained expert. Unlike a bulky PSG or EEG recorder on the head, the development of an electrooculogram (EOG)-based automatic sleep-staging system will enable physiological computing systems (PhyCS) to progress toward easy sleep and comfortable monitoring. In this paper, an EOG-based sleep scoring system is proposed. EOG signals are also coupling some of sleep characteristics of EEG signals. Compared to PSG or EEG recordings, EOG has the advantage of easy placement, and can be operated by the user individually at home. The proposed method was found to be more than 83% accurate when compared with the manual scorings applied to sixteen subjects. In addition to sleep-quality evaluation, the proposed system encompasses adaptive brightness control of light according to online monitoring of the users sleep stages. The experiments show that the EOG-based sleep scoring system is a practicable solution for homecare and sleep monitoring due to the advantages of comfortable recording and accurate sleep staging.
An HVS-Directed neural-network-based image resolution enhancement scheme for image resizing
(2007-08-01)
Lin, Chin-Teng
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Fan, Kang-Wei
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Pu, Her-Chang
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Lu, Shih-Mao
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Liang, Sheng-Fu
In this paper, a novel human visual system (HVS)directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the HVS is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. Bilinear interpolation is used to interpolate the nonsensitive regions and a neural network is proposed to interpolate the sensitive regions along edge directions. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce a higher visual quality for the interpolated image than the conventional interpolation methods.
Assessment of Musical Training Induced Neuroplasticity by Auditory Event Related Potentials and Neural Networks
(2009)
Liang, Sheng-Fu
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Liu, Chi-Sheng
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Chang, Wan-Lin
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Tsao, Yu-Hsiang
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Ko, Li-Wei
Music provides a tool to study numerous aspects of neuroscience from motion-skill to emotion since listening to and producing music involves many brain functions. The musician\'s brain is also regarded as an ideal model to investigate plasticity of the human brain. In this paper, an EEG-based neural network is proposed to assess neuroplasticity induced by musical training. A musical chord perception experiment is designed to acquire and compare the behavioral and neural responses of musicians and non-musicians. The ERPs elicited by the consonant and dissonant chords are combined together as the features of the model. The principle component analysis (PCA) is used to reduce feature dimensions and the dimension-reduced features are input to a feedforward neural network to recognize the brain potentials belong to a musician or a non-musician. The accuracy can reach 97% in average for leave-one-out cross validation of six subjects in this experiment. It demonstrates the feasibility of assessing effects of musical training by ERP signals elicited by musical chord perception.
Automated recognition system to classify subcellular protein localizations in images of different cell lines acquired by different imaging systems
(2008-04-01)
Tsai, Yuh-Show
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Chung, I-Fang
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Simpson, Jeremy C.
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Lee, Mei-I
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Hsiung, Chia-Cheng
Systemic analysis of subcellular protein localization (location proteomics) provides clues for understanding gene functions and physiological condition of the cells. However, recognition of cell images of subcellular structures highly depends on experience and becomes the rate-limiting step when classifying subcellular protein localization. Several research groups have extracted specific numerical features for the recognition of subcellular protein localization, but these recognition systems are restricted to images of single particular cell line acquired by one specific imaging system and not applied to recognize a range of cell image sources. In this study, we establish a single system for automated subcellular structure recognition to identify cell images from various sources. Two different sources of cell images, 317 Vero (http://gfp-cdna.embl.de) and 875 CHO cell images of subcellular structures, were used to train and test the system. When the system was trained by a single source of images, the recognition rate is high and specific to the trained source. The system trained by the CHO cell images gave high average recognition accuracy for CHO cells of 96%, but this was reduced to 46% with Vero images. When we trained the system using a mixture of CHO and Vero cell images, an average accuracy of recognition reached 86.6% for both CHO and Vero cell images. The system can reject images with low confidence and identify the cell images correctly recognized to avoid manual reconfirmation. In summary, we have established a single system that can recognize subcellular protein localizations from two different sources for location-proteomic studies.
Automatic stage scoring of single-channel sleep EEG based on multiscale permutation entropy
(2011)
Kuo, Chih-En
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Liang, Sheng-Fu
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智慧型仿生系統研究中心
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Biomimetic Systems Research Center
Multiscale entropy is a recently developed method to estimate complexity associated with the long-range temporal correlation of a time series. Since sleep EEG patterns also change regularly from light to deep sleep states, we firstly applied multiscale permutation entropy (MPE) to analysis sleep EEG to investigate the relations between changes of sleep stages and the MPE values. It was observed that correlation coefficient between the averaged MPE values of sleep EEG and the manual scoring of sleep stages can reach over 0.7. Then a MPE-based sleep scoring method for single channel EEG was developed. After training based on the data from 10 subjects, the overall sensitivity of the proposed automatic sleep scoring method combining MPE, autoregressive models, and linear discriminant analysis can reach 89.1% evaluated by the data of the other 10 subjects. Due to high accuracy and requiring only single-channel EEG, the proposed method has good applicability for sleep monitoring and home cares.
Classification of driver's cognitive responses using nonparametric single-trial EEG analysis
(2007)
Lin, Chin-Teng
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Ko, Li-Wei
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Lin, Ken-Li
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Kuo, Bor-Chen
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Liang, Sheng-Fu
Accidents caused by errors and failures in human performance among traffic fatalities have a high rate causing death and become an important issue in public security. The key problem causing these car accidents is mainly because that the drivers failed 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 ongoing assessment of driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-fight experiments in a virtual-reality (VR) dynamic driving environment. Three different feature extraction methods including Nonparametric Weighted Feature Extraction (NWFE), Principlal Component Analysis (PCA), Discriminant Analysis Feature Extraction (DAFE) are applied to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data can be classified with fewer features and their classification results are compared by utilizing three different classifiers including Gaussian classifier (GC), k Nearest neighbor classification (KNNC), and Naive Bayes Classifier (NBC). Experimental results show that the successful rate of Nonparametric Weighted Feature Extraction combined with Gaussian classifier is higher more than 10% compared with other combinations. It also demonstrates the feasibility of detecting and analyzing single-trail ERP signals that represent operators' cognitive states and responses to task events.
Closed-loop seizure control on epileptic rat models
(2011-08-01)
Liang, Sheng-Fu
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Liao, Yi-Cheng
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Shaw, Fu-Zen
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Chang, Da-Wei
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Young, Chung-Ping
In this paper numerous alternative treatments in addition to pharmacological therapy are proposed for their use in epileptic patients. Epileptic animal models can play a crucial role in the performance evaluation of new therapeutic techniques. The objective of this research is to first develop various epileptic rat models; second, develop a portable wireless closed-loop seizure controller including on-line seizure detection and real-time electrical stimulation for seizure elimination; and third, apply the developed seizure controller to the animal models to perform on-line seizure elimination. The closed-loop seizure controller was applied to three Long-Evans rats with spontaneous spike-wave discharges (non-convulsive) and three Long-Evans rats with epileptiform activities induced by pentylenetetrazol (PTZ) injection (convulsive) for evaluation. The seizure detection accuracy is greater than 92% (up to 99%), and averaged seizure detection latency is less than 0.6 s for both spontaneous non-convulsive and PTZ-induced convulsive seizures. The average false stimulation rate is 3.1%. Near 30% of PTZ-induced convulsive seizures need more than two times of 0.5 s electrical stimulation for suppression and 90% of the non-convulsive seizures can be suppressed by only one 0.5 s electrical stimulation.