Browsing by Author "Jung, Tzyy-Ping"

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A Brain-Computer Interface with Real-Time Independent Component Analysis for Biomedical Applications
(2012)
Hsieh, Zong-Han
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Fang, Wai-Chi
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Jung, Tzyy-Ping
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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
This paper presents a brain-computer interface with real-time independent component analysis for biomedical applications. EEG-based BCI technology using cell-phone and Bluetooth transmission of bio-signals has been established in previous studies, and experimental results show that subjects should avoid eye blink who are under test to increase the accuracy. In order to enhance the dialing system, the authors integrate a real-time ICA chip to artifacts arising from eye blinking. The designed system enables the acquisition and subsequent ICA elaboration signals in real-time, the result shows suitable for BCI applications. The efficiency of the implemented technique has been experimentally demonstrated.
A brain-machine interface using dry-contact, low-noise EEG sensors
(2008-01-01)
Sullivan, Thomas J.
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Deiss, Stephen R.
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Jung, Tzyy-Ping
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Cauwenberghs, Gert
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交大名義發表
Electroencephalograph (EEG) recording systems offer a versatile, non-invasive window on the brain's spatiotemporal activity for many neuroscience and clinical applications. Our research aims to improve the convenience and mobility of EEG recording by eliminating the need for conductive gel and creating sensors that fit into a scalable array architecture. The EEG dry-contact electrodes are created with micro-electrical-mechanical system (MEMS) technology. Each channel of our analog signal processing front-end comes on a custom-built, dime-sized circuit board which contains an amplifier, filters, and analog-to-digital conversion. A daisy-chain configuration between boards with bit-serial output reduces the wiring needed. A system consisting of seven sensors is demonstrated in a real-world setting. Consuming just 3 mW, it is suitable for mobile applications. The system achieves an input-referred noise of 0.28 mu V-rms in the signal band of 1 to 100 Hz, comparable to the best medical-grade systems in use. Noise behavior across the daisy-chain is characterized, alpha-band rhythms are detected, and an eye-blink study is demonstrated.
A Low Power Independent Component Analysis Processor in 90nm CMOS Technology for EEG Signal Processing Systems
(2011)
Chen, Chiu-Kuo
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Wang, Yi-Yuan
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Hsieh, Zong-Han
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Chua, Ericson
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Fang, Wai-Chi
This paper presents a low-power VLSI implementation of a 4-channel independent component analysis (ICA) processor for portable EEG signal processing applications. The low-power scheme employed for this ICA chip is based on power gating and clock gating by utilizing Cadence common power flow (CPF) low-power methodology and also according to the characteristics of ICA training behavior using different training window sizes. The proposed low power ICA processor can separate EEG and mixed EEG-like super-Gaussian signals in real time. The chip can be operated at up to 60MHz working frequency and a maximum sampling rate of 9.394 KHz for EEG signals. The power consumption of this chip is 0.690 mW during training under the condition of 0.9V supply voltage and 10 MHz operating frequency using UMC 90nm High-Vt CMOS technology. The total chip area is 1230 x 1230 mu m(2).
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.
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 EEG-Based Fatigue Detection and Mitigation System
(2016-06)
Huang, Kuan-Chih
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Huang, Teng-Yi
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Chuang, Chun-Hsiang
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King, Jung-Tai
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Wang, Yu-Kai
Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual\'s internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant\'s EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha-and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject\'s cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.
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 Effective Chip Implementation of A Real-time Eight-channel EEG Signal Processor Based on On-line Recursive ICA Algorithm
(2012)
Shih, Wei-Yeh
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Huang, Kuan-Ju
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Chen, Chiu-Kuo
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Fang, Wai-Chi
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Cauwenberghs, Gert
This paper presents an effective chip implementation of a real-time eight-channel electroencephalogram signal processor based on on-line recursive independent component analysis (ORICA) algorithm. The system architecture is composed of a memory unit, a whitening unit, an ORICA training unit, and an ORICA computation unit. The proposed architecture is implemented using TSMC 90 nm CMOS technology. It occupies a core area of 800x800 mu m(2) and consumes 4.18 mW at a core supply voltage of 1.0 V and 50 MHz clock operating frequency. Simulated super and sub-Gaussian signals are used to verify the system. The separated signals match those obtained using off-line Matlab-based analysis.
An Efficient ASIC Implementation of 16-channel On-line Recursive ICA Processor for Real-time EEG System
(2014-01-01)
Fang, Wai-Chi
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Huang, Kuan-Ju
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Chou, Chia-Ching
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Chang, Jui-Chung
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Cauwenberghs, Gert
This is a proposal for an efficient very-large-scale integration (VLSI) design, 16-channel on-line recursive independent component analysis (ORICA) processor ASIC for real-time EEG system, implemented with TSMC 40 nm CMOS technology. ORICA is appropriate to be used in real-time EEG system to separate artifacts because of its highly efficient and real-time process features. The proposed ORICA processor is composed of an ORICA processing unit and a singular value decomposition (SVD) processing unit. Compared with previous work [1], this proposed ORICA processor has enhanced effectiveness and reduced hardware complexity by utilizing a deeper pipeline architecture, shared arithmetic processing unit, and shared registers. The 16-channel random signals which contain 8-channel super-Gaussian and 8-channel sub-Gaussian components are used to analyze the dependence of the source components, and the average correlation coefficient is 0.95452 between the original source signals and extracted ORICA signals. Finally, the proposed ORICA processor ASIC is implemented with TSMC 40 nm CMOS technology, and it consumes 15.72 mW at 100 MHz operating frequency.
An Efficient VLSI Implementation of On-line Recursive ICA Processor for Real-time Multi-channel EEG Signal Separation
(2013)
Shih, Wei-Yeh
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Liao, Jui-Chieh
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Huang, Kuan-Ju
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Fang, Wai-Chi
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Cauwenberghs, Gert
This paper presents an efficient VLSI implementation of on-line recursive ICA (ORICA) processor for real-time multi-channel EEG signal separation. The proposed design contains a system control unit, a whitening unit, a singular value decomposition unit, a floating matrix multiply unit and, and an ORICA weight training unit. Because the input sample rate of the ORICA processor is 128 Hz, the ORICA processor should produce independent components before the next sample is input in 1/128 s. Under the timing constraints of commutating multi-channel ORICA in real time, the design of the ORICA processor is a mixed architecture, which is designed as different hardware parallelism according to the complexity of processing units. The shared arithmetic processing unit and shared register can reduce hardware complexity and power consumption. The proposed design is implemented used TSMC 90nm CMOS technology with 8-channel EEG processing in 128 Hz sample rate of raw data and consumes 2.827 mW at 50 MHz clock rate. The performance of the proposed design is also shown to reach 0.0078125 s latency after each EEG sample time, and the average correlation coefficient between the original source signals and extracted ORICA signals for each 1s frame is 0.9763.
Arousing Feedback Rectifies Lapse in Performance and Corresponding EEG Power Spectrum
(2010)
Jung, Tzyy-Ping
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Huang, Kuan-Chih
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Chuang, Chun-Hsiang
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Chen, Jian-Ann
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Ko, Li-Wei
This study explores electroencephalographic (EEG) dynamics and behavioral changes in response to arousing auditory signals presented to individuals experiencing momentary cognitive lapses. Arousing auditory feedback was delivered to the subjects in half of the non-responded lane-deviation events during a sustained-attention driving task, which immediately agitated subject\'s responses to the events. The improved behavioral performance was accompanied by concurrent power suppression in the theta- and alpha-bands in the lateral occipital cortices. This study further explores the feasibility of estimating the efficacy of arousing feedback presented to the drowsy subjects by monitoring the changes in EEG power spectra.
Associations Among Emotional State, Sleep Quality, and Resting-State EEG Spectra: A Longitudinal Study in Graduate Students
(2020-04-01)
Komarov, Oleksii
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Ko, Li-Wei
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Jung, Tzyy-Ping
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交大名義發表
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生物資訊及系統生物研究所
University students are routinely influenced by a variety of natural stressors and experience irregular sleep-wake cycles caused by the necessity to trade sleep for studying while dealing with academic assignments. Often these factors result in long-term issues with daytime sleepiness, emotional instability, and mental exhaustion, which may lead to difficulties in the educational process. This study introduces the Daily Sampling System (DSS) implemented as a smartphone application, which combines a set of self-assessment scales for evaluating variations in the emotional state and sleep quality throughout a full academic term. In addition to submitting the daily sampling scores, the participants regularly filled in the Depression, Anxiety, and Stress Scales (DASS) reports and took part in resting-state EEG data recording immediately after report completion. In total, this study collected 1835 daily samples and 94 combined DASS with EEG datasets from 18 university students (aged 23-27 years), with 79.3 +/- 15.3% response ratio in submitting the daily reports during an academic semester. The results of pairwise testing and multiple regression analysis demonstrate that the daily level of self-perceived fatigue correlates positively with stress, daytime sleepiness, and negatively with alertness on awakening, self-evaluated sleep quality, and sleep duration. The spectral analysis of the EEG data reveals a significant increase in the resting-state spectral power density across the theta and low-alpha frequency bands associated with increased levels of anxiety and stress. Additionally, the state of depression was accompanied by an intensification of high-frequency EEG activity over the temporal regions. No significant differences in prefrontal alpha power asymmetry were observed under the described experimental conditions while comparing the states of calmness and emotional arousal of the participants for the three conditions of depression, anxiety, and stress.
Augmenting VR/AR Applications with EEG/EOG Monitoring and Oculo-Vestibular Recoupling
(2016-01-01)
Zao, John K.
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Jung, Tzyy-Ping
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Chang, Hung-Ming
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Gan, Tchin-Tze
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Wang, Yu-Te
Head-mounted virtual reality and augmented reality displays (a. k. a. VR/AR goggles) created a revolutionary multimedia genre that is seeking ever-broadening applications and novel natural human interfaces. Adding neuromonitoring and neurofeedback to this genre is expected to introduce a new dimension to user interaction with the cyber-world. This study presents the development of a Neuromonitoring VR/AR Goggle armed with electroencephalo- gram and electrooculogram sensors, programmable milli-Ampere current stimulators and wireless fog/cloud computing support. Beside of its potential use in mitigating cybersickness, this device may have potential applications in augmented cognition ranging from feedback-controlled perceptual training to on-line learning and virtual social interactions. A prototype of the device has been made from a Samsung Gear VR for S6. This study explains its technical design to ensure precision data sampling, synchronous event marking, real-time signal processing and big data cloud computing support. This study also demonstrates the effective-ness in measuring the event-related potentials during a visual oddball experiment.
Automatic Design for Independent Component Analysis based Brain-computer Interfacing
(2013)
Chuang, Chun-Hsiang
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Lin, Yuan-Pin
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Ko, Li-Wei
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Jung, Tzyy-Ping
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Lin, Chin-Teng
This study proposes a new framework, independent component ensemble, to leverage the acquired knowledge into a truly automatic and on-line EEG-based brain-computer interfacing (BCI). The envisioned design includes: (1) independent source recover using independent component analysis (ICA) (2) automatic selection of the independent components of interest (ICi) associated with human behaviors; (3) multiple classifiers with a parallel constructing and processing structure; and (4) a simple fusion scheme to combine the decisions from multiple classifiers. Its implications in BCI are demonstrated through a sample application: cognitive-state monitoring of participants performing a realistic sustained-attention driving task. Empirical results showed the proposed ensemble design could provide an improvement of 7%similar to 15% in overall accuracy for the classification of the arousal state and the driving performance. In summary, constructing ICi-ensemble classifiers and combining their outputs demonstrates a practical option for ICA-based BCIs to reduce the risk of not obtaining any desired independent source or selecting an inadequate component. Most importantly, the ensemble design for integrating information across multiple brain areas creates potentials for developing more complicated BCIs for real world applications.
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.
Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data
(2018-11-12)
Fonseca, Andre
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Kerick, Scott
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King, Jung-Tai
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Lin, Chin-Teng
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Jung, Tzyy-Ping
The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.
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.
Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra
(2013-10-01)
Lin, Chin-Teng
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Huang, Kuan-Chih
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Chuang, Chun-Hsiang
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Ko, Li-Wei
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Jung, Tzyy-Ping
Objective. This study explores the neurophysiological changes, measured using an electroencephalogram (EEG), in response to an arousing warning signal delivered to drowsy drivers, and predicts the efficacy of the feedback based on changes in the EEG. Approach. Eleven healthy subjects participated in sustained-attention driving experiments. The driving task required participants to maintain their cruising position and compensate for randomly induced lane deviations using the steering wheel, while their EEG and driving performance were continuously monitored. The arousing warning signal was delivered to participants who experienced momentary behavioral lapses, failing to respond rapidly to lane-departure events (specifically the reaction time exceeded three times the alert reaction time). Main results. The results of our previous studies revealed that arousing feedback immediately reversed deteriorating driving performance, which was accompanied by concurrent EEG theta-and alpha-power suppression in the bilateral occipital areas. This study further proposes a feedback efficacy assessment system to accurately estimate the efficacy of arousing warning signals delivered to drowsy participants by monitoring the changes in their EEG power spectra immediately thereafter. The classification accuracy was up 77.8% for determining the need for triggering additional warning signals. Significance. The findings of this study, in conjunction with previous studies on EEG correlates of behavioral lapses, might lead to a practical closed-loop system to predict, monitor and rectify behavioral lapses of human operators in attention-critical settings.
Cell-phone Based Drowsiness Monitoring and Management System
(2012)
Wang, Yu-Te
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Cheng, Chung-Kuan
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Huang, Kuan-Chih
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Lin, Chin-Teng
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Wang, Yijun
In recent years, driving fatigue and cognitive lapses have gained increasing attentions in the fields of public security, especially regarding the safe manipulation of vehicles. Studies have explored the relation between electroencephalogram (EEG) and driving behavior and several of them further proposed effective approaches (e. g., auditory feedback) to arouse subjects from drowsiness. However, these studies were performed under laboratory-oriented configurations using tethered, ponderous EEG equipment. In real environments, the setup of bulky cognitive monitoring equipment with a long-prep time is not feasible. Therefore, this study extends previous laboratory work by developing an on-line Drowsiness Monitoring and Management (DMM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. The DMM system can continuously observe EEG dynamics, deliver arousing feedback to users experiencing momentary cognitive lapses, and assess the efficacy of the feedback in near real-time.
Co-modulatory spectral changes in independent brain processes are correlated with task performance
(2012-09-01)
Chuang, Shang-Wen
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Ko, Li-Wei
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Lin, Yuan-Pin
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Huang, Ruey-Song
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Jung, Tzyy-Ping
"This study investigates the independent modulators that mediate the power spectra of electrophysiological processes, measured by electroencephalogram (EEG), in a sustained-attention experiment. EEG and behavioral data were collected during 1-2 hour virtual-reality based driving experiments in which subjects were instructed to maintain their cruising position and compensate for randomly induced drift using the steering wheel. Independent component analysis (ICA) applied to 30-channel EEG data separated the recorded EEG signals into a sum of maximally temporally independent components (ICs) for each of 30 subjects. Logarithmic spectra of resultant IC activities were then decomposed by principal component analysis, followed by ICA, to find spectrally fixed and temporally independent modulators (IM). Across subjects, the spectral ICA consistently found four performance-related independent modulators: delta, delta-theta, alpha, and beta modulators that multiplicatively affected the spectra of spatially distinct IC processes when the participants experienced waves of alternating alertness and drowsiness during long-hour simulated driving. The activation of the delta-theta modulator increased monotonically as subjects' task performances decreased. Furthermore, the time courses of the theta-beta modulator were highly correlated with concurrent changes in driving errors across subjects (r = 0.77 +/- 0.13). (c) 2012 Elsevier Inc. All rights reserved."