My name is Chun-Hsiang (Michael) Chuang. I worked at the University of Technology Sydney (UTS) as a Lecturer (~Assistant Professor in US academic rank) from 2016 to 2017. Currently, I am an Assistant Professor in the Department of Computr Science & Engineering of the National Taiwan Ocean University, a Honorary Associate of FEIT, UTS, a Core Member of the Centre of Artificial Intelligence, UTS.
For more information, please go to www.chchuang.me
PhD: I received my PhD in electrical and control engineering from National Chiao Tung University (NCTU) in Taiwan in March 2014. My PhD project focused on exploring the principles and methods that can be used to design individualised real-time neuroergonomic systems to enhance operator situational awareness and decision making under several forms of cognitive fatigue, and to thereby improve total human-system performance. This research led to 23 research publications in high-ranking journals including Nature Sci. Rep., IEEE TNNLS, IEEE TNSRE and NeuroImage and major international conferences including IJCNN, EMBC, ISCAS, etc.
Work Experience: I was offered a full-time position at the Brain Research Center (BRC) and the Department of Electrical and Computer Engineering (ECE), NCTU, as a Postdoctoral Researcher in 2014. One year later, I received a recommendation for promotion to Assistant Researcher in recognition of my exceptional research excellence.
I have been invited to serve as a referee for high-impact journals such as NeuroImage, J. Neuroeng. Rehabil., IEEE Trans. Neural Syst. Rehabil. Eng., IEEE Trans. Neural Netw. Learn. Syst., IEEE Trans. Fuzzy Syst., IEEE Trans. Cogn. Devel. Syst., and Integr. Comput. Aided Eng.;
I served as CI of two research projects funded by the US Army Research Lab. One was “Investigate the multitasking-related neural co-modulation among independent brain processes”, and the other was “Online fatigue/lapse detection for adapting BCI technologies”. The two projects received AUD$~400K in total in grant funding;
I was awarded funding to visit UCSD in 2012-2013 the Graduate Students Study Abroad Program, the Outstanding PhD Student Award in 2014, and the Best Live Demo Award at the 3rd IEEE EMB/CAS/SMC Workshop on Brain-Machine-Body Interface for my presentation on BCI technology – Mobile and Wireless EEG System for Predicting Lapse;
Myself and my colleagues engaged in a technology transfer with AmTRAN Inc.
Data Mining, Machine Learning, Biomedical Signal Processing, Brain-Computer Interface
Advanced Data Analytics Algorithms (32513)
Advanced Data Analytics (31005)
Technology Research Preparation (32144)
Wu, D., King, J.T., Chuang, C.H., Lin, C.T. & Jung, T.P. 2018, 'Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)', IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, pp. 771-781.View/Download from: Publisher's site
© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%.
Cao, Z., Lai, K.-.L., Lin, C.-.T., Chuang, C.-.H., Chou, C.-.C. & Wang, S.-.J. 2018, 'Exploring resting-state EEG complexity before migraine attacks.', Cephalalgia: an international journal of headache, pp. 333102417733953-333102417733953.View/Download from: Publisher's site
Objective Entropy-based approaches to understanding the temporal dynamics of complexity have revealed novel insights into various brain activities. Herein, electroencephalogram complexity before migraine attacks was examined using an inherent fuzzy entropy approach, allowing the development of an electroencephalogram-based classification model to recognize the difference between interictal and preictal phases. Methods Forty patients with migraine without aura and 40 age-matched normal control subjects were recruited, and the resting-state electroencephalogram signals of their prefrontal and occipital areas were prospectively collected. The migraine phases were defined based on the headache diary, and the preictal phase was defined as within 72 hours before a migraine attack. Results The electroencephalogram complexity of patients in the preictal phase, which resembled that of normal control subjects, was significantly higher than that of patients in the interictal phase in the prefrontal area (FDR-adjusted p<0.05) but not in the occipital area. The measurement of test-retest reliability (n=8) using the intra-class correlation coefficient was good with r1=0.73 ( p=0.01). Furthermore, the classification model, support vector machine, showed the highest accuracy (76±4%) for classifying interictal and preictal phases using the prefrontal electroencephalogram complexity. Conclusion Entropy-based analytical methods identified enhancement or "normalization" of frontal electroencephalogram complexity during the preictal phase compared with the interictal phase. This classification model, using this complexity feature, may have the potential to provide a preictal alert to migraine without aura patients.
Lin, C.-.T., Chiu, T.-.C., Wang, Y.-.K., Chuang, C.-.H. & Gramann, K. 2018, 'Granger causal connectivity dissociates navigation networks that subserve allocentric and egocentric path integration.', Brain Research, vol. 1679, pp. 91-100.View/Download from: UTS OPUS or Publisher's site
Studies on spatial navigation demonstrate a significant role of the retrosplenial complex (RSC) in the transformation of egocentric and allocentric information into complementary spatial reference frames (SRFs). The tight anatomical connections of the RSC with a wide range of other cortical regions processing spatial information support its vital role within the human navigation network. To better understand how different areas of the navigational network interact, we investigated the dynamic causal interactions of brain regions involved in solving a virtual navigation task. EEG signals were decomposed by independent component analysis (ICA) and subsequently examined for information flow between clusters of independent components (ICs) using direct short-time directed transfer function (sdDTF). The results revealed information flow between the anterior cingulate cortex and the left prefrontal cortex in the theta (4-7Hz) frequency band and between the prefrontal, motor, parietal, and occipital cortices as well as the RSC in the alpha (8-13Hz) frequency band. When participants prefered to use distinct reference frames (egocentric vs. allocentric) during navigation was considered, a dominant occipito-parieto-RSC network was identified in allocentric navigators. These results are in line with the assumption that the RSC, parietal, and occipital cortices are involved in transforming egocentric visual-spatial information into an allocentric reference frame. Moreover, the RSC demonstrated the strongest causal flow during changes in orientation, suggesting that this structure directly provides information on heading changes in humans.
Lin, C.T., Hsieh, T.Y., Liu, Y.T., Lin, Y.Y., Fang, C.N., Wang, Y.K., Yen, G., Pal, N.R. & Chuang, C.H. 2018, 'Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 5, pp. 950-962.View/Download from: UTS OPUS or Publisher's site
© 1989-2012 I EEE. The class imbalance problem in machine learning occurs when certain classes are underrepresented relative to the others, leading to a learning bias toward the majority classes. To cope with the skewed class distribution, many learning methods featuring minority oversampling have been proposed, which are proved to be effective. To reduce information loss during feature space projection, this study proposes a novel oversampling algorithm, named minority oversampling in kernel adaptive subspaces (MOKAS), which exploits the invariant feature extraction capability of a kernel version of the adaptive subspace self-organizing maps. The synthetic instances are generated from well-trained subspaces and then their pre-images are reconstructed in the input space. Additionally, these instances characterize nonlinear structures present in the minority class data distribution and help the learning algorithms to counterbalance the skewed class distribution in a desirable manner. Experimental results on both real and synthetic data show that the proposed MOKAS is capable of modeling complex data distribution and outperforms a set of state-of-the-art oversampling algorithms.
Lin, C., Liu, Y.-.T., Wu, S.-.L., Cao, Z., Wang, Y., Huang, C.-.S., King, J.-.T., Chen, S.-.A., Lu, S.-.W. & Chuang, C. 2017, 'EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method', IEEE Systems Man and Cybernetics Magazine, vol. 3, no. 4, pp. 16-26.View/Download from: UTS OPUS or Publisher's site
This article presents the latest BCI-related research done in our group. Our previous work applied computational intelligence technology in BCIs to inspire detailed investigations of practical issues in real-life applications. Novel EEG devices featuring dry electrodes facilitate and speed up electrode positioning before recording and allow subjects to move freely in operational environments. We also demonstrate the feasibility of applying CCA, RBFNs, effective connectivity measurements, and D-S theory to help BCIs extract informative knowledge from brain signals. Two recent trends in research in the computational and artificial intelligence community, big data and deep learning, are expected to impact the direction and development of BCIs.
Lin, C.T., Chuang, C.H., Cao, Z., Singh, A.K., Hung, C.S., Yu, Y.H., Nascimben, M., Liu, Y.T., King, J.T., Su, T.P. & Wang, S.J. 2017, 'Forehead EEG in Support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment', IEEE Access, vol. 5, pp. 10612-10621.View/Download from: UTS OPUS or Publisher's site
© 2013 IEEE. There are current limitations in the recording technologies for measuring EEG activity in clinical and experimental applications. Acquisition systems involving wet electrodes are time-consuming and uncomfortable for the user. Furthermore, dehydration of the gel affects the quality of the acquired data and reliability of long-term monitoring. As a result, dry electrodes may be used to facilitate the transition from neuroscience research or clinical practice to real-life applications. EEG signals can be easily obtained using dry electrodes on the forehead, which provides extensive information concerning various cognitive dysfunctions and disorders. This paper presents the usefulness of the forehead EEG with advanced sensing technology and signal processing algorithms to support people with healthcare needs, such as monitoring sleep, predicting headaches, and treating depression. The proposed system for evaluating sleep quality is capable of identifying five sleep stages to track nightly sleep patterns. Additionally, people with episodic migraines can be notified of an imminent migraine headache hours in advance through monitoring forehead EEG dynamics. The depression treatment screening system can predict the efficacy of rapid antidepressant agents. It is evident that frontal EEG activity is critically involved in sleep management, headache prevention, and depression treatment. The use of dry electrodes on the forehead allows for easy and rapid monitoring on an everyday basis. The advances in EEG recording and analysis ensure a promising future in support of personal healthcare solutions.
Wu, S.L., Liu, Y.T., Hsieh, T.Y., Lin, Y.Y., Chen, C.Y., Chuang, C.H. & Lin, C.T. 2017, 'Fuzzy Integral with Particle Swarm Optimization for a Motor-Imagery-Based Brain-Computer Interface', IEEE Transactions on Fuzzy Systems, vol. 25, no. 1, pp. 21-28.View/Download from: UTS OPUS or Publisher's site
© 2016 IEEE.A brain-computer interface (BCI) system using electroencephalography signals provides a convenient means of communication between the human brain and a computer. Motor imagery (MI), in which motor actions are mentally rehearsed without engaging in actual physical execution, has been widely used as a major BCI approach. One robust algorithm that can successfully cope with the individual differences in MI-related rhythmic patterns is to create diverse ensemble classifiers using the subband common spatial pattern (SBCSP) method. To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence levels for classifiers. The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for offline single-trial classification of MI and real-time control of a robotic arm using MI. This paper represents the first attempt to utilize fuzzy fusion technique to attack the individual differences problem of MI applications in real-world noisy environments. The results of this study demonstrate the practical feasibility of implementing the proposed method for real-world applications.
Cao, Z., Lin, C.-.T., Chuang, C.-.H., Lai, K.-.L., Yang, A.C., Fuh, J.-.L. & Wang, S.-.J. 2016, 'Resting-state EEG power and coherence vary between migraine phases.', The journal of headache and pain, vol. 17, no. 1, p. 102.View/Download from: UTS OPUS or Publisher's site
Migraine is characterized by a series of phases (inter-ictal, pre-ictal, ictal, and post-ictal). It is of great interest whether resting-state electroencephalography (EEG) is differentiable between these phases.We compared resting-state EEG energy intensity and effective connectivity in different migraine phases using EEG power and coherence analyses in patients with migraine without aura as compared with healthy controls (HCs). EEG power and isolated effective coherence of delta (1-3.5 Hz), theta (4-7.5 Hz), alpha (8-12.5 Hz), and beta (13-30 Hz) bands were calculated in the frontal, central, temporal, parietal, and occipital regions.Fifty patients with episodic migraine (1-5 headache days/month) and 20 HCs completed the study. Patients were classified into inter-ictal, pre-ictal, ictal, and post-ictal phases (n=22, 12, 8, 8, respectively), using 36-h criteria. Compared to HCs, inter-ictal and ictal patients, but not pre- or post-ictal patients, had lower EEG power and coherence, except for a higher effective connectivity in fronto-occipital network in inter-ictal patients (p<.05). Compared to data obtained from the inter-ictal group, EEG power and coherence were increased in the pre-ictal group, with the exception of a lower effective connectivity in fronto-occipital network (p<.05). Inter-ictal and ictal patients had decreased EEG power and coherence relative to HCs, which were "normalized" in the pre-ictal or post-ictal groups.Resting-state EEG power density and effective connectivity differ between migraine phases and provide an insight into the complex neurophysiology of migraine.
Chuang, S.-.W., Chuang, C.-.H., Yu, Y.-.H., King, J.-.T. & Lin, C.-.T. 2016, 'EEG Alpha and Gamma Modulators Mediate Motion Sickness-Related Spectral Responses.', International journal of neural systems, vol. 26, no. 2, p. 1650007.View/Download from: UTS OPUS or Publisher's site
Motion sickness (MS) is a common experience of travelers. To provide insights into brain dynamics associated with MS, this study recruited 19 subjects to participate in an electroencephalogram (EEG) experiment in a virtual-reality driving environment. When riding on consecutive winding roads, subjects experienced postural instability and sensory conflict between visual and vestibular stimuli. Meanwhile, subjects rated their level of MS on a six-point scale. Independent component analysis (ICA) was used to separate the filtered EEG signals into maximally temporally independent components (ICs). Then, reduced logarithmic spectra of ICs of interest, using principal component analysis, were decomposed by ICA again to find spectrally fixed and temporally independent modulators (IMs). Results demonstrated that a higher degree of MS accompanied increased activation of alpha (r = 0.421) and gamma (r =0.478) IMs across remote-independent brain processes, covering motor, parietal and occipital areas. This co-modulatory spectral change in alpha and gamma bands revealed the neurophysiological demand to regulate conflicts among multi-modal sensory systems during MS.
Huang, K.-.C., Huang, T.-.Y., Chuang, C.-.H., King, J.-.T., Wang, Y.-.K., Lin, C.-.T. & Jung, T.-.P. 2016, 'An EEG-Based Fatigue Detection and Mitigation System.', International journal of neural systems, vol. 26, no. 4, pp. 1650018-1650018.View/Download from: UTS OPUS or Publisher's site
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.
Lin, C.-.T., Chuang, C.-.H., Kerick, S., Mullen, T., Jung, T.-.P., Ko, L.-.W., Chen, S.-.A., King, J.-.T. & McDowell, K. 2016, 'Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving.', Scientific reports, vol. 6, p. 21353.View/Download from: UTS OPUS or Publisher's site
Fluctuations in attention behind the wheel poses a significant risk for driver safety. During transient periods of inattention, drivers may shift their attention towards internally-directed thoughts or feelings at the expense of staying focused on the road. This study examined whether increasing task difficulty by manipulating involved sensory modalities as the driver detected the lane-departure in a simulated driving task would promote a shift of brain activity between different modes of processing, reflected by brain network dynamics on electroencephalographic sources. Results showed that depriving the driver of salient sensory information imposes a relatively more perceptually-demanding task, leading to a stronger activation in the task-positive network. When the vehicle motion feedback is available, the drivers may rely on vehicle motion to perceive the perturbations, which frees attentional capacity and tends to activate the default mode network. Such brain network dynamics could have major implications for understanding fluctuations in driver attention and designing advance driver assistance systems.
Liu, Y.-.T., Lin, Y.-.Y., Wu, S.-.L., Chuang, C.-.H. & Lin, C.-.T. 2016, 'Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.', IEEE transactions on neural networks and learning systems, vol. 27, no. 2, pp. 347-360.View/Download from: UTS OPUS or Publisher's site
This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.
Liu, Y.T., Lin, C., Chuang, C.H., Wang, Y.K., Huang, S.H., King, J.T., Chen, S.A. & Lu, S.W. 2016, 'Novel Neurotechnology and Computational Intelligence Method Applied to EEG-based Brain-Computer Interfaces', IEEE Systems, Man, and Cybernetics Magazine.
Yu, Y.-.H., Lu, S.-.W., Chuang, C.-.H., King, J.-.T., Chang, C.-.L., Chen, S.-.A., Chen, S.-.F. & Lin, C.-.T. 2016, 'An Inflatable and Wearable Wireless System for Making 32-Channel Electroencephalogram Measurements.', IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol. 24, no. 7, pp. 806-813.View/Download from: UTS OPUS or Publisher's site
Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.
Chuang, C.H., Huang, C.S., Ko, L.W. & Lin, C.T. 2015, 'An EEG-based perceptual function integration network for application to drowsy driving', Knowledge-Based Systems, vol. 80, pp. 143-152.View/Download from: UTS OPUS or Publisher's site
© 2015 Elsevier B.V. All rights reserved. Drowsy driving is among the most critical causes of fatal crashes. Thus, the development of an effective algorithm for detecting a driver's cognitive state demands immediate attention. For decades, studies have observed clear evidence using electroencephalography that the brain's rhythmic activities fluctuate from alertness to drowsiness. Recognition of this physiological signal is the major consideration of neural engineering for designing a feasible countermeasure. This study proposed a perceptual function integration system which used spectral features from multiple independent brain sources for application to recognize the driver's vigilance state. The analysis of brain spectral dynamics demonstrated physiological evidenced that the activities of the multiple cortical sources were highly related to the changes of the vigilance state. The system performances showed a robust and improved accuracy as much as 88% higher than any of results performed by a single-source approach.
Huang, C.-.S., Pal, N.R., Chuang, C.-.H. & Lin, C.-.T. 2015, 'Identifying changes in EEG information transfer during drowsy driving by transfer entropy.', Frontiers in human neuroscience, vol. 9, p. 570.View/Download from: UTS OPUS or Publisher's site
Drowsy driving is a major cause of automobile accidents. Previous studies used neuroimaging based approaches such as analysis of electroencephalogram (EEG) activities to understand the brain dynamics of different cortical regions during drowsy driving. However, the coupling between brain regions responding to this vigilance change is still unclear. To have a comprehensive understanding of neural mechanisms underlying drowsy driving, in this study we use transfer entropy, a model-free measure of effective connectivity based on information theory. We investigate the pattern of information transfer between brain regions when the vigilance level, which is derived from the driving performance, changes from alertness to drowsiness. Results show that the couplings between pairs of frontal, central, and parietal areas increased at the intermediate level of vigilance, which suggests that an enhancement of the cortico-cortical interaction is necessary to maintain the task performance and prevent behavioral lapses. Additionally, the occipital-related connectivity magnitudes monotonically decreases as the vigilance level declines, which further supports the cortical gating of sensory stimuli during drowsiness. Neurophysiological evidence of mutual relationships between brain regions measured by transfer entropy might enhance the understanding of cortico-cortical communication during drowsy driving.
This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time-frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain-behavior relations in operating enviro...
Chuang, C.-.H., Ko, L.-.W., Lin, Y.-.P., Jung, T.-.P. & Lin, C.-.T. 2014, 'Independent component ensemble of EEG for brain-computer interface.', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 2, pp. 230-238.View/Download from: UTS OPUS or Publisher's site
Recently, successful applications of independent component analysis (ICA) to electroencephalographic (EEG) signals have yielded tremendous insights into brain processes that underlie human cognition. Many studies have further established the feasibility of using independent processes to elucidate human cognitive states. However, various technical problems arise in the building of an online brain-computer interface (BCI). These include the lack of an automatic procedure for selecting independent components of interest (ICi) and the potential risk of not obtaining a desired ICi. Therefore, this study proposes an ICi-ensemble method that uses multiple classifiers with ICA processing to improve upon existing algorithms. The mechanisms that are used in this ensemble system include: 1) automatic ICi selection; 2) extraction of features of the resultant ICi; 3) the construction of parallel pipelines for effectively training multiple classifiers; and a 4) simple process that combines the multiple decisions. The proposed ICi-ensemble is demonstrated in a typical BCI application, which is the monitoring of participants' cognitive states in a realistic sustained-attention driving task. The results reveal that the proposed ICi-ensemble outperformed the previous method using a single ICi with 7% (91.6% versus 84.3%) in the cognitive state classification. Additionally, the proposed ICi-ensemble method that characterizes the EEG dynamics of multiple brain areas favors the application of BCI in natural environments.
Lin, C.-.T., Chuang, C.-.H., Huang, C.-.S., Tsai, S.-.F., Lu, S.-.W., Chen, Y.-.H. & Ko, L.-.W. 2014, 'Wireless and wearable EEG system for evaluating driver vigilance.', IEEE Transactions on Biomedical Circuits and Systems, vol. 8, no. 2, pp. 165-176.View/Download from: UTS OPUS or Publisher's site
Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time.
Lin, C.-.T., Huang, K.-.C., Chuang, C.-.H., Ko, L.-.W. & Jung, T.-.P. 2013, 'Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra.', Journal of neural engineering, vol. 10, no. 5, p. 056024.View/Download from: UTS OPUS or Publisher's site
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.
Lin, F.C., Ko, L.W., Chuang, C.H., Su, T.P. & Lin, C.T. 2012, 'Generalized EEG-based drowsiness prediction system by using a self-organizing neural fuzzy system', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 59, no. 9, pp. 2044-2055.View/Download from: UTS OPUS or Publisher's site
A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks (p-value < 0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications. © 2012 IEEE.
Yang, J.M., Kuo, B.C., Yu, P.T. & Chuang, C.H. 2010, 'A dynamic subspace method for hyperspectral image classification', IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 7, pp. 2840-2853.View/Download from: UTS OPUS or Publisher's site
Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. In this paper, we propose a novel subspace selection mechanism, named the dynamic subspace method (DSM), to improve RSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Two importance distributions are proposed to impose on the process of constructing ensemble classifiers. One is the distribution of subspace dimensionality, and the other is the distribution of band weights. Based on the two distributions, DSM becomes an automatic, dynamic, and adaptive ensemble. The real data experimental results show that the proposed DSM obtains sound performances than RSM, and that the classification maps remarkably produce fewer speckles. © 2006 IEEE.
Lance, B., Touryan, J., Wang, Y.K., Lu, S.W., Chuang, C.H., Khooshabeh, P., Sajda, P., Marathe, A., Jung, T.P., Lin, C.T. & McDowell, K. 2015, 'Towards Serious Games for Improved BCI' in Nakatsu, R., Rauterberg, M. & Ciancarini, P. (eds), Handbook of Digital Games and Entertainment Technologies, Springer, Germany, pp. 1-28.View/Download from: UTS OPUS or Publisher's site
Brain-computer interface (BCI) technologies, or technologies that use online brain signal processing, have a great promise to improve human interactions with computers, their environment, and even other humans. Despite this promise, there are no current serious BCI technologies in widespread use, due to the lack of robustness in BCI technologies. The key neural aspect of this lack of robustness is human variability, which has two main components: (1) individual differences in neural signals and (2) intraindividual variability over time. In order to develop widespread BCI technologies, it will be necessary to address this lack of robustness. However, it is currently unknown how neural variability affects BCI performance. To accomplish these goals, it is essential to obtain data from large numbers of individuals using BCI technologies over considerable lengths of time. One promising method for this is through the use of BCI technologies embedded into games with a purpose (GWAP). GWAP are a game-based form of crowdsourcing which players choose to play for enjoyment and during which the player performs key tasks which cannot be automated but that are required to solve research questions. By embedding BCI paradigms in GWAP and recording neural and behavioral data, it should be possible to much more clearly understand the differences in neural signals between individuals and across different time scales, enabling the development of novel and increasingly robust adaptive BCI algorithms.
Liu, Y.T., Wu, S.L., Chou, K.P., Lin, Y.Y., Lu, J., Zhang, G., Chuang, C.H., Lin, W.C. & Lin, C.T. 2016, 'A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application', 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, IEEE International Conference on Fuzzy Systems, IEEE, Vancouver, BC, Canada, pp. 2495-2500.View/Download from: UTS OPUS or Publisher's site
© 2016 IEEE.A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.
Ko, L.W., Lai, W.K., Liang, W.G., Chuang, C.H., Lu, S.W., Lu, Y.C., Hsiung, T.Y., Wu, H.H. & Lin, C.T. 2015, 'Single channel wireless EEG device for real-time fatigue level detection', Proceedings of the International Joint Conference on Neural Networks, IEEE International Joint Conference on Neural Networks, IEEE, Killarney, Ireland.View/Download from: UTS OPUS or Publisher's site
© 2015 IEEE. Driver fatigue problem is one of the important factors of traffic accidents. Recent years, many research had investigated that using EEG signals can effectively detect driver's drowsiness level. However, real-time monitoring system is required to apply these fatigue level detection techniques in the practical application, especially in the real-road driving. Therefore, it required less channels, portable and wireless, real-time monitoring and processing techniques for developing the real-time monitoring system. In this study, we develop a single channel wireless EEG device which can real-time detect driver's fatigue level on the mobile device such as smart phone or tablet. The developed device is investigated to obtain a better and precise understanding of brain activities of mental fatigue under driving, which is of great benefit for devolvement of detection of driving fatigue system. This system consists of a Bluetooth-enabled one channel EEG, a regression model, and smartphone, which was a platform recording and transforming the raw EEG data to useful driving status. In the experiment, this was a sustained-attention driving task to implement in a virtual-reality (VR) driving simulator. To training model and develop the system, we were performed for 15 subjects to study Electroencephalography (EEG) brain dynamics by using a mobile and wireless EEG device. Based on the outstanding training results, the leave-one-subject-out cross validation test obtained 90% fatigue detection accuracy. These results indicate that the combination of a smartphone and wireless EEG device constitutes an effective and easy wearable solution for detecting and preventing driver fatigue in real driving environments.
Wu, D., Chuang, C.H. & Lin, C.T. 2015, 'Online driver's drowsiness estimation using domain adaptation with model fusion', 2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015, International Conference on Affective Computing and Intelligent Interaction, IEEE, Xian, PEOPLES R CHINA, pp. 904-910.View/Download from: Publisher's site
© 2015 IEEE. Drowsy driving is a pervasive problem among drivers, and is also an important contributor to motor vehicle accidents. It is very important to be able to estimate a driver's drowsiness level online so that preventative actions could be taken to avoid accidents. However, because of large individual differences, it is very challenging to design an estimation algorithm whose parameters fit all subjects. Some subject-specific calibration data must be used to tailor the algorithm for each new subject. This paper proposes a domain adaptation with model fusion (DAMF) online drowsiness estimation approach using EEG signals. By making use of EEG data from other subjects in a transfer learning framework, DAMF requires very little subject-specific calibration data, which significantly increases its utility in practice. We demonstrate using a simulated driving experiment and 15 subjects that DAMF can achieve much better performance than several other approaches.
Liu, Y.T., Lin, Y.Y., Wu, S.L., Chuang, C.H., Prasad, M. & Lin, C.T. 2014, 'EEG-based driving fatigue prediction system using functional-link-based fuzzy neural network', Proceedings of the International Joint Conference on Neural Networks, pp. 4109-4113.View/Download from: Publisher's site
© 2014 IEEE. This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)-based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a nonlinear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.
Chuang, C.-.H., Lin, Y.-.P., Ko, L.-.W., Jung, T.-.P. & Lin, C.-.T. 2013, 'Automatic design for independent component analysis based brain-computer interfacing.', Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Osaka, Japan, pp. 2180-2183.View/Download from: UTS OPUS or Publisher's site
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% ~ 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.
Ko, L.W., Chuang, C.H., Huang, C.S., Chen, Y.H., Lu, S.W., Liao, L.D., Chang, W.T. & Lin, C.T. 2013, 'Real-time vigilance estimation using mobile wireless mindo EEG device with spring-loaded sensors', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Augmented Cognition, Springer, Las Vegas, NV, USA, pp. 450-458.View/Download from: UTS OPUS or Publisher's site
Monitoring the neurophysiological activities of human brain dynamics in an operational environment poses a severe measurement challenge using current laboratory-oriented biosensor technology. The goal of this research is to design, develop and test the wearable and wireless dry-electrode EEG human-computer interface (HCI) that can allow assessment of brain activities of participants actively performing ordinary tasks in natural body positions and situations within a real operational environment. Its implications in HCI were demonstrated through a sample application: vigilance-state prediction of participants performing a realistic sustained-attention driving task. Besides, this study further developed an online signal processing for extracting EEG features and assessing cognitive performance. We demonstrated the feasibility of using dry EEG sensors and miniaturized supporting hardware/software to continuously collect EEG data recorded from hairy sites (i.e., occipital region) in a realistic VR-based dynamic driving simulator. © 2013 Springer-Verlag Berlin Heidelberg.
Lin, C.T., Chuang, C.H., Huang, C.S., Chen, Y.H. & Ko, L.W. 2013, 'Real-time assessment of vigilance level using an innovative Mindo4 wireless EEG system', Proceedings - IEEE International Symposium on Circuits and Systems, International Symposium on Circuits and Systems Nano-Bio Circuit Fabrics and Systems (ISCAS), IEEE, Beijing, China, pp. 1528-1531.View/Download from: UTS OPUS or Publisher's site
Monitoring the neurophysiological activities of driver in an operational environment poses a severe measurement challenge using a current laboratory-oriented biosensor technology. The aims of this research are to 1) introduce a dry and wireless EEG system used for conveniently recording EEG signals from forehead regions, 2) propose an effective system for processing EEG recordings and translating them into the vigilance level, and 3) implement the proposed system with a JAVA-based graphical user interface (GUI) for online analysis. To validate the performance of the proposed system, this study recruited eight voluntary subjects to participate a 90-min sustained-attention driving task in a virtual-realistic driving environment. Physiological evidence obtained from the power spectral analysis showed that the dry EEG system could distinguish an alert EEG from a drowsy EEG by evaluating the spectral dynamics of delta and alpha activities. Furthermore, the experimental result of the comparison of the prediction performance using four forehead electrode sites (AF8, FP2, FP1, and AF7) implied that a single-electrode EEG signal used in the mobile and wireless EEG system is able to obtain a high prediction accuracy (93%). Taken together, the proposed system applied a dry-EEG device combined with an effective algorithm can be a promising technology for real driving applications. © 2013 IEEE.
Chuang, C.H., Huang, C.S., Lin, C.T., Ko, L.W., Chang, J.Y. & Yang, J.M. 2012, 'Mapping information flow of independent source to predict conscious level: A granger causality based brain-computer interface', Proceedings - 2012 International Symposium on Computer, Consumer and Control, IS3C 2012, pp. 813-816.View/Download from: UTS OPUS or Publisher's site
Recent studies have shown that the various brain networks over different cognitive states. In contrast to measure a physiological change over a single region, the information flows between brain regions described by effective connectivity provides an informative dynamic over the whole brain. In this study, we proposed a source information flow network based on the combination of Granger causality and support vector regression to predict driver's conscious level. This work provides the first application of using brain network to develop a brain-computer interface and obtain a sound result of performance. © 2012 IEEE.
Huang, K.-.C., Jung, T.-.P., Chuang, C.-.H., Ko, L.-.W. & Lin, C.-.T. 2012, 'Preventing lapse in performance using a drowsiness monitoring and management system.', 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, San Diego, CA, USA, pp. 3336-3339.View/Download from: UTS OPUS or Publisher's site
Research on public security, especially the safe manipulation and control of vehicles, has gained increasing attention in recent years. This study proposes a closed-loop drowsiness monitoring and management system that can estimate subjects' driving performance. The system observes electroencephalographic (EEG) dynamics and behavioral changes, delivers arousing feedback to individuals experiencing momentary cognitive lapses, and assesses the efficacy of the feedback. Results of this study showed that the arousing feedback immediately improved subject performance, which was accompanied by concurrent theta- and alpha-power suppression in the bilateral occipital areas. This study further demonstrated the feasibility of accurately assessing the efficacy of arousing feedback presented to drowsy participants by monitoring the changes in their EEG power spectra.
Kuo, B.C., Lin, S.S., Wu, H.M. & Chuang, C.H. 2010, 'A novel classification processing based on the spatial information and the concept of Adaboost for hyperspectral image classification', International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2816-2819.View/Download from: Publisher's site
In this paper, a novel classification processing based on the spatial information and the concept of Adaboost for hyperspectral image classification is proposed. This classification process is named as adaptive feature extraction with spatial information (AdaFESI). The main idea is adaptive in the sense that subsequent feature spaces are tweaked in favor of those instances misclassified by spectral or spatial classifiers in the previous feature space. All training samples are projected into these feature spaces to train various classifiers and then constitute a multiple classifier system. The experimental results based on two hyperspectral data sets show that the proposed algorithm can generate better classification results. © 2010 IEEE.
Chuang, C.H., Lai, P.C., Ko, L.W., Kuo, B.C. & Lin, C.T. 2010, 'Driver's cognitive state classification toward brain computer interface via using a generalized and supervised technology', Proceedings of the International Joint Conference on Neural Networks.View/Download from: Publisher's site
Growing numbers of traffic accidents had become a serious social safety problem in recent years. The main factor of the high fatalities was the obvious decline of the driver's cognitive state in their perception, recognition and vehicle control abilities while being sleepy. The key to avoid the terrible consequents is to build a detecting system for ongoing assessment of driver's cognitive state. A quickly growing research, brain-computer interface (BCI), offers a solution offering great assistance to those who require alternative communicatory and control mechanisms. In this study, we propose an alertness/drowsiness classification system based on investigating electroencephalographic (EEG) brain dynamics in lane-keeping driving experiments in a virtual reality (VR) driving environment with a motion platform. The core of the classification system is composed of dimension reduction technique and classifier learning algorithm. In order to find the suitable method for better describing the data structure, we explore the performances using different feature extraction and feature selection methods with different classifiers. Experiment results show that the accuracy is over 80% in most combinations and even near 90% under Principal Component Analysis (PCA) and Nonparametric Weighted Feature Extraction (NWFE) going with Gaussian Maximum Likelihood classifier (ML) and k-Nearest-Neighbor classifier (kNN), respectively. In addition, this developed classification system can also solve the individual brain dynamic differences caused from different subjects and overcome the subject dependent limitation. The optimized solution with better accuracy performance out of all combinations can be considered to implement in the kernel brain-computer interface. © 2010 IEEE.
Jung, T.-.P., Huang, K.-.C., Chuang, C.-.H., Chen, J.-.A., Ko, L.-.W., Chiu, T.-.W. & Lin, C.-.T. 2010, 'Arousing feedback rectifies lapse in performance and corresponding EEG power spectrum.', Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, pp. 1792-1795.View/Download from: Publisher's site
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.
Jung, T.P., Huang, K.C., Chuang, C.H., Chen, J.A., Ko, L.W., Chiu, T.W. & Lin, C.T. 2010, 'Arousing feedback rectifies lapse in performance and corresponding EEG power spectrum', 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, pp. 1792-1795.View/Download from: Publisher's site
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. © 2010 IEEE.
Yu, Y.H., Lai, P.C., Ko, L.W., Chuang, C.H., Kuo, B.C. & Lin, C.T. 2010, 'An EEG-based classification system of passenger's motion sickness level by using feature extraction/selection technologies', Proceedings of the International Joint Conference on Neural Networks.View/Download from: Publisher's site
Past studies reported that the main electrogastrography (EEG) dynamic changes related to motion sickness (MS) were occurred in occipital, parietal, and somatosensory brain area, especially in the power increasing of the alpha band (8-13 Hz) and theta band (4-7 Hz) which had positive correlation with the subjective MS level. Depend on these main findings correlated with MS, we attempt to develop an EEG based classification system to automatically classify subject's MS level and find the suitable EEG features via common feature extraction, selection and classifiers technologies in this study. If we can find the regulations and then develop an algorithm to predict MS occurring, it would be a great benefit to construct a safe and comfortable environment for all drivers and passengers when they are cruising in the car, bus, ship or airplane. EEG is one of the best methods for monitoring the brain dynamics induced by motion-sickness because of its high temporal resolution and portability. After collecting the EEG signals and subjective MS level in a realistic driving environment, we first do the data pre-processing part including ICA, component clustering analysis and time-frequency analysis. Then we adopt three common feature extractions and two feature selections (FE/FS) technologies to extract or select the correlated features such as principal component analysis (PCA), linear discriminate analysis (LDA), nonparametric weighted feature extraction (NWFE), forward feature selections (FFS) and backward feature selections (BFS) and feed the feature maps into three classifiers (Gaussian Maximum Likelihood Classifier (ML), k-Nearest-Neighbor Classifier (kNN) and Support Vector Machine (SVM)). Experimental results show that classification performance of all our proposed technologies can be reached almost over 95%. It means it is possible to apply the effective technology combination to predict the subject's MS level in the real life applications. The better combination in ...
Kuo, B.C., Chuang, C.H., Huang, C.S. & Hung, C.C. 2009, 'A nonparametric contextual classification based on Markov random fields', WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.View/Download from: Publisher's site
In this paper a nonparametric contextual classification using both spectral and spatial information will be proposed for hyperspectral image classification. Essentially, among the classification, spatial information is acquired on the basis of Markov random field (MRF) and then joined with the nonparametric density estimation. Two MRF-based nonparametric contextual classifications based on kNN and Parzen density estimation will be introduced. We expect this combination could strengthen the capability for classifying pixels of different class labels with similar spectral values and dealing with data that has no clear numerical interpretation. © 2009 IEEE.
Kuo, B.C., Chuang, C.H., Li, C.H. & Lin, C.T. 2009, 'Subspace selection based multiple classifier systems for hyperspectral image classification', WHISPERS '09 - 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.View/Download from: Publisher's site
In a typical supervised classification task, the size of training data fundamentally affects the generality of a classifier. Given a finite and fixed size of training data, the classification result may be degraded as the number of features (dimensionality) increase. Many researches have demonstrated that multiple classifier systems (MCS) or socalled ensembles can alleviate small sample size and high dimensionality concern, and obtain more outstanding and robust results than single models. One of the effective approaches for generating an ensemble of diverse base classifiers is the use of different feature subsets such as random subspace method (RSM). The objective of this research is to develop a novel ensemble technique based on cluster algorithms for strengthening RSM. The results of real data experiments show that the proposed method obtains the sound performance especially in the situation of using less number of classifiers. © 2009 IEEE.
Chuang, C.H., Kuo, B.C. & Wang, H.P. 2008, 'Fuzzy fusion method for combining small number of classifiers in hyperspectral image classification', Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008, pp. 327-332.View/Download from: Publisher's site
For hyperspectral image classification problem, the random subspace method has been shown that is a good approach to overcome the small sample problem,and the machinery of it is to randomly select a batch of subspaces to train different classifiers and then get the final decision by using the majority vote method. Theoretically, more classifiers we train, more stable and more accurate result we obtain. However, it shows the bad outcome when using small number of classifiers. In this paper, a fuzzy measure has been applied into the fusion process as a new evaluation to combine classifiers to try to improve the performance in the situation of less classifier. From the experiment results, it displays that this fuzzy measure has effectively progressed in the classification accuracy. © 2008 IEEE.
Kuo, B.C., Chuang, C.H., Hung, C.C. & Yang, S.W. 2008, 'A novel random subspace method using spectral and spatial information for hyperspectral image classification', International Geoscience and Remote Sensing Symposium (IGARSS).View/Download from: Publisher's site
Many studies have demonstrated that multiple classifier systems, such as random subspace method, obtain more outstanding and robust results than a single classifier. In this study, we propose a novel RSM framework which is composed of two parts. The first part is the construction of a weighted RSM, where weights are given by two classifier-based distributions. One is the feature weighting distribution, and the other is the subspace dimensionality distribution that helps for dynamically selecting the size of subspace with respect to the employed classifiers. The second part is to introduce the spatial information estimated by the Markov random filed theory into the Bayesian classifiers used in the framework. The real data experimental results show that the proposed framework obtains satisfactory performances, and the classification maps remarkably produce fewer speckles. © 2008 IEEE.