This page has only limited features, please log in for full access.

Unclaimed
Yi-Hung Liu
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 04 July 2021 in Actuators
Reads 0
Downloads 0

Classification between individuals with mild cognitive impairment (MCI) and healthy controls (HC) based on electroencephalography (EEG) has been considered a challenging task to be addressed for the purpose of its early detection. In this study, we proposed a novel EEG feature, the kernel eigen-relative-power (KERP) feature, for achieving high classification accuracy of MCI versus HC. First, we introduced the relative powers (RPs) between pairs of electrodes across 21 different subbands of 2-Hz width as the features, which have not yet been used in previous MCI-HC classification studies. Next, the Fisher’s class separability criterion was applied to determine the best electrode pairs (five electrodes) as well as the frequency subbands for extracting the most sensitive RP features. The kernel principal component analysis (kernel PCA) algorithm was further performed to extract a few more discriminating nonlinear principal components from the optimal RPs, and these components form a KERP feature vector. Results carried out on 51 participants (24 MCI and 27 HC) show that the newly introduced subband RP feature showed superior classification performance to commonly used spectral power features, including the band power, single-electrode relative power, and also the RP based on the conventional frequency bands. A high leave-one-participant-out cross-validation (LOPO-CV) classification accuracy 86.27% was achieved by the RP feature, using a simple linear discriminant analysis (LDA) classifier. Moreover, with the same classifier, the proposed KERP further improved the accuracy to 88.24%. Finally, cascading the KERP feature to a nonlinear classifier, the support vector machine (SVM), yields a high MCI-HC classification accuracy of 90.20% (sensitivity = 87.50% and specificity = 92.59%). The proposed method demonstrated a high accuracy and a high usability (only five electrodes are required), and therefore, has great potential to further develop an EEG-based computer-aided diagnosis system that can be applied for the early detection of MCI.

ACS Style

Yu-Tsung Hsiao; Chia-Fen Tsai; Chien-Te Wu; Thanh-Tung Trinh; Chun-Ying Lee; Yi-Hung Liu. MCI Detection Using Kernel Eigen-Relative-Power Features of EEG Signals. Actuators 2021, 10, 152 .

AMA Style

Yu-Tsung Hsiao, Chia-Fen Tsai, Chien-Te Wu, Thanh-Tung Trinh, Chun-Ying Lee, Yi-Hung Liu. MCI Detection Using Kernel Eigen-Relative-Power Features of EEG Signals. Actuators. 2021; 10 (7):152.

Chicago/Turabian Style

Yu-Tsung Hsiao; Chia-Fen Tsai; Chien-Te Wu; Thanh-Tung Trinh; Chun-Ying Lee; Yi-Hung Liu. 2021. "MCI Detection Using Kernel Eigen-Relative-Power Features of EEG Signals." Actuators 10, no. 7: 152.

Journal article
Published: 22 August 2019 in Journal of Geophysical Research: Solid Earth
Reads 0
Downloads 0
ACS Style

Yi‐Hung Liu; Ting‐Chen Yeh; Kate Huihsuan Chen; Yaochieh Chen; Yuan‐Yi Yen; Horng‐Yuan Yen. Investigation of Single‐Station Classification for Short Tectonic Tremor in Taiwan. Journal of Geophysical Research: Solid Earth 2019, 124, 8803 -8822.

AMA Style

Yi‐Hung Liu, Ting‐Chen Yeh, Kate Huihsuan Chen, Yaochieh Chen, Yuan‐Yi Yen, Horng‐Yuan Yen. Investigation of Single‐Station Classification for Short Tectonic Tremor in Taiwan. Journal of Geophysical Research: Solid Earth. 2019; 124 (8):8803-8822.

Chicago/Turabian Style

Yi‐Hung Liu; Ting‐Chen Yeh; Kate Huihsuan Chen; Yaochieh Chen; Yuan‐Yi Yen; Horng‐Yuan Yen. 2019. "Investigation of Single‐Station Classification for Short Tectonic Tremor in Taiwan." Journal of Geophysical Research: Solid Earth 124, no. 8: 8803-8822.

Journal article
Published: 27 July 2018 in Applied Sciences
Reads 0
Downloads 0

Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with positive pictures from the International Affective Picture System. Because MDD is associated with blunted positive emotions, we reasoned that this approach would yield highly dissimilar EEG features in healthy versus depressed adults. We extracted three types of relative EEG power features from different frequency bands (delta, theta, alpha, beta, and gamma) during the emotion task and resting state. We also applied a novel classifier, called a conformal kernel support vector machine (CK-SVM), to try to improve the generalization performance of conventional SVMs. We then compared CK-SVM performance with three machine learning classifiers: linear discriminant analysis (LDA), conventional SVM, and quadratic discriminant analysis. The results from the initial analyses using the LDA classifier on 55 participants (24 MDD, 31 healthy controls) showed that the participant-independent classification accuracy obtained by leave-one-participant-out cross-validation (LOPO-CV) was higher for the EEG recorded during the positive emotion induction versus the resting state for all types of relative EEG power. Furthermore, the CK-SVM classifier achieved higher LOPO-CV accuracy than the other classifiers. The best accuracy (83.64%; sensitivity = 87.50%, specificity = 80.65%) was achieved by the CK-SVM, using seven relative power features extracted from seven electrodes. Overall, combining positive emotion induction with the CK-SVM classifier proved useful for detecting MDD on the basis of EEG signals. In the future, this approach might be used to develop a brain–computer interface system to assist with the detection of MDD in the clinic. Importantly, such a system could be implemented with a low-density electrode montage (seven electrodes), highlighting its practical utility.

ACS Style

Chien-Te Wu; Daniel G. Dillon; Hao-Chun Hsu; Shiuan Huang; Elyssa Barrick; Yi-Hung Liu. Depression Detection Using Relative EEG Power Induced by Emotionally Positive Images and a Conformal Kernel Support Vector Machine. Applied Sciences 2018, 8, 1244 .

AMA Style

Chien-Te Wu, Daniel G. Dillon, Hao-Chun Hsu, Shiuan Huang, Elyssa Barrick, Yi-Hung Liu. Depression Detection Using Relative EEG Power Induced by Emotionally Positive Images and a Conformal Kernel Support Vector Machine. Applied Sciences. 2018; 8 (8):1244.

Chicago/Turabian Style

Chien-Te Wu; Daniel G. Dillon; Hao-Chun Hsu; Shiuan Huang; Elyssa Barrick; Yi-Hung Liu. 2018. "Depression Detection Using Relative EEG Power Induced by Emotionally Positive Images and a Conformal Kernel Support Vector Machine." Applied Sciences 8, no. 8: 1244.

Journal article
Published: 03 July 2018 in Journal of Systems Architecture
Reads 0
Downloads 0

Development of brain-computer interface (BCI)-controlled virtual reality (VR) games has received increasing attention. Yet, the up-to-date BCI-VR systems were still based on one single BCI and a virtual environment (VE). In this paper, we propose and implement a novel BCI-controlled VR (BCI-VR) game based on a structure of internet of brains (IoB) allowing multiple players from different sites to play a car racing game online. Electroencephalographic (EEG) and electromyographic (EMG) signals from different sites’ BCIs are uploaded to a high-performance cloud server where the car-controlled algorithms are performed. During the online car racing period, the players mentally control the speeds of their chosen cars by means of concentration, and the concentration level can be adjusted by performing a mental arithmetic (MA) task with different levels of difficulty. Two linear and two nonlinear EEG features, including theta band power (BP), beta BP, Higuchi's fractal dimension (HFD), and Katz's FD (KFD), are used to transform the concentration level to speeds of four different cars. The players can also sensitively trigger the car in the VE to jump by performing a slight teeth-gritting task to generate easy-to-detect EMG signals. Six subjects participated in this study to test the performance of the proposed hybrid (EEG plus EMG) BCI-VR car racing game. The results indicate that theta BP and HFD are more sensitive to the MA-induced concentration in comparison with beta BP and KFD. Through the test of online car racing game, the results also demonstrated the feasibility that different players play the game in the same VE through multiple BCI control at different sites. More importantly, our BCI-VR implementation has a high usability (only two electrodes are required; calibration needs only 64 seconds) and high feasibility (high average scores of the control, sensory, and distraction factors in a 30-item post-experimental presence questionnaire).

ACS Style

Shih-Ching Yeh; Chung-Lin Hou; Wei-Hao Peng; Zhen-Zhan Wei; Shiuan Huang; Edward Yu-Chen Kung; Longsong Lin; Yi-Hung Liu. A multiplayer online car racing virtual-reality game based on internet of brains. Journal of Systems Architecture 2018, 89, 30 -40.

AMA Style

Shih-Ching Yeh, Chung-Lin Hou, Wei-Hao Peng, Zhen-Zhan Wei, Shiuan Huang, Edward Yu-Chen Kung, Longsong Lin, Yi-Hung Liu. A multiplayer online car racing virtual-reality game based on internet of brains. Journal of Systems Architecture. 2018; 89 ():30-40.

Chicago/Turabian Style

Shih-Ching Yeh; Chung-Lin Hou; Wei-Hao Peng; Zhen-Zhan Wei; Shiuan Huang; Edward Yu-Chen Kung; Longsong Lin; Yi-Hung Liu. 2018. "A multiplayer online car racing virtual-reality game based on internet of brains." Journal of Systems Architecture 89, no. : 30-40.

Editorial
Published: 18 April 2018 in Journal of Healthcare Engineering
Reads 0
Downloads 0
ACS Style

Yi-Hung Liu; David Moratal; Javier Escudero; Han-Pang Huang. Medical Mechatronics for Healthcare. Journal of Healthcare Engineering 2018, 2018, 1 -3.

AMA Style

Yi-Hung Liu, David Moratal, Javier Escudero, Han-Pang Huang. Medical Mechatronics for Healthcare. Journal of Healthcare Engineering. 2018; 2018 ():1-3.

Chicago/Turabian Style

Yi-Hung Liu; David Moratal; Javier Escudero; Han-Pang Huang. 2018. "Medical Mechatronics for Healthcare." Journal of Healthcare Engineering 2018, no. : 1-3.

Original article
Published: 30 March 2018 in Journal of Medical and Biological Engineering
Reads 0
Downloads 0

Bilateral upper-limb motor imagery has been demonstrated to be a useful mental task in electroencephalography (EEG)-based brain–computer interfaces (BCIs). By contrast, few studies have examined bilateral lower-limb motor imagery, and all of them have focused on imaginary foot movements. The left–right classification accuracy reported in these studies based on the EEG mu rhythm (8–13 Hz) and beta band (13–30 Hz) remains unsatisfactory. The present study investigated the possibility of using lower-limb stepping motor imagery as the mental task and analysed the EEG difference between imaginary left-leg stepping (L-stepping) and right-leg stepping (R-stepping) movements. An experimental paradigm was designed to collect 5-s motor imagery EEG signals at nine recording sites around the vertex of the brain. Results from eight able-bodied participants indicated that the commonly used mu event-related desynchronisation (ERD) feature exhibited no significant difference between the two imaginary movements for all recording sites and all time intervals within the 5-s motor imagery period. Regarding the other commonly used feature, beta event-related synchronisation, no significant difference between the two imagery tasks was observed for most of the recording sites and time intervals. Instead, theta band (4–8 Hz) ERD significantly differed between the L- and R-stepping imagery tasks at five sites (FC4, C3, CP3, Cz, CPz) within the first 2 s after motor imagery cue onset. The findings from the present study may be a basis for further development of BCI systems for decoding left and right stepping during mental exercise where the two motions are alternately imagined.

ACS Style

Yi-Hung Liu; Li-Fong Lin; Chun-Wei Chou; Yun Chang; Yu-Tsung Hsiao; Wei-Chun Hsu. Analysis of Electroencephalography Event-Related Desynchronisation and Synchronisation Induced by Lower-Limb Stepping Motor Imagery. Journal of Medical and Biological Engineering 2018, 39, 54 -69.

AMA Style

Yi-Hung Liu, Li-Fong Lin, Chun-Wei Chou, Yun Chang, Yu-Tsung Hsiao, Wei-Chun Hsu. Analysis of Electroencephalography Event-Related Desynchronisation and Synchronisation Induced by Lower-Limb Stepping Motor Imagery. Journal of Medical and Biological Engineering. 2018; 39 (1):54-69.

Chicago/Turabian Style

Yi-Hung Liu; Li-Fong Lin; Chun-Wei Chou; Yun Chang; Yu-Tsung Hsiao; Wei-Chun Hsu. 2018. "Analysis of Electroencephalography Event-Related Desynchronisation and Synchronisation Induced by Lower-Limb Stepping Motor Imagery." Journal of Medical and Biological Engineering 39, no. 1: 54-69.

Journal article
Published: 03 July 2017 in Sensors
Reads 0
Downloads 0

Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in the brain–computer interface (BCI) community. In this study, we address this critical issue by introducing the Grassberger–Procaccia and Higuchi’s methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients. Moreover, a Fisher’s criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites. An EEG data collection paradigm is designed to collect the EEG signal of resting state and the imagination of three movements, including right hand grasping (RH), left hand grasping (LH), and left foot stepping (LF). Five late-stage ALS patients without receiving any SMR training participated in this study. Experimental results show that the proposed GPFD feature is not only superior to the previously-used SMR features (mu and beta band powers of EEG from sensorimotor cortex) but also better than HFD. The accuracies achieved by the SMR features are not satisfactory (all lower than 80%) in all binary classification tasks, including RH imagery vs. resting, LH imagery vs. resting, and LF imagery vs. resting. For the discrimination between RH imagery and resting, the average accuracies of GPFD in 30-channel (without channel selection) and top-five-channel configurations are 95.25% and 93.50%, respectively. When using only one channel (the best channel among the 30), a high accuracy of 91.00% can still be achieved by the GPFD feature and a linear discriminant analysis (LDA) classifier. The results also demonstrate that the proposed Fisher’s criterion-based channel selection is capable of removing a large amount of redundant and noisy EEG channels. The proposed GPFD feature extraction combined with the channel selection strategy can be used as the basis for further developing high-accuracy and high-usability motor imagery BCI systems from which the patients with ALS can really benefit.

ACS Style

Yi-Hung Liu; Shiuan Huang; Yi-De Huang. Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-Based Channel Selection. Sensors 2017, 17, 1557 .

AMA Style

Yi-Hung Liu, Shiuan Huang, Yi-De Huang. Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-Based Channel Selection. Sensors. 2017; 17 (7):1557.

Chicago/Turabian Style

Yi-Hung Liu; Shiuan Huang; Yi-De Huang. 2017. "Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-Based Channel Selection." Sensors 17, no. 7: 1557.

Journal article
Published: 14 June 2017 in Sensors
Reads 0
Downloads 0

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.

ACS Style

Shih-Cheng Liao; Chien-Te Wu; Hao-Chuan Huang; Wei-Teng Cheng; Yi-Hung Liu. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns. Sensors 2017, 17, 1385 .

AMA Style

Shih-Cheng Liao, Chien-Te Wu, Hao-Chuan Huang, Wei-Teng Cheng, Yi-Hung Liu. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns. Sensors. 2017; 17 (6):1385.

Chicago/Turabian Style

Shih-Cheng Liao; Chien-Te Wu; Hao-Chuan Huang; Wei-Teng Cheng; Yi-Hung Liu. 2017. "Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns." Sensors 17, no. 6: 1385.

Article
Published: 22 March 2017 in International Journal of Fuzzy Systems
Reads 0
Downloads 0
ACS Style

Yi-Hung Liu; Tzyy-Ping Jung; Chin-Teng (Ct) Lin; Lun-De Liao. Editorial Message: Special Issue on Fuzzy Brain–Computer Interface Systems. International Journal of Fuzzy Systems 2017, 19, 528 -528.

AMA Style

Yi-Hung Liu, Tzyy-Ping Jung, Chin-Teng (Ct) Lin, Lun-De Liao. Editorial Message: Special Issue on Fuzzy Brain–Computer Interface Systems. International Journal of Fuzzy Systems. 2017; 19 (2):528-528.

Chicago/Turabian Style

Yi-Hung Liu; Tzyy-Ping Jung; Chin-Teng (Ct) Lin; Lun-De Liao. 2017. "Editorial Message: Special Issue on Fuzzy Brain–Computer Interface Systems." International Journal of Fuzzy Systems 19, no. 2: 528-528.

Article
Published: 03 October 2016 in International Journal of Fuzzy Systems
Reads 0
Downloads 0

Although various kinds of motor imageries have been used for BCI applications, imaginary lower limb stepping movement has not been studied yet. The purpose of this study is to investigate the possibilities of using electroencephalography (EEG) signal to classify imaginary lower limb stepping movements and to design a robust motor imagery classifier based on support vector machine (SVM). A cue-based experimental paradigm is designed to record nine-channel EEG associated with imaginary left leg stepping (L-stepping) and right leg stepping (R-stepping) movements from eight healthy subjects. Features including band powers (BPs), common spatial pattern (CSP), and a filter-bank CSP (FB-CSP) were extracted from the recorded EEG. Fuzzy SVM (FSVM) is introduced to this study to classify L-stepping and R-stepping imageries. We propose a novel kernel-induced membership function to address the issue of data relative importance assignment. The FSVM with the membership function suggested in the original work of FSVM (Type-I FSVM) and the FSVM with the one we proposed (Type-II FSVM) is compared. Results indicated that the classification accuracies based on BP features are near the chance level (~50 %). Both alpha-band CSP (71.25 %) and FB-CSP (75.63 %) gave acceptable results as a simple k-NN classifier is performed. Results show that both types of FSVM performed better than the conventional SVM. Also, Type-II FSVM outperforms Type-I FSVM, especially when the alpha-CSP feature is employed, where the improvement in error reduction rate is over 15 %. The highest average L-stepping versus R-stepping classification accuracy over the eight subjects is achieved (86.25 % in single-trial analysis) by FB-CSP and FSVM-II. The high classification result suggests the feasibility of using lower limb stepping imagery to develop a BCI that can control devices or might be able to serve as a neurofeedback tool for users who need lower limb stepping imagery training for gait function improvement.

ACS Style

Wei-Chun Hsu; Li-Fong Lin; Chun-Wei Chou; Yu-Tsung Hsiao; Yi-Hung Liu. EEG Classification of Imaginary Lower Limb Stepping Movements Based on Fuzzy Support Vector Machine with Kernel-Induced Membership Function. International Journal of Fuzzy Systems 2016, 19, 566 -579.

AMA Style

Wei-Chun Hsu, Li-Fong Lin, Chun-Wei Chou, Yu-Tsung Hsiao, Yi-Hung Liu. EEG Classification of Imaginary Lower Limb Stepping Movements Based on Fuzzy Support Vector Machine with Kernel-Induced Membership Function. International Journal of Fuzzy Systems. 2016; 19 (2):566-579.

Chicago/Turabian Style

Wei-Chun Hsu; Li-Fong Lin; Chun-Wei Chou; Yu-Tsung Hsiao; Yi-Hung Liu. 2016. "EEG Classification of Imaginary Lower Limb Stepping Movements Based on Fuzzy Support Vector Machine with Kernel-Induced Membership Function." International Journal of Fuzzy Systems 19, no. 2: 566-579.

Conference paper
Published: 01 October 2016 in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Reads 0
Downloads 0

This paper presents a visual feedback available robotic gait training system for motor recovery of hemiplegic stroke survivors. The system is composed of a treadmill consisting of two split belts, a pelvic support manipulator assisting patient's pelvic movement, and a visual interface feedbacking patient's gait phase. The split-belt treadmill allow patient to walk in different velocities between sound side and affected side legs, and detect patient's gait phase by current value of DC motor. The pelvic support manipulator provides three active actuations to assist patient's leg swinging during walk training. The virtual walking scenario gives visual feedback for patient while providing patient's gait phase calculated from motor current of the treadmill. One subject with simulated stroke participated in this study. Experiment results indicate gait phase of the virtual model can well track that of patient's walking, verify the feasibility of the proposed system to improve gait recovery during rehabilitation.

ACS Style

Quanquan Liu; Bo Zhang; Yi-Hung Liu; Yu-Tsung Hsiao; Mu-Der Jeng; Masakatsu G. Fujie. Integration of visual feedback system and motor current based gait rehabilitation robot for motor recovery. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2016, 001856 -001860.

AMA Style

Quanquan Liu, Bo Zhang, Yi-Hung Liu, Yu-Tsung Hsiao, Mu-Der Jeng, Masakatsu G. Fujie. Integration of visual feedback system and motor current based gait rehabilitation robot for motor recovery. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2016; ():001856-001860.

Chicago/Turabian Style

Quanquan Liu; Bo Zhang; Yi-Hung Liu; Yu-Tsung Hsiao; Mu-Der Jeng; Masakatsu G. Fujie. 2016. "Integration of visual feedback system and motor current based gait rehabilitation robot for motor recovery." 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , no. : 001856-001860.

Journal article
Published: 12 May 2016 in Applied Sciences
Reads 0
Downloads 0

This paper presents a novel brain-computer interface (BCI)-based healthcare control system, which is based on steady-state visually evoked potential (SSVEP) and P300 of electroencephalography (EEG) signals. The proposed system is composed of two modes, a brain switching mode and a healthcare function selection mode. The switching mode can detect whether a user has the intent to activate the function selection mode by detecting SSVEP in an ongoing EEG. During the function selection mode, the user is able to select any functions that he/she wants to activate through a healthcare control panel, and the function selection is done by detecting P300 in the user’s EEG signals. The panel provides 25 functions representing 25 frequently performed activities of daily life. Therefore, users with severe motor disabilities can activate the system and any functions in a self-paced manner, achieving the goal of autonomous healthcare. To achieve high P300 detection accuracy, a novel P300 detector based on kernel Fisher’s discriminant analysis (kernel FDA) and support vector machine (SVM) is also proposed. Experimental results, carried out on five subjects, show that the proposed BCI system achieves high SSVEP detection (93%) and high P300 detection (95.5%) accuracies, meaning that the switching mode has a high sensitivity, and the function selection mode has the ability to accurately detect the functions that the users want to trigger. More important, only three electrodes (Oz, Cz, and Pz) are required to measure EEG signals, enabling the system to have good usability in practical use.

ACS Style

Yi-Hung Liu; Shih-Hao Wang; Ming-Ren Hu. A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector. Applied Sciences 2016, 6, 142 .

AMA Style

Yi-Hung Liu, Shih-Hao Wang, Ming-Ren Hu. A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector. Applied Sciences. 2016; 6 (5):142.

Chicago/Turabian Style

Yi-Hung Liu; Shih-Hao Wang; Ming-Ren Hu. 2016. "A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector." Applied Sciences 6, no. 5: 142.

Conference paper
Published: 01 October 2015 in 2015 IEEE International Conference on Systems, Man, and Cybernetics
Reads 0
Downloads 0

Several studies on autism spectrum disorder (ASD) show that there exists significant heterogeneity in phenotype of the disorder. Additionally, many published findings also suggested that ASD is defined by atypical local/global processing. In this paper, we designed a puzzled-based intervention to examine the sensitiveness to the information of local /global processing on individuals with ASD. Additionally, we surveyed the participants and their parents regarding their perceptions and feelings while completing the puzzles. The results verify that the proposed method can evaluate the degree of perception of local/global information by those on the spectrum. As a result, the design of puzzle-based visual stimuli with local/global information involved may become one of the crucial phenotypes to define ASD and can be further applied to facilitate a sub grouping for people with ASD.

ACS Style

Wei-Wen Hsu; Min Zhang; Chung-Hao Chen; Jonna Bobzien; Chien-Te Wu; Yi-Hung Liu. A Puzzle-Based Tool to Study Individualized Perception Reactions for Children with Autism. 2015 IEEE International Conference on Systems, Man, and Cybernetics 2015, 682 -686.

AMA Style

Wei-Wen Hsu, Min Zhang, Chung-Hao Chen, Jonna Bobzien, Chien-Te Wu, Yi-Hung Liu. A Puzzle-Based Tool to Study Individualized Perception Reactions for Children with Autism. 2015 IEEE International Conference on Systems, Man, and Cybernetics. 2015; ():682-686.

Chicago/Turabian Style

Wei-Wen Hsu; Min Zhang; Chung-Hao Chen; Jonna Bobzien; Chien-Te Wu; Yi-Hung Liu. 2015. "A Puzzle-Based Tool to Study Individualized Perception Reactions for Children with Autism." 2015 IEEE International Conference on Systems, Man, and Cybernetics , no. : 682-686.

Research article
Published: 23 March 2015 in Mathematical Problems in Engineering
Reads 0
Downloads 0

Safety design and probabilistic optimization are fields that are widely subject to uncertainty, thus making traditional deterministic methods highly unreliable for these two fields. Popular design optimizations methods widely used for safety design and probabilistic optimization are the performance measure approach (PMA) and the performance measure approach (RIA). In a problem where the analysis is performed from an infeasible design space, a modified reliability index approach (MRIA) is employed to address some inefficiency of the traditional RIA to be able to find the optimal solutions. The PMA uses an inverse reliability analysis, which is more computationally efficient at finding the most probable design points but has been reported to have numerical instabilities on some cases. In this paper, three benchmark examples were thoroughly studied with various initial points to examine the stability and efficiency of MRIA and PMA. A hybrid reliability approach (HRA) was then presented after determining a selection factor from the optimum conditions. The proposed HRA aims to determine which of the two optimization methods would be more appropriate during the optimization processes.

ACS Style

Po Ting Lin; Mark Christian E. Manuel; Yi-Hung Liu; Yu-Cheng Chou; Yung Ting; Shian-Shing Shyu; Chang-Kuo Chen; Chun-Lin Lee. A Multifaceted Approach for Safety Design and Probabilistic Optimization. Mathematical Problems in Engineering 2015, 2015, 1 -14.

AMA Style

Po Ting Lin, Mark Christian E. Manuel, Yi-Hung Liu, Yu-Cheng Chou, Yung Ting, Shian-Shing Shyu, Chang-Kuo Chen, Chun-Lin Lee. A Multifaceted Approach for Safety Design and Probabilistic Optimization. Mathematical Problems in Engineering. 2015; 2015 (1):1-14.

Chicago/Turabian Style

Po Ting Lin; Mark Christian E. Manuel; Yi-Hung Liu; Yu-Cheng Chou; Yung Ting; Shian-Shing Shyu; Chang-Kuo Chen; Chun-Lin Lee. 2015. "A Multifaceted Approach for Safety Design and Probabilistic Optimization." Mathematical Problems in Engineering 2015, no. 1: 1-14.

Editorial
Published: 22 April 2014 in Mathematical Problems in Engineering
Reads 0
Downloads 0
ACS Style

Yi-Hung Liu; Chung-Hao Chen; Paul C.-P. Chao. Mathematical Methods Applied to Digital Image Processing. Mathematical Problems in Engineering 2014, 2014, 1 -4.

AMA Style

Yi-Hung Liu, Chung-Hao Chen, Paul C.-P. Chao. Mathematical Methods Applied to Digital Image Processing. Mathematical Problems in Engineering. 2014; 2014 ():1-4.

Chicago/Turabian Style

Yi-Hung Liu; Chung-Hao Chen; Paul C.-P. Chao. 2014. "Mathematical Methods Applied to Digital Image Processing." Mathematical Problems in Engineering 2014, no. : 1-4.

Research article
Published: 24 February 2014 in Mathematical Problems in Engineering
Reads 0
Downloads 0

In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA) is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL) algorithm and support vector machine (SVM). We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

ACS Style

Yi-Hung Liu; Yu-Tsung Hsiao; Wei-Teng Cheng; Yan-Chen Liu; Jui-Yiao Su. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine. Mathematical Problems in Engineering 2014, 2014, 1 -11.

AMA Style

Yi-Hung Liu, Yu-Tsung Hsiao, Wei-Teng Cheng, Yan-Chen Liu, Jui-Yiao Su. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine. Mathematical Problems in Engineering. 2014; 2014 (5):1-11.

Chicago/Turabian Style

Yi-Hung Liu; Yu-Tsung Hsiao; Wei-Teng Cheng; Yan-Chen Liu; Jui-Yiao Su. 2014. "Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine." Mathematical Problems in Engineering 2014, no. 5: 1-11.

Research article
Published: 19 February 2014 in Mathematical Problems in Engineering
Reads 0
Downloads 0

Face detection is a crucial prestage for face recognition and is often treated as a binary (face and nonface) classification problem. While this strategy is simple to implement, face detection accuracy would drop when nonface training patterns are undersampled. To avoid these problems, we propose in this paper a one-class learning-based face detector called support vector data description (SVDD) committee, which consists of several SVDD members, each of which is trained on a subset of face patterns. Nonfaces are not required in the training of the SVDD committee. Therefore, the face detection accuracy of SVDD committee is independent of the nonface training patterns. Moreover, the proposed SVDD committee is also able to improve generalization ability of the original SVDD when the face data set has a multicluster distribution. Experiments carried out on the extended MIT face data set show that the proposed SVDD committee can achieve better face detection accuracy than the widely used SVM face detector and performs better than other one-class classifiers, including the original SVDD and the kernel principal component analysis (Kernel PCA).

ACS Style

Yi-Hung Liu; Yung Ting; Shian-Shing Shyu; Chang-Kuo Chen; Chung-Lin Lee; Mu-Der Jeng. A Support Vector Data Description Committee for Face Detection. Mathematical Problems in Engineering 2014, 2014, 1 -9.

AMA Style

Yi-Hung Liu, Yung Ting, Shian-Shing Shyu, Chang-Kuo Chen, Chung-Lin Lee, Mu-Der Jeng. A Support Vector Data Description Committee for Face Detection. Mathematical Problems in Engineering. 2014; 2014 (5):1-9.

Chicago/Turabian Style

Yi-Hung Liu; Yung Ting; Shian-Shing Shyu; Chang-Kuo Chen; Chung-Lin Lee; Mu-Der Jeng. 2014. "A Support Vector Data Description Committee for Face Detection." Mathematical Problems in Engineering 2014, no. 5: 1-9.

Conference paper
Published: 01 October 2013 in 2013 IEEE International Conference on Systems, Man, and Cybernetics
Reads 0
Downloads 0

A two-state self-paced brain-computer interface (SP-BCI) divides human mental states into two parts: intentional control (IC) and no-control (NC) states. The state during which the user wants to activate the BCI by is called the IC state. False positive rate (FPR) plays a critical role in evaluating a SP-BCI's usability, where FPR refer to the rate incorrectly classifying NC states. Therefore, development of a method which can not only minimize the FPR and also control it to be smaller than any predefined threshold is of vital importance. In this paper, an imbalanced support vector machine (ISVM)-based FPR control scheme is proposed. This method is able to force the FPR of a two-state SP-BCI to achieve the desired FPR, and this effect is independent of subjects and the feature extraction methods used. In this study, the IC state refers to the state during which the subjects perform an instructed motor imagery task, while during the NC state the subjects are instructed to relax. Spectral power and common spatial pattern (CSP) were used to extract features from EEG signals. Results on the IC and NC EEG data from four participants demonstrate the validity of the proposed ISVM-based FPR control scheme in controlling the FPR to be equal to or smaller than any thresholds, including 5% and 0%.

ACS Style

Yi-Hung Liu; Chun-Wei Huang; Yu-Tsung Hsiao. Controlling the False Positive Rate of a Two-State Self-Paced Brain-Computer Interface. 2013 IEEE International Conference on Systems, Man, and Cybernetics 2013, 1476 -1481.

AMA Style

Yi-Hung Liu, Chun-Wei Huang, Yu-Tsung Hsiao. Controlling the False Positive Rate of a Two-State Self-Paced Brain-Computer Interface. 2013 IEEE International Conference on Systems, Man, and Cybernetics. 2013; ():1476-1481.

Chicago/Turabian Style

Yi-Hung Liu; Chun-Wei Huang; Yu-Tsung Hsiao. 2013. "Controlling the False Positive Rate of a Two-State Self-Paced Brain-Computer Interface." 2013 IEEE International Conference on Systems, Man, and Cybernetics , no. : 1476-1481.

Conference paper
Published: 01 May 2013 in 2013 International Conference on Advanced Robotics and Intelligent Systems
Reads 0
Downloads 0

Self-paced brain-computer interface (SP-BCI) has been considered a more practical BCI for users with motor disabilities. Previously, various methods have been proposed to improve the performance of the SP-BCI. However, no studies have been presented to compare the existing methods. In this study, we concentrate on a motor imagery-based two-state SP-BCI and compare the state-of-the-art methods, including various feature extraction and classification methods. Comparisons were carried on EEG data collected from four participants. True positive and false positive rates were used as the performance indices to evaluate the methods for the motor imagery-based SP-BCI. Comparison results indicate that common spatial pattern (CSP) is the best representation method. Also, support vector machine (SVM) achieves the highest true positive rate. However, its false positive rate is also the highest in most cases. Moreover, imbalanced SVM (ISVM) can keep the false positive rate below a desired threshold.

ACS Style

Yi-Hung Liu; Chun-Wei Huang; Yu-Tsung Hsiao. Comparsion of methods for a motor imagery-based two-state self-paced brain-computer interface. 2013 International Conference on Advanced Robotics and Intelligent Systems 2013, 174 -178.

AMA Style

Yi-Hung Liu, Chun-Wei Huang, Yu-Tsung Hsiao. Comparsion of methods for a motor imagery-based two-state self-paced brain-computer interface. 2013 International Conference on Advanced Robotics and Intelligent Systems. 2013; ():174-178.

Chicago/Turabian Style

Yi-Hung Liu; Chun-Wei Huang; Yu-Tsung Hsiao. 2013. "Comparsion of methods for a motor imagery-based two-state self-paced brain-computer interface." 2013 International Conference on Advanced Robotics and Intelligent Systems , no. : 174-178.

Journal article
Published: 01 June 2011 in Applied Mechanics and Materials
Reads 0
Downloads 0

This work adopts data related to the rotor efficiency of wind turbine to estimate the performance of wind turbine. To achieve this goal, two novel machine learning methods are adopted to build models for wind-turbine fault detection: one is the support vector data description (SVDD) and the other is the kernel principal component analysis (KPCA). The data collected from a normally-operating wind turbine are used to train models. In addition, we also build a health index using the KPCA reconstruction error, which can be used to predict the performance of a wind turbine when it operates online. The data used in our experiments were collected from a real wind turbine in Taiwan. Experiments results show that the model based on KPCA performs better than the one based on SVDD. The highest fault detection rate for KPCA model is higher than 98%. The results also indicate the validity of using rotor efficacy to predict the overall performance of a wind turbine.

ACS Style

Yi Hung Liu; Wei Zhi Lin; Jui Yiao Su; Yan Chen Liu. Automatic Fault Detection for Wind Turbines Using Single-Class Machine Learning Methods. Applied Mechanics and Materials 2011, 58-60, 2602 -2607.

AMA Style

Yi Hung Liu, Wei Zhi Lin, Jui Yiao Su, Yan Chen Liu. Automatic Fault Detection for Wind Turbines Using Single-Class Machine Learning Methods. Applied Mechanics and Materials. 2011; 58-60 ():2602-2607.

Chicago/Turabian Style

Yi Hung Liu; Wei Zhi Lin; Jui Yiao Su; Yan Chen Liu. 2011. "Automatic Fault Detection for Wind Turbines Using Single-Class Machine Learning Methods." Applied Mechanics and Materials 58-60, no. : 2602-2607.