Browsing by Author "Chen, Yong-Sheng"

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A Fast and Accurate Algorithm for Diffeomorphic and Symmetric Non-rigid Registration of Brain Magnetic Resonance Images
(2015-01-01)
Lin, Shih-Yen
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Chen, Yong-Sheng
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Chen, Li-Fen
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交大名義發表
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資訊工程學系
This paper proposes a non-rigid, symmetric and diffeomorphic algorithm for the registration of MR images. A log-Euclidean framework was used to model diffeomorphisms. Multiple features were used to enhance the speed of the proposed algorithm, including compactly supported Wendland\'s basis functions as models of velocity fields, a greedy optimization strategy, and a coarse-to-fine hierarchical framework. Symmetry was ensured through the invertibility of diffeomorphisms and the symmetry of the objective function and optimization algorithm. The proposed algorithm was evaluated using the results of 1560 pairwise registrations of 40 T1-weighted MRIs in the LPBA40 dataset. Our results demonstrated that the proposed algorithm is diffeomorphic with sub-voxel accuracy in terms of symmetry. The proposed algorithm outperforms 14 other registration algorithms in terms of speed (compared with diffeomorphic algorithms) and accuracy.
A novel point cloud registration using 2D image features
(2017-01-07)
Lin, Chien-Chou
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Tai, Yen-Chou
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Lee, Jhong-Jin
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Chen, Yong-Sheng
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資訊工程學系
Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.
Accurate and robust extraction of brain regions using a deformable model based on radial basis functions
(2009-10-15)
Liu, Jia-Xiu
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Chen, Yong-Sheng
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Chen, Li-Fen
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資訊工程學系
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Department of Computer Science
Brain extraction from head magnetic resonance (MR) images is a classification problem of segmenting image volumes into brain and non-brain regions. It is a difficult task due to the convoluted brain surface and the inapparent brain/non-brain boundaries in images. This paper presents an automated, robust, and accurate brain extraction method which utilizes a new implicit deformable model to well represent brain contours and to segment brain regions from MR images. This model is described by a set of Wendland's radial basis functions (RBFs) and has the advantages of compact support property and low computational complexity. Driven by the internal force for imposing the smoothness constraint and the external force for considering the intensity contrast across boundaries, the deformable model of a brain contour can efficiently evolve from its initial state toward its target by iteratively updating the RBF locations. In the proposed method, brain contours are separately determined on 2D coronal and sagittal slices. The results from these two views are generally complementary and are thus integrated to obtain a complete 3D brain volume. The proposed method was compared to four existing methods, Brain Surface Extractor, Brain Extraction Tool, Hybrid Watershed Algorithm, and Model-based Level Set, by using two sets of MR images as well as manual segmentation results obtained from the Internet Brain Segmentation Repository. Our experimental results demonstrated that the proposed approach outperformed these four methods when jointly considering extraction accuracy and robustness. (C) 2009 Elsevier B.V. All rights reserved.
Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis
(2017-12-01)
Low, Intan
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Kuo, Po-Chih
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Liu, Yu-Hsiang
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Tsai, Cheng-Lin
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Chao, Hsiang-Tai
How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiological mechanism of chronic pain. In this study, complexity features with multiple time scales and spectral features were extracted from resting-state magnetoencephalographic signals of 156 female participants with/without primary dysmenorrhea (PDM) during pain-free state. Revealed by multiscale sample entropy (MSE), PDM patients (PDMs) exhibited loss of brain complexity in regions associated with sensory, affective, and evaluative components of pain, including sensorimotor, limbic, and salience networks. Significant correlations between MSE values and psychological states (depression and anxiety) were found in PDMs, which may indicate specific nonlinear disturbances in limbic and default mode network circuits after long-term menstrual pain. These findings suggest that MSE is an important measure of brain complexity and is potentially applicable to future diagnosis of chronic pain.
An Image Based Overexposed Taillight Detection Method for Frontal Vehicle Detection in Night Vision
(2016)
Chien, Chun-Liang
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Hang, Hsueh-Ming
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Tseng, Din-Chang
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Chen, Yong-Sheng
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交大名義發表
To achieve the goal of frontal vehicle detection in night-driving condition, we propose an effective method to detect the red taillights of vehicles. The challenge is that the taillight images captured with automatic exposure typically are overexposed, which makes red color segmentation often erroneous. Instead of customizing the camera hardware to tackle this problem, we combine morphological and logical operations to extract the overexposed region in taillights, which leads to a much more reliable taillight detection scheme. Then, we develop a robust pairing process that clusters two taillight candidates into a pair that represents a vehicle. Several criteria are considered in the pairing process, including the similarities of area, shape, and height of a pair of lights. In addition, we include the temporal consistency criterion; that is, a pair of taillights should be continually detected for a certain duration of time. An energy function is used to aggregate these criteria together. Our experiments show that both the missing and false detection rates are lower than 1.5%.
Assessment of Quantitative Magnetoencephalography using Cross-frequency Phase-phase Coupling
(2018-01-01)
Lee, Pin-Shiuan
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Chen, Yong-Sheng
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Chan, Hui-Ling
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Chen, Li-Fen
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資訊工程學系
Cross-frequency phase-phase coupling (n:m CFC) within a cortical region has been proposed as a potential mechanism to regulate neural communication between different temporal scales. This study investigated n:m CFC values between high and low frequencies in resting-state networks using magnetoencephalographic (MEG) data. Simulation experiments with different frequency pairs were first conducted to evaluate the effect of SNR on the estimation of phase and CFC. The values of n:m CFC between each pair of delta, theta, alpha, beta, and gamma frequencies were estimated from eye-open resting-state MEG data. The topographic maps of CFC values between alpha and other frequencies were illustrated. The results of simulation data showed the increased accuracy of phase and n:m CFC estimation for balanced power of two targeted frequencies. Results of MEG data exhibited that CFC maps of low-alpha activity presented broadly distributed coupling with high frequencies (including beta and gamma) in the frontal regions of salient ventral attention and frontoparietal control networks, whereas high-alpha activity coupling with the gamma band in the posterior part of frontoparietal control network. Our study demonstrates the versatile of n:m CFC values at rest in human brain. Caution should be considered when n:m CFC values would be used as an index of dysfunction at resting state in diseased brains.
Asymmetric Engagement of Amygdala and Its Gamma Connectivity in Early Emotional Face Processing
(2015-01-28)
Liu, Tai-Ying
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Chen, Yong-Sheng
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Hsieh, Jen-Chuen
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Chen, Li-Fen
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資訊工程學系
The amygdala has been regarded as a key substrate for emotion processing. However, the engagement of the left and right amygdala during the early perceptual processing of different emotional faces remains unclear. We investigated the temporal profiles of oscillatory gamma activity in the amygdala and effective connectivity of the amygdala with the thalamus and cortical areas during implicit emotion-perceptual tasks using event-related magnetoencephalography (MEG). We found that within 100 ms after stimulus onset the right amygdala habituated to emotional faces rapidly (with duration around 20-30 ms), whereas activity in the left amygdala (with duration around 50-60 ms) sustained longer than that in the right. Our data suggest that the right amygdala could be linked to autonomic arousal generated by facial emotions and the left amygdala might be involved in decoding or evaluating expressive faces in the early perceptual emotion processing. The results of effective connectivity provide evidence that only negative emotional processing engages both cortical and subcortical pathways connected to the right amygdala, representing its evolutional significance (survival). These findings demonstrate the asymmetric engagement of bilateral amygdala in emotional face processing as well as the capability of MEG for assessing tha-lamo-cortico-limbic circuitry.
Automated Sleep Staging using Single EEG Channel for REM Sleep Deprivation
(2009)
Lee, Yu-Hsun
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Chen, Yong-Sheng
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Chen, Li-Fen
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資訊工程學系
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Department of Computer Science
In medical literatures, it has been reported that the increased REM (rapid eye movement) density is one of the characters of depressed sleep. Some experiments were conducted to confirm that REM sleep deprivation (REM-SD) for a period of time is therapeutic for endogenous depressed patients. However, because of its high complexity and intensive labor requirement, this therapy has not yet been proved validity by a sufficient amount of depressed patients. Therefore, we propose to develop an automated sleep staging system using only single EEC channel to achieve on-line detection for REM state during sleep. For classifier design, we use a dataset of 25 subjects and the staging accuracy can achieve 80%. Once the REM state is detected by the system, the system will alarm the subject to deprive the REM sleep. The effect of REM sleep deprivation can be examined by hypnogram and the proposed system will be applied for clinical trials of depression therapy.
Beamformer Source Estimation Improves Accuracy of Motor-Imagery-based Brain Computer Interface
(2018-01-01)
Chan, Hui-Ling
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Wang, Jung-Wei
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Chen, Yong-Sheng
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交大名義發表
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分子醫學與生物工程研究所
Motor-imagery-based brain computer interface has advantages in high information transition rate and has potential to achieve high portability without using a display for visual stimulation. However, electroencephalographic data is a mixture of neural activity from various brain locations and also external artifacts. This paper presents a novel method utilizing source level features computed based on maximum contrast beamformer. The estimated source activity has the maximum contrast between the power during the reference period and the period that event-related synchronization or desynchronization emerges. Moreover, this method utilizes the classification structure based on divide-and-conquer concept to identify four classes of motor imageries, including left hand, right hand, tongue, and foot. The experimental results demonstrate the improvement in classification accuracy by using the proposed method.
Beamformer-based Imaging of Phase-Amplitude Coupling using Electromagnetic Brain Activity
(2015)
Chan, Hui-Ling
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Chen, Yong-Sheng
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Chen, Li-Fen
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Baillet, Sylvain
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資訊工程學系
Phase-amplitude coupling (PAC) between neural oscillations of different frequencies plays a crucial role in cognitive processing. Assessing the PAC at both sensor and source levels may encounter the problem of spurious coupling because of the volume conduction, field spread, and source leakage. This paper presents a novel method, beamformer-based imaging of PAC (BIPAC), to estimate PAC between sources from electromagnetic signals. For each targeted brain region, this method can extract the source component with the maximum PAC to the reference signal. The results from two simulated MEG data sets demonstrated that the proposed method can achieve high localization accuracy and low spurious coupling.
Beamformer-based Spatiotemporal Imaging of Correlated Brain Activities
(2009)
Chen, I-Tzu
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Chen, Yong-Sheng
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Chen, Li-Fen
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資訊工程學系
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Department of Computer Science
The past findings have suggested that temporal correlation may relate the communications between the distributed areas. There are some studies in Magnetoencephalography and electroencephalography to analyze the functional connectivity between cortical areas with the oscillations feature of neuronal activity. However, it is also important to observe the functional connectivity through temporal correlation between cortical areas. We proposed a beamformer-based approach which exploits a maximum correlation criterion to maximize the significance level of correlation between brain activities. This criterion leads to a closed-form solution of the dipole orientation. Experiments with simulation data clearly demonstrate the effectiveness, necessity, and accuracy of the proposed method.
Beamformer-based spatiotemporal imaging of linearly-related source components using electromagnetic neural signals
(2015-07-01)
Chan, Hui-Ling
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Chen, Li-Fen
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Chen, I-Tzu
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Chen, Yong-Sheng
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分子醫學與生物工程研究所
Functional connectivity calculated using multiple channels of electromagnetic brain signals is often over- or underestimated due to volume conduction or field spread. Considering connectivity measures, coherence is suitable for the detection of rhythmic synchronization, whereas temporal correlation is appropriate for transient synchronization. This paper presents a beamformer-based imaging method, called spatiotemporal imaging of linearly-related source component (SILSC), which is capable of estimating connectivity at the cortical level by extracting the source component with the maximum temporal correlation between the activity of each targeted region and a reference signal. The spatiotemporal correlation dynamics can be obtained by applying SILSC at every brain region and with various time latencies. The results of six simulation studies demonstrated that SILSC is sensitive to detect the source activity correlated to the specified reference signal and is accurate and robust to noise in terms of source localization. In a facial expression imitation experiment, the correlation dynamics estimated by SILSC revealed the regions with mirror properties and the regions involved in motor control network when performing the imitation and execution tasks, respectively, with the left inferior frontal gyrus specified as the reference region. (C) 2015 Elsevier Inc. All rights reserved.
Bird's-eye view vision system for vehicle surrounding monitoring
(2008)
Liu, Yu-Chih
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Lin, Kai-Ying
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Chen, Yong-Sheng
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資訊工程學系
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Department of Computer Science
Blind spots usually lead to difficulties for drivers to maneuver their vehicles in complicated environments, such as garages, parking spaces, or narrow alleys. This paper presents a vision system which can assist drivers by providing the panoramic image of vehicle surroundings in a bird's-eye view. In the proposed system, there are six fisheye cameras mounted around a vehicle so that their views cover the whole surrounding area. Parameters of these fisheye cameras were calibrated beforehand so that the captured images can be dewarped into perspective views for integration. Instead of error-prone stereo matching, overlapping regions of adjacent views are stitched together by aligning along a seam with dynamic programming method followed by propagating the deformation field of alignment with Wendland functions. In this way the six fisheye images can be integrated into a single, panoramic, and seamless one from a look-down viewpoint. Our experiments clearly demonstrate the effectiveness of the proposed image-stitching method for providing the bird's eye view vision for vehicle surrounding monitoring.
Brain morphological changes associated with cyclic menstrual pain
(2010-09-01)
Tu, Cheng-Hao
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Niddam, David M.
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Chao, Hsiang-Tai
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Chen, Li-Fen
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Chen, Yong-Sheng
Primary dysmenorrhea (PDM) is the most prevalent gynecological disorder for women in the reproductive age. PDM patients suffer from lower abdominal pain that starts with the onset of the menstrual flow. Prolonged nociceptive input to the central nervous system can induce functional and structural alterations throughout the nervous system. In PDM, a chronic viscero-nociceptive drive of cyclic nature, indications of central sensitization and altered brain metabolism suggest a substantial central reorganization. Previously, we hypothesized that disinhibition of orbitofrontal networks could be responsible for increased pain and negative affect in PDM. Here, we further tested this hypothesis. We used an optimized voxel-based morphometry (VBM) approach to compare total and regional gray matter (GM) increases and decreases in 32 PDM patients with 32 healthy age and menstrual cycle matched (peri-ovulatory phase) controls. Abnormal decreases were found in regions involved in pain transmission, higher level sensory processing, and affect regulation while increases were found in regions involved in pain modulation and in regulation of endocrine function. Moreover. GM changes in regions involved in top-down pain modulation and in generation of negative affect were related to the severity of the experienced PDM pain. Our results demonstrate that abnormal GM volume changes are present in PDM patients even in the absence of pain. These changes may underpin a combination of impaired pain inhibition, increased pain facilitation and increased affect. Our findings highlight that longer lasting central changes may occur not only in sustained chronic pain conditions but also in cyclic occurring pain conditions. (C) 2010 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.
Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition
(2018-10-09)
Chan, Hui-Ling
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Kuo, Po-Chih
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Cheng, Chia-Yi
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Chen, Yong-Sheng
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交大名義發表
The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.
Decoding and encoding of visual patterns using magnetoencephalographic data represented in manifolds
(2014-11-15)
Kuo, Po-Chih
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Chen, Yong-Sheng
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Chen, Li-Fen
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Hsieh, Jen-Chuen
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分子醫學與生物工程研究所
Visual decoding and encoding are crucial aspects in investigating the representation of visual information in the human brain. This paper proposes a bidirectional model for decoding and encoding of visual stimulus based on manifold representation of the temporal and spatial information extracted from magnetoencephalographic data. In the proposed decoding process, principal component analysis is applied to extract temporal principal components (TPCs) from the visual cortical activity estimated by a beamforming method. The spatial distribution of each TPC is in a high-dimensional space and can be mapped to the corresponding spatiotemporal component (STC) on a low-dimensional manifold. Once the linear mapping between the STC and the wavelet coefficients of the stimulus image is determined, the decoding process can synthesize an image resembling the stimulus image. The encoding process is performed by reversing the mapping or transformation in the decoding model and can predict the spatiotemporal brain activity from a stimulus image. In our experiments using visual stimuli containing eleven combinations of checkerboard patches, the information of spatial layout in the stimulus image was revealed in the embedded manifold. The correlation between the reconstructed and original images was 0.71 and the correlation map between the predicted and original brain activity was highly correlated to the map between the original brain activity for different stimuli (r = 0.89). These results suggest that the temporal component is important in visual processing and manifolds can well represent the information related to visual perception. (c) 2014 Published by Elsevier Inc.
Decoding the perception of endogenous pain from resting-state MEG
(2017-01-01)
Kuo, Po-Chih
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Chen, Yi-Ti
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Chen, Yong-Sheng
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Chen, Li-Fen
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分子醫學與生物工程研究所
Decoding the neural representations of pain is essential to obtaining an objective assessment as well as an understanding of its underlying mechanisms. The complexities involved in the subjective experience of pain make it difficult to obtain a quantitative assessment from the induced spatiotemporal patterns of brain activity of high dimensionality. Most previous studies have investigated the perception of pain by analyzing the amplitude or spatial patterns in the response of the brain to external stimulation. This study investigated the decoding of endogenous pain perceptions according to resting-state magnetoencephalographic (MEG) recordings. In our experiments, we applied a beamforming method to calculate the brain activity for every brain region and examined temporal and spectral features of brain activity for predicting the intensity of perceived pain in patients with primary dysmenorrhea undergoing menstrual pain. Our results show that the asymmetric index of sample entropy in the precuneus and the sample entropy in the left posterior cingulate gyrus were the most informative characteristics associated with the perception of menstrual pain. The correlation coefficient (p=0.64, p < 0.001) between the predicted and self-reported pain scores demonstrated the high prediction accuracy. In addition to the estimated brain activity, we were able to predict accurate pain scores directly from MEG channel signals (p=0.65, p < 0.001). These findings suggest the possibility of using the proposed model based on resting-state MEG to predict the perceived intensity of endogenous pain.
Deep Neural Network with Attention Mechanism for Classification of Motor Imagery EEG
(2019-01-01)
Huang, Yen-Cheng
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Chang, Jia-Ren
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Chen, Li-Fen
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Chen, Yong-Sheng
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交大名義發表
This paper presents a deep neural network architecture for the classification of motor imagery electroencephalographic recordings. This classification task usually encounters difficulties such as data with poor signal-to-noise ratio, contamination from muscle activity, body movements, and external interferences, and both intra-subject and intersubject variability. Through the spatiotemporal features automatically learned from training data, deep neural networks continue to demonstrate their good performance, versatility, and adaptation capability. In this work, we developed a novel neural network model which can extract signal features from multiple electrodes in a manner similar to that of conventional signal processing methods, such as common spatial patterns and common temporal patterns. The proposed neural network model comprises an attention mechanism, which calculates the importance of each electrode, and a spatial convolution layer. Compared to the results obtained using a variety of state-of-the-art deep learning techniques, the proposed scheme represents a considerable advancement in classification accuracy when applied to the BCI competition IV dataset 2a. By training with data of all subjects, the proposed universal neural network model outperforms state-of-the-art methods in terms of classification accuracy.
Differences in white matter abnormalities between bipolar I and II disorders
(2010-12-01)
Liu, Jia-Xiu
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Chen, Yong-Sheng
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Hsieh, Jen-Chuen
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Su, Tung-Ping
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Yeh, Tzu-Chen
Background: Although patients with bipolar I and II disorders exhibit heterogeneous clinical presentations and cognitive functions, it remains unclear whether these two subtypes have distinct neural substrates. This study aimed to differentiate the fiber abnormalities between bipolar I and II patients using diffusion tensor images. Method: Fourteen bipolar I patients, thirteen bipolar II patients, and twenty-one healthy subjects were recruited. Fractional anisotropy (FA) values calculated from diffusion tensor images were compared among groups using two-sample t-test analysis in a voxel-wise manner. Correlations between the mean FA value of each survived area and the clinical characteristics as well as the scores of neuropsychological tests were further analyzed. Results: Patients of both subtypes manifested fiber impairments in the thalamus, anterior cingulate, and inferior frontal areas, whereas the bipolar II patients showed more fiber alterations in the temporal and inferior prefrontal regions. The FA values of the subgenual anterior cingulate cortices for both subtypes correlated with the performance of working memory. The FA values of the right inferior frontal area of bipolar land the left middle temporal area of bipolar II both correlated with executive function. For bipolar II patients, the left middle temporal and inferior prefrontal FA values correlated with the scores of YMRS and hypomanic episodes, respectively. Conclusions: Our findings suggest distinct neuropathological substrates between bipolar I and II subtypes. The fiber alterations observed in the bipolar I patients were majorly associated with cognitive dysfunction, whereas those in the bipolar II patients were related to both cognitive and emotional processing. (C) 2010 Elsevier B.V. All rights reserved.
Different patterns of abnormal gamma oscillatory activity in unipolar and bipolar disorder patients during an implicit emotion task
(2012-06-01)
Liu, Tai-Ying
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Hsieh, Jen-Chuen
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Chen, Yong-Sheng
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Tu, Pei-Chi
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Su, Tung-Ping
This study investigates the distinct patterns of local and long-range gamma oscillations between patients with bipolar disorder (BD) and major depressive disorder (MDD). Twenty BD patients, twenty MDD patients, and twenty normal controls participated in this study. For each participant, the event-related magnetoencephalographic responses while performing an implicit emotional task were recorded and processed with time-frequency analysis. Compared to normal controls, the BD patients exhibited the gamma power decease at the right frontal and prefrontal regions and yet gamma power increase at the right posterior temporal region. The abnormal long-range gamma oscillation between the right frontal and parietal-occipital region was also found. These results indicate that the BD patients may have hyperactivity in perceptual binding of emotional features and tend to be oversensitive to facial features. On the other hand, MOD patients displayed increased early gamma activity at the left anterior temporal region, which may imply their hyperactivated binding process of emotional features at corticolimbic regions. The distinct alterations of gamma patterns between the BD and MDD patients implicate that their impairments of binding processes are located at different regions. Gamma activity in the parietal and left posterior temporal regions may be a potential index to differentiate BD patients from MDD patients. (C) 2012 Elsevier Ltd. All rights reserved.