Browsing by Author "Kuo, Po-Chih"

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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.
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.
Interactions of BDNF Val66Met Polymorphism and Menstrual Pain on Brain Complexity
(2018-11-20)
Low, Intan
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Kuo, Po-Chih
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Tsai, Cheng-Lin
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Liu, Yu-Hsiang
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Lin, Ming-Wei
The irregularity and uncertainty of neurophysiologic signals across different time scales can be regarded as neural complexity, which is related to the adaptability of the nervous system and the information processing between neurons. We recently reported general loss of brain complexity, as measured by multiscale sample entropy (MSE), at pain-related regions in females with primary dysmenorrhea (PDM). However, it is unclear whether this loss of brain complexity is associated with inter-subject genetic variations. Brain-derived neurotrophic factor (BDNF) is a widely expressed neurotrophin in the brain and is crucial to neural plasticity. The BDNF Val66Met single-nucleotide polymorphism (SNP) is associated with mood, stress, and pain conditions. Therefore, we aimed to examine the interactions of BDNF Val66Met polymorphism and long-term menstrual pain experience on brain complexity. We genotyped BDNF Val66Met SNP in 80 PDM females (20 Val/Val, 31 Val/Met, 29 Met/Met) and 76 healthy female controls (25 Val/Val, 36 Val/Met, 15 Met/Met). MSE analysis was applied to neural source activity estimated from resting-state magnetoencephalography (MEG) signals during pain-free state. We found that brain complexity alterations were associated with the interactions of BDNF Val66Met polymorphism and menstrual pain experience. In healthy female controls, Met carriers (Val/Met and Met/Met) demonstrated lower brain complexity than Val/Val homozygotes in extensive brain regions, suggesting a possible protective role of Val/Val homozygosity in brain complexity. However, after experiencing long-term menstrual pain, the complexity differences between different genotypes in healthy controls were greatly diminished in PDM females, especially in the limbic system, including the hippocampus and amygdala. Our results suggest that pain experience preponderantly affects the effect of BDNF Val66Met polymorphism on brain complexity. The results of the present study also highlight the potential utilization of resting-state brain complexity for the development of new therapeutic strategies in patients with chronic pain.
Is immersion of any value? Whether, and to what extent, game immersion experience during serious gaming affects science learning
(2017-03)
Cheng, Meng-Tzu
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Lin, Yu-Wen
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She, Hsiao-Ching
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Kuo, Po-Chih
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教育研究所
Many studies have shown the positive impact of serious gaming on learning outcomes, but few have explored the relationships between game immersion and science learning. Accordingly, this study was conducted to investigate the effectiveness of learning by playing, as well as the dynamic process of game immersion experiences, and to further identify whether, and to what extent, immersion affects science learning through serious gaming by using the techniques of cluster analysis. A total of 63 seventh-grade students participated in the study, and a quasi-experimental research design was employed. The results demonstrated that the students gained a holistic understanding of the relevant scientific concepts because their performances on the scenario-based science assessment were significantly improved across serious gameplaying occasions, and the effect of learning was retained long term. Moreover, serious gaming provided students with the experience of immersion insofar as the students indicated a greater degree of immersion in the game over time. Furthermore, two core clusters presenting meaningful patterns, high gaming performance/high immersion and high science learning/low immersion, were revealed. Finally, various interpretations and implications of the obtained data are discussed.
Is immersion of any value? Whether, and to what extent, game immersion experience during serious gaming affects science learning
(2017-03)
Cheng, Meng-Tzu
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Lin, Yu-Wen
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She, Hsiao-Ching
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Kuo, Po-Chih
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教育研究所
Many studies have shown the positive impact of serious gaming on learning outcomes, but few have explored the relationships between game immersion and science learning. Accordingly, this study was conducted to investigate the effectiveness of learning by playing, as well as the dynamic process of game immersion experiences, and to further identify whether, and to what extent, immersion affects science learning through serious gaming by using the techniques of cluster analysis. A total of 63 seventh-grade students participated in the study, and a quasi-experimental research design was employed. The results demonstrated that the students gained a holistic understanding of the relevant scientific concepts because their performances on the scenario-based science assessment were significantly improved across serious gameplaying occasions, and the effect of learning was retained long term. Moreover, serious gaming provided students with the experience of immersion insofar as the students indicated a greater degree of immersion in the game over time. Furthermore, two core clusters presenting meaningful patterns, high gaming performance/high immersion and high science learning/low immersion, were revealed. Finally, various interpretations and implications of the obtained data are discussed.
Manifold decoding for neural representations of face viewpoint and gaze direction using magnetoencephalographic data
(2018-05-01)
Kuo, Po-Chih
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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
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資訊工程學系
The main challenge in decoding neural representations lies in linking neural activity to representational content or abstract concepts. The transformation from a neural-based to a low-dimensional representation may hold the key to encoding perceptual processes in the human brain. In this study, we developed a novel model by which to represent two changeable features of faces: face viewpoint and gaze direction. These features are embedded in spatiotemporal brain activity derived from magnetoencephalographic data. Our decoding results demonstrate that face viewpoint and gaze direction can be represented by manifold structures constructed from brain responses in the bilateral occipital face area and right superior temporal sulcus, respectively. Our results also show that the superposition of brain activity in the manifold space reveals the viewpoints of faces as well as directions of gazes as perceived by the subject. The proposed manifold representation model provides a novel opportunity to gain further insight into the processing of information in the human brain.
MoBI-Box: A Next Generation Mobile Brain-Body Imaging Platform
(2016)
Wu, Ying Choon
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Wang, Jeffery
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Tran, Andrew
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Schperberg, Alex
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Caldwell, John
MoBI-Box is an inexpensive, portable, and ergonomic desktop system for simultaneously measuring and integrating multiple types of biosignals that transpire at different time scales and levels of organization. It can be assembled from readily available, off-the-shelf components and customized to suit specific needs. It can be implemented in a wide range of settings, including homes, classrooms, clinics, and research labs for only a modest start-up cost (beginning around $1200). MoBI-Box systems are currently in use for a number of training and research applications. This paper will outline an ongoing project to explore the impact of motor imagery on learning of a complex skill with both motoric and conceptual elements namely, learning Mandarin orthography. Findings have implications for the development of training systems in other specialized domains that require not only kinesthetic expertise, but also specialized knowledge.
NEUROSCIENCE-INSPIRED RECURRENT NETWORK FOR OBJECT RECOGNITION
(2017-01-01)
Chang, Jia-Ren
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Kuo, Po-Chih
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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
Deep neural networks inspired by the connections among biological neurons have been highly effective in the advancement of computer vision. Recent research into recurrent neural networks has taken account of forward as well as recurrent connections in a neural network architecture. In this study, we proposed a model that mirrors the architecture of the human ventral pathway with separate layers representing brain regions connected using long-range recurrent links. MAR 10/100 datasets were used to assess the performance of object recognition using the proposed model and the results were compared with those obtained using state-of-the-art methods. We demonstrated that the classification accuracy increased as the number of recurrences increased. Our results suggest that the proposed neuroscience-inspired model can facilitate object recognition in computer vision and may help to elucidate neurological mechanisms in the human brain.
Objective Assessment of Menstrual Pain Scale from Resting Brain Signals
(2014-01-01)
Hu, Pei-Chi
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Kuo, Po-Chih
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Chen, Li-Fen
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Chen, Yong-Sheng
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分子醫學與生物工程研究所
Objective assessment of pain scale is essential to the understanding of its mechanism as well as the development of pain treatments. The purpose of this work is to develop an objective metrics for measuring endogenous pain scale by analyzing resting magnetoencephalographic data. The data used in this study were collected from fourteen PDM patients at the menstrual phase. Features were extracted by using spectral analysis, temporal complexity analysis and hemispheric asymmetric calculation. Eight features were selected and linear regression was adopted to predict the pain level. The averaged residual error was 0.21 with pain scales ranging from 0 to 10, suggesting that the proposed method could be a reliable indicator for the assessment of endogenous pain scale.
Supervised learning for neural manifold using spatiotemporal brain activity
(2015-12)
Kuo, Po-Chih
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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
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資訊工程學系
Objective. Determining the means by which perceived stimuli are compactly represented in the human brain is a difficult task. This study aimed to develop techniques for the construction of the neural manifold as a representation of visual stimuli. Approach. We propose a supervised locally linear embedding method to construct the embedded manifold from brain activity, taking into account similarities between corresponding stimuli. In our experiments, photographic portraits were used as visual stimuli and brain activity was calculated from magnetoencephalographic data using a source localization method. Main results. The results of 10 x 10-fold cross-validation revealed a strong correlation between manifolds of brain activity and the orientation of faces in the presented images, suggesting that high-level information related to image content can be revealed in the brain responses represented in the manifold. Significance. Our experiments demonstrate that the proposed method is applicable to investigation into the inherent patterns of brain activity.