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Direction of Arrival Estimation of Speech Signals Using ICA and MUSIC Methods
(2010)
Liu, Chun-Li
;
Hang, Hsueh-Ming
;
電機學院
;
College of Electrical and Computer Engineering
Techniques of using a microphone array to determine a sound source location, the localization problem, has been studied for many years. A popular method is the so-called MUSIC (Multiple Signal Classification). There is a second type of method that tries to solve both sound separation and localization problems in one setting. The second method used for localization purpose is less known. In this study, we investigate and compare these two approaches. We show the strengths of each method and what their best application scenarios are.(1)
可攜式EEG/EKG/fNIRS腦神經影像系統研發暨其整合型生醫感測處理晶片系統設計---子計畫三：低功耗生醫信號處理及影像重建系統單晶片設計(I)
(2008)
方偉騏
;
Fang Wai-Chi
;
國立交通大學電子工程學系及電子研究所
本計畫在設計一顆低功率生醫訊號處理及影像重建系統單晶片(SoC)，應用在可攜式的腦神經影像儀之核心硬體。此晶片系統架構主體為一個新穎的二維獨立成份分析(2D-ICA)影像處理器並結合資訊壓縮模組以及短距通訊模組。此系統單晶片收集來自前端偵測模組的生醫訊號其中包含了fNIRS、EEG及EKG的訊號，並進行即時的生醫訊號處理及影像重建運算，將處理完的結果送至後端的science station。此系統單晶片進行影像重建功能時，除了自身取得的影像資訊，另外必須從其周圍四顆fNIR node擷取raw image data，以進行五組的2D-ICA影像去雜訊及重建演算。當即時解析所擷取含雜訊的影像資訊時，必須並配合ARM micro computer控制EKG信號，以便補償血液流動與血管擴張時對fNIR image所產生的雜訊。經過獨立成份分析影像處理後的清晰fNIR影像資訊會採JPEG無失真壓縮機制，保存完整醫學影像資訊，最後以無線網路(WLAN)傳輸至science station進行3D影像重建。我們提出一個由2×2 node 組成的cluster架構，此種擺放方式可以使得整體node 陣列所需的擺放面積大幅減少，單位node面積減少一半，還可以提升受測者的舒適度；此外我們引進super node concept，將所有cluster內所有node raw image在2D-ICA演算完畢後都由ARM 7控制，傳輸至cluster內同一顆super node，並批次傳輸至science station，達到降低功率耗損，增加頻寬使用的效率。本子計畫的目的便是針對總計劃輕量化可攜式即時fNIR影像去雜訊的需求，來設計製作『低功率生醫訊號處理及影像重建系統單晶片』，單晶片系統包含ICA image pre-processor、2D-ICA image processor、power management module以及ARM IP，為了節省功率消耗，我們未來將採用90m製程規格晶片下線。研究人員將根據以下三點來建構系統：（1）設計fNIR影像處理器（2）設計cluster內node communication、super node低功率，高頻寬使用效率ARM IP。（3）結合EKG及node自身與周圍raw image資訊，找出最佳fNIR/EKG演算法整合至影像重建系統單晶片中。SoC系統驗證將採用CIC-concord平台並與CIC合作開發適合此計畫使用，並將所有子系統模組IP化並整合至此平台。第一年計畫摘要：本計劃將發展2D-ICA演算法並據之來設計功能性近紅外線 (fNIR)影像處理晶片。此晶片將處理來自子計畫2 經過ADC傳送過來的多樣生醫訊號。本計劃初期演算法設計冀求能達成即時處理生醫影像訊號的功能，另考慮晶片設計需達到低功率消耗之要求，故此設計也需精簡的fNIR影像處理演算法。為了達成可攜式fNIR影像處理系統，本計劃將研發低功耗cell based libray，並以之來設計低功率的影像處理晶片。研究初期將以FPGA驗證根據2D-ICA演算法發展出來之硬體雛型規格；另一方面，本計劃也將發展JPEG無失真壓縮演算法，並以FPGA平台來驗證根據無失真JPEG演算法發展出來之硬體雛型規格。在此階段，本計劃將協助建立對應於子計畫1所需之生物規格與驗證方法。第二年計畫摘要：第二年的工作預計採用TSMC 0.13um 1P6M mixed-signal 製程，以晶片實現第一年經FPGA發展出來之演算法硬體雛型。此外，本系統亦整合ARM IP，設計系統pipeline流程以及communication控制器。本年度將持續完成系統與science station的wireless communication，以System in Package (SiP)方式完成fNIR/EEG生醫信號處理平台。最後，評估系統穩定性及容錯度是否符合當初制定的規格。第三年計畫摘要：根據子計畫1提供之相關於fNIR與全域EKG （global EKG）訊號時變關係規格，近一步研發整合EKG訊號至fNIR image processor，以改良fNIR/EKG影像成像效果。第三年的工作將採用UMC 90nm mixed-signal製程來整合fNIR image processor以及wireless communication module，冀期完成整合多功能fNIR/EEG/EKG 單晶片系統，此單晶片測試及驗證將使用CIC-concord平台來實踐。本計劃將與子計畫1配合來完成系統層次的整合，並協助進行fNIR/EEG/EKG生醫訊號科學分析及應用。
FPGA Implementation of 4-Channel ICA for On-line EEG Signal Separation
(2008)
Huang, Wei-Chung
;
Hung, Shao-Hang
;
Chung, Jen-Feng
;
Chang, Meng-Hsiu
;
Van, Lan-Da
Blind source separation of independent sources from their mixtures is a common problem for multi-sensor applications in real world, for example, speech or biomedical signal processing. This paper presents an independent component analysis (ICA) method with information maximization (Infomax) update applied into 4-channel one-line EEG signal separation. This can be implemented on FPGA with a fixed-point number representation, and then the separated signals are transmitted via Bluetooth. As experimental results, the proposed design is faster 56 times than soft performance, and the correlation coefficients at least 80% with the absolute value are compared with off-line processing results. Finally, live demonstration is shown in the DE2 FPGA board, and the design is consisted of 16,605 logic elements.
EEG-based Cognitive State Monitoring and Predition by Using the Self-Constructing Neural Fuzzy System
(2010)
Lin, Fu-Chang
;
Ko, Li-Wei
;
Chen, Shi-An
;
Chen, Ching-Fu
;
Lin, Chin-Teng
Driver's cognitive state monitoring has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. In our past studies, we found that the EEG power spectrum changes were highly correlated with the driver's driving behavior performance. In this study, we attempt to construct an EEG-based self-constructing neural fuzzy system to monitor and predict the driver's cognitive state. The difficulties in developing such a system are lack of significant index for detecting drowsiness and the interference of the complicated noise in a realistic and dynamic driving environment. Our experimental results including correlation and prediction show that the performances of our proposed system are significantly higher than using the traditional neural networks. Besides, the proposed EEG-based self-constructing neural fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications.
Portable Brain-Heart Monitoring System
(2010)
Fu, Chih-Chung
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Chen, Chiu-Kuo
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Tseng, Shao-Yen
;
Kang, Shih
;
Chua, Ericson
A portable brain-heart monitoring system is proposed to integrate and miniaturize those heavy equipments in the hospitals. The system comprises a 4-channel independent component analysis (ICA) engine for artifact removal from EEG, a heart-rate variability (HRV) analysis engine for on-line HRV analysis and a diffuse optical tomography (DOT) engine for reconstruction of the absorption coefficient image of the brain tissue. A lossless compression module achieves 2.5 compression ratio is also employed to reduce the power consumption of the wireless transmission. EEG, EKG and near-infrared signals acquired from the analog front-end IC are processed in real-time or bypassed according to user configurations. Processed data and raw data are compressed and sent to a remote science station by a commercial Bluetooth module for further analysis and 3-D visualization and remote diagnosis. The ICA and HRV engine are verified by real EEG and EKG signals while the DOT engine is verified by an experimental model. The system is implemented using UMC 65nm CMOS technology, and the core size is 680x680 um(2), and the estimated power consumption of the chip working at 24 MHz under full mode is 3.6 mW.
Estimating the Level of Motion Sickness Based on EEG Spectra
(2011)
Ko, Li-Wei
;
Wei, Chun-Shu
;
Jung, Tzyy-Ping
;
Lin, Chin-Teng
;
電機工程學系
Motion sickness (MS) is a normal response to real, perceived, or even anticipated movement. People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Although many motion sickness-related biomarkers have been identified, but how to estimate human's motion sickness level (MSL) is a big challenge in the operational environment. Traditionally, questionnaire and physical check are the common ways to passively evaluate subject's sickness level. Our past studies had investigated the EEG activities correlated with motion sickness in a virtual-reality based driving simulator. The driving simulator comprised an actual automobile mounted on a Stewart motion platform with six degrees of freedom, providing both visual and vestibular stimulations to induce motion-sickness in a manner that is close to that in daily life. EEG data were acquired at a sampling rate of 500 Hz using a 32-channel EEG system. The acquired EEG signals were analyzed using independent component analysis (ICA) and time-frequency analysis to assess EEG correlates of motion sickness. Subject's degree of motion-sickness was simultaneously and continuously reported using an onsite joystick, providing non-stop psychophysical references to the recorded EEG changes. We found that the parietal, motor, occipital brain regions exhibited significant EEG power changes in response to vestibular and visual stimuli. Based on these findings and experimental results, this study aims to develop an EEG-based system to estimate subject's motion sickness level upon the EEG power spectra from motion-sickness related brain areas. The MS evaluation system can be applied to early detection of the subject's motion sickness and prevent its uncomfortable syndromes in our daily life. Furthermore, the experiment results could also lead to a practical human-machine interface for noninvasive monitoring of motion sickness of drivers or passengers in real-world environments.
An EEG-Based Brain-Computer Interface for Dual Task Driving Detection
(2011)
Lin, Chin-Teng
;
Wang, Yu-Kai
;
Chen, Shi-An
;
腦科學研究中心
;
Brain Research Center
A novel detective model for driver distraction was proposed in this study. Driver distraction is a significant cause of traffic accidents during these years. To study human cognition under a specific driving task, one virtual reality (VR)-based simulation was built. Unexpected car deviations and mathematics questions with stimulus onset asynchrony (SOA) were designed. Electroencephalography (EEG) is a good index for the distraction level to monitor the effects of the dual tasks. Power changing in Frontal and Motor cortex were extracted for the detective model by independent component analysis (ICA). All distracting and non-distracting EEG epochs could be revealed the existence by self-organizing map (SOM). The results presented that this system approached about 90% accuracy to recognize the EEG epochs of non-distracting driving, and might be practicable for daily life.
Motion-Sickness Related Brain Areas and EEG Power Activates
(2009)
Chen, Yu-Chieh
;
Duann, Jeng-Ren
;
Lin, Chun-Ling
;
Chuang, Shang-Wen
;
Jung, Tzyy-Ping
This study investigates electroencephalographic (EEG) correlates of motion sickness in a virtual-reality based driving simulator. The driving simulator comprised an actual automobile mounted on a Stewart motion platform with six degrees of freedom, providing both visual and vestibular stimulations to induce motion-sickness in a manner that is close to that in daily life. EEG data were acquired at a sampling rate of 500 Hz using a 32-channel EEG system. The acquired EEG signals were analyzed using independent component analysis (ICA) and time-frequency analysis to assess EEG correlates of motion sickness. Subject's degree of motion-sickness was simultaneously and continuously reported using an onsite joystick, providing non-stop psychophysical references to the recorded EEG changes. Five Motion-sickness related brain processes with equivalent dipoles located in the left motor, the parietal, the right motor, the occipital and the occipital midline areas were consistently identified across all subjects. These components exhibited distinct spectral suppressions or augmentation in motion sickness. The results of this study could lead to a practical human-machine interface for noninvasive monitoring of motion sickness of drivers or passengers in real-world environments.
Implementation of a Motion Sickness Evaluation System Based on EEG Spectrum Analysis
(2011)
Wei, Chun-Shu
;
Chuang, Shang-Wen
;
Wang, Wan-Ru
;
Ko, Li-Wei
;
Jung, Tzyy-Ping
Motion sickness is a normal response to real, perceived, or even anticipated movement. People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Motion sickness occurs when the body, the inner ear, and the eyes send conflicting signals to the brain. Sensory conflict theory that came about in the 1970's has become the most widely accepted theorem of motion-sickness among scientists [1]. The theory proposed that the conflict between the incoming sensory inputs could induce motion-sickness. However, some new research studies have appeared to tackle the issue of the vestibular function in central nervous system (CNS). In the previous human subject studies, researchers attempt to confirm the brain areas involved in the conflict in multi-modal sensory systems by means of clinical or anatomical methods. Our past studies had investigated the EEG activities correlated with motion sickness in a virtual-reality based driving simulator. We found that the parietal, motor, occipital brain regions exhibited significant EEG power changes in response to vestibular and visual stimuli. Based on these experimental results, we attempt to implement an EEG-based evaluation system to estimate subject's motion sickness level upon the major EEG power spectra from these motion sickness related brain area in this study. The evaluation system can be applied to early detect the subject's motion sickness level and prevent the uncomfortable syndromes occurred in advance in our daily life.
An Efficient Human Detection System Using Adaptive Neural Fuzzy Networks
(2008-09-01)
Lin, Chin-Teng
;
Siana, Linda
;
電控工程研究所
;
Institute of Electrical and Control Engineering
The proposed efficient human detection system is based on an adaptive neural fuzzy network (ANFN). In the preprocessing process, we apply a background subtraction algorithm with Gaussian mixture model (GMM) background model to extract moving objects, and adopt a shadow elimination process to eliminate some noise and irregular moving objects. The modified independent component analysis (mICA) based conditional entropy is presented to extract and select the efficient features (independent components). Furthermore, we use an adaptive neural fuzzy network as a human detection system to recognize human objects. The ANFN model uses a functional link neural network (FLNN) to create the consequent part of the fuzzy rules. The orthogonal polynomial is applied as a functional expansion of the FLNN. The learning process of ANFN consists of structure learning and parameter learning. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the back propagation method, is used to adjust the membership function and corresponding weights of the FLNN. Finally, the proposed human detection system is applied in various circumstances. The results of this study demonstrate the accuracy of the proposed method.

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🏛️國立陽明交通大學機構典藏

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