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Augmented Wire-Embedded Silicon-Based Dry-Contact Sensors for Electroencephalography Signal Measurements
(2020-04-01)
Lin, Chin-Teng
;
Yu, Yi-Hsin
;
King, Jung-Tai
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Liu, Chi-Hsien
;
Liao, Lun-De
The aim of this study was to develop a novel dry electroencePhalography (EEG) sensor with a soft, pliable pad that conforms to the contours of the skin and skull providing a suitable surface contact area for collecting elec: trical potential signals and ensuring a reliable connection. In this study, based on our experience in developing flexible silver/silicon-based dry-contact sensors (SBDSs) for biognal measurements, we proposed a new, augmented weirle-embedded silicon-based dry-contact sensor (WSBDS) with a long lifespan and better performance in EEG measurements. The following two augmentation concepts were proposed in this design and implemented in fabrication: 1) the addition of a metal stud and 2) the embedding of copper wires into the fingers of an acicular SBDS. The forehead sensor is suitable for forehead EEG measurements, and the acicular sensor is designed for application to hair-covered sites, where it can overcome hair interference to achieve satisfactory scalp contact while maintaining low impedance at the skin-electrode interface. Finally, thisi faugm!nted WSBDS performed well in human EEG recording in a designed brain-computer interface (BCI) experiment and is easiblefor practical applications.
Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients
(2020-01-01)
Cao, Zehong
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Lin, Chin-Teng
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Lai, Kuan-Lin
;
Ko, Li-Wei
;
King, Jung-Tai
Inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity reflecting the robustness of brain systems. In this study, we present a novel application of multiscale relative inherent fuzzy entropy using repetitive steady-state visual evoked potentials (SSVEPs) to investigate EEG complexity change between two migraine phases, i.e., interictal (baseline) and preictal (before migraine attacks) phases. We used a wearable headband EEG device with O1, Oz, O2, and Fpz electrodes to collect EEG signals from 80 participants [40 migraine patients and 40 healthy controls (HCs)] under the following two conditions: During resting state and SSVEPs with five 15-Hz photic stimuli. We found a significant enhancement in occipital EEG entropy with increasing stimulus times in both HCs and patients in the interictal phase, but a reverse trend in patients in the preictal phase. In the 1st SSVEP, occipital EEG entropy of the HCs was significantly lower than that of patents in the preictal phase (FDR-adjusted p < 0.05). Regarding the transitional variance of EEG entropy between the 1st and 5th SSVEPs, patients in the preictal phase exhibited significantly lower values than patients in the interictal phase (FDR-adjusted p < 0.05). Furthermore, in the classification model, the AdaBoost ensemble learning showed an accuracy of 81 $ \pm $ 6 & x0025; and area under the curve of 0.87 for classifying interictal and preictal phases. In contrast, there were no differences in EEG entropy among groups or sessions by using other competing entropy models, including approximate entropy, sample entropy, and fuzzy entropy on the same dataset. In conclusion, inherent fuzzy entropy offers novel applications in visual stimulus environments and may have the potential to provide a preictal alert to migraine patients.
A Motor Imagery Based Brain-Computer Interface System via Swarm-Optimized Fuzzy Integral and Its Application
(2016)
Wu, Shang-Lin
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Liu, Yu-Ting
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Chou, Kuang-Pen
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Lin, Yang-Yin
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Lu, Jie
A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.
Driving Fatigue Prediction with Pre-Event Electroencephalography (EEG) via a Recurrent Fuzzy Neural Network
(2016)
Liu, Yu-Ting
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Wu, Shang-Lin
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Chou, Kuang-Pen
;
Lin, Yang-Yin
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Lu, Jie
We propose an electroencephalography (EEG) prediction system based on a recurrent fuzzy neural network (RFNN) architecture to assess drivers\' fatigue degrees during a virtual-reality (VR) dynamic driving environment. Prediction of fatigue degrees is a crucial and arduous biomedical issue for driving safety, which has attracted growing attention of the research community in the recent past. Meanwhile, combined with the benefits of measuring EEG signals facilitates, many EEG-based brain-computer interfaces (BCIs) have been developed for use in real-time mental assessment. In the literature, EEG signals are severely blended with stochastic noise; therefore, the performance of BCIs is constrained by low resolution in recognition tasks. For this rationale, independent component analysis (ICA) is usually used to find a source mapping from original data that has been blended with unrelated artificial noise. However, the mechanism of ICA cannot be used in real-time BCI design. To overcome this bottleneck, the proposed system in this paper utilizes a recurrent self-evolving fuzzy neural work (RSEFNN) to increase memory capability for adaptive noise cancellation when assessing drivers\' mental states during a car driving task. The experimental results without the use of ICA procedure indicate that the proposed RSEFNN model remains superior performance compared with the state-of-thearts models.
EEG-based Driving Fatigue Prediction System Using Functional-link-based Fuzzy Neural Network
(2014)
Liu, Yu-Ting
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Lin, Yang-Yin
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Wu, Shang-Lin
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Chuang, Chun-Hsiang
;
Prasad, Mukesh
This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a non-linear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers\' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.
Assessment of Mental Fatigue: An EEG-based Forecasting System for Driving Safety
(2015-01-01)
Liu, Yu-Ting
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Lin, Yang-Yin
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Wu, Shang-Lin
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Hsieh, Tsung-Yu
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Lin, Chin-Teng
This study proposes an EEG-based forecasting system based on a functional-link recurrent self-evolving fuzzy neural network (FL-RSEFNN) for assessing mental fatigue during a highway driving task. Drivers\' cognitive states significantly affect driving safety, especially for fatigue or drowsy driving which is one of common factors to endanger individuals and the public safety. In this study, a FL-RSEFNN employs an on-line gradient descent (GD) learning rule to address the EEG regression problem in brain dynamics for estimation of driving fatigue. We analyze brain dynamics in a car driving task, which is constructed in a simulated virtual reality (VR) environment. The EEG-based forecasting system is evaluated using the generalized cross-subject approach, and the results indicate that the FL-RSEFNN is superior to state-of-the-art models regardless of the use of recurrent or non-recurrent structures.
An EEG-based brain-computer interface for dual task driving detection
(2014-04-10)
Wang, Yu-Kai
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Chen, Shi-An
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Lin, Chin-Teng
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資訊工程學系
;
腦科學研究中心
The development of brain-computer interfaces (BCI) for multiple applications has undergone extensive growth in recent years. Since distracted driving is a significant cause of traffic accidents, this study proposes one BCI system based on EEG for distracted driving. The removal of artifacts and the selection of useful brain sources are the essential and critical steps in the application of electroencephalography (EEG)-based BCI. In the first model, artifacts are removed, and useful brain sources are selected based on the independent component analysis (ICA). In the second model, all distracted and concentrated EEG epochs are recognized with a self-organizing map (SOM). This BCI system automatically identified independent components with artifacts for removal and detected distracted driving through the specific brain sources which are also selected automatically. The accuracy of the proposed system approached approximately 90% for the recognition of EEG epochs of distracted and concentrated driving according to the selected frontal and left motor components. (C) 2013 Published by Elsevier B.V.
Design of the Multi-channel Electroencephalography-based Brain-Computer Interface with Novel Dry Sensors
(2012)
Wu, Shang-Lin
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Liao, Lun-De
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Liou, Chang-Hong
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Chen, Shi-An
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Ko, Li-Wei
The traditional brain-computer interface (BCI) system measures the electroencephalography (EEG) signals by the wet sensors with the conductive gel and skin preparation processes. To overcome the limitations of traditional BCI system with conventional wet sensors, a wireless and wearable multi-channel EEG-based BCI system is proposed in this study, including the wireless EEG data acquisition device, dry spring-loaded sensors, a size-adjustable soft cap. The dry spring-loaded sensors are made of metal conductors, which can measure the EEG signals without skin preparation and conductive gel. In addition, the proposed system provides a size-adjustable soft cap that can be used to fit user's head properly. Indeed, the results are shown that the proposed system can properly and effectively measure the EEG signals with the developed cap and sensors, even under movement. In words, the developed wireless and wearable BCI system is able to be used in cognitive neuroscience applications.
Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors
(2012-01-28)
Liao, Lun-De
;
Chen, Chi-Yu
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Wang, I-Jan
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Chen, Sheng-Fu
;
Li, Shih-Yu
A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering.
Alpha modulation in parietal and retrosplenial cortex correlates with navigation performance
(2012-01-01)
Chiu, Te-Cheng
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Gramann, Klaus
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Ko, Li-Wei
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Duann, Jeng-Ren
;
Jung, Tzyy-Ping
The present study investigated the brain dynamics accompanying spatial navigation based on distinct reference frames. Participants preferentially using an allocentric or an egocentric reference frame navigated through virtual tunnels and reported their homing direction at the end of each trial based on their spatial representation of the passage. Task-related electroencephalographic (EEG) dynamics were analyzed based on independent component analysis (ICA) and subsequent clustering of independent components. Parietal alpha desynchronization during encoding of spatial information predicted homing performance for participants using an egocentric reference frame. In contrast, retrosplenial and occipital alpha desynchronization during retrieval covaried with homing performance of participants using an allocentric reference frame. These results support the assumption of distinct neural networks underlying the computation of distinct reference frames and reveal a direct relationship of alpha modulation in parietal and retrosplenial areas with encoding and retrieval of spatial information for homing behavior.

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