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Exploration of User's Mental State Changes during Performing Brain-Computer Interface
(2020-06-01)
Ko, Li-Wei
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Chikara, Rupesh Kumar
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Lee, Yi-Chieh
;
Lin, Wen-Chieh
;
交大名義發表
Substantial developments have been established in the past few years for enhancing the performance of brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user's mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user's visual area. BCI user's cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users' physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user's cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1-4 Hz), theta (4-7 Hz), and beta (13-30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1-4 Hz), alpha (8-12 Hz), and beta (13-30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user's cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.
Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems
(2019-11-01)
Lin, Chin-Teng
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Liu, Chi-Hsien
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Wang, Po-Sheng
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King, Jung-Tai
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Liao, Lun-De
A brain-computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.
Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface
(2019-10-01)
Lin, Bor-Shyh
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Lin, Bor-Shing
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Yen, Tzu-Hsiang
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Hsu, Chien-Chin
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Wang, Yao-Chin
Brain-computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).
Using a Novel LDA-Ensemble Framework to Classification of Motor Imagery Tasks for Brain-Computer Interface Applications
(2015-01-01)
Chiu, Ching-Yu
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Chen, Chih-Yu
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Lin, Yang-Yin
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Chen, Shi-An
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Lin, Chin-Teng
In this paper, we introduce a novel linear discriminate analysis (LDA) ensemble classifier utilizing the Mindo as a brain-computer interface (BCI) device to deal with the problem of motor imagery classification. With regard to the composition of the proposed system, we combine filter bank, sub-band common spatial pattern (SBCSP), LDA together for extracting features of EEG data and classifying the motor imagery with left or right states. In addition, we also employ a gradient descent (GD) algorithm to find the best weight associated with probability fusion function. This novel architecture not only boosts the accuracy of classification but maintains the computational efficiency of the system. Therefore, the proposed LDA-ensemble framework is able to be satisfied with each subject as demonstrated in Section III.
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression
(2016-01-01)
Wu, Dongrui
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Lawhern, Vernon J.
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Gordon, Stephen
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Lance, Brent J.
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Lin, Chin-Teng
There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabeled EEG epochs recorded during driving, how do we optimally select a small number of them to label so that an accurate regression model can be built from them to label the rest? Active learning is a promising solution to this problem, but interestingly, to our best knowledge, it has not been used for regression problems in BCI so far. This paper proposes a novel enhanced batch-mode active learning (EBMAL) approach for regression, which improves upon a baseline active learning algorithm by increasing the reliability, representativeness and diversity of the selected samples to achieve better regression performance. We validate its effectiveness using driver drowsiness estimation from EEG signals. However, EBMAL is a general approach that can also be applied to many other offline regression problems beyond BCI.
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
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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.
三維立體深度資訊於視覺誘發腦控顯示系統之研究
(2015)
黃乙白
;
Yi-Pai Huang
;
國立交通大學光電工程學系（所）
隨著科技的進步，為了讓生活更加便利，高科技產品已是生活中不可缺少的元素。在以人為出發點的理念上，我們所需要的是更方便、更簡單直觀的操作方法與介面。因此，腦機介面(Brain Computer Interfaces, BCI)成為一門重要的課題。腦機介面是連接大腦與電腦的溝通介面，能夠將使用者的想法面直接轉換成操作指令進而控制外部裝置，是一相當具直覺性的操作介面。本計畫選用的是穩態視覺誘發電位(Steady-State Visual Evoked Potential, SSVEP)作為媒介，但目前以視覺誘發電位做為媒介的研究，所選擇的大多是以2D、低閃爍頻率(<15Hz)的單色光作為輸入刺激源。這樣低頻率閃爍的刺激，會讓人感到不適、視覺疲勞，甚至可能引起偏頭痛、誘使癲癇的發作，所以為了改善這些情況，我們選擇中高頻率(30-45Hz)再加上將2D刺激源提升至3D刺激源，期望降低閃爍感並提高視覺誘發電位的效能。本計畫最終目標是創造出以視覺誘發電位做為基礎之腦機介面的應用，為了達成此目標，先探討不同3D變因對視覺誘發電位的影響，主要分成三大項：1.研究3D影像立體深度資訊對SSVEP的影響。2. 3D顯示技術對SSVEP響應之優化。3. 3D顯示影像不適感之量化回饋。3D-SSVEP技術不僅可應用於3D顯示器上以達到活化顯示面板業界之功效，更提供了腦機介面的應用平台，未來將可共同研發出適用於臨床醫學及日常生活(消費性電子)的腦機介面應用。
多彩視覺誘發腦控顯示系統與影像補償技術之研究
(2015)
謝漢萍
;
SHIEH HAN-PING D.
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國立交通大學光電工程學系（所）
現今顯示系統與使用者的互動已經越來越普及，然而在顯示技術逐漸成熟的同時，如何再創造出更多顯示器未來的價值，更是重要的課題。本計畫的研究目的便是在結合顯示技術提出一視覺腦機介面互動式顯示系統。視覺腦機介面為一經視覺刺激誘發腦電波變化，透過腦電波讓使用者可以直接對外界裝置與儀器進行控制的一種溝通與連結的管道。此技術的優勢在於以更直覺化的操作模式，並結合日益重要的腦科學研究領域，發展一具更為人性化的互動系統。本計畫研究不同的色彩視覺刺激對大腦神經電位反應的變化，進而延伸至靜態、動態影像的視覺刺激，發展影像處理之技術，同時解決影像失真、色分離等問題，保持影像的完整性，如此方可使視覺腦機互動系統有更靈活的運用空間，亦為顯示器產業引入更智慧化的應用層面，為顯示器產業與視覺腦機互動系統的重要發展。
A Novel Mechanism to Fuse Various Sub-aspect Brain-computer Interface (BCI) Systems with PSO for Motor Imagery Task
(2015-01-01)
Lin, Chin-Teng
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Hsieh, Tsung-Yu
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Liu, Yu-Ting
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Wu, Shang-Lin
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Lin, Yang-Yin
In this study, we develop a novel multi-fusion brain-computer interface (BCI) system based on a fuzzy neural network (FNN) and information fusion approaches to cope with a classification task for identifying right/left hand motor imagery. In the proposed system, we utilize a filter bank and sub-band common spatial pattern (SBCSP) to extract features from raw EEG data. A self-organizing neural fuzzy inference network (SONFIN) is then applied for a recognition task. In order to improve the classification performance, we form a committee of networks and employ fuzzy integral (FI) to attain a joint decision. To further optimize the fusion approaches, a particle swarm optimization (PSO) algorithm is exploited to globally update parameters used in the fusion stage. In consequence, our experimental result shows that the proposed fuzzy fusion system possesses superior performance compared to other comparative models.
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.

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