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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Subject: search.filters.subject.brain-computer interface

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Now showing 1 - 8 of 8
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    Augmented Wire-Embedded Silicon-Based Dry-Contact Sensors for Electroencephalography Signal Measurements
    (2020-04-01)
    Lin, Chin-Teng
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    Yu, Yi-Hsin
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    King, Jung-Tai
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    Liu, Chi-Hsien
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    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.
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    Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model
    (2019-09-01)
    Chikara, Rupesh Kumar
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    Ko, Li-Wei
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    交大名義發表
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    生物科技學系
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    生物資訊及系統生物研究所
    Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research.
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    To Repeat or Not to Repeat? Redesigning Repeating Auditory Alarms Based on EEG Analysis
    (2019-01-01)
    Lee, Yi-Chen
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    Cherng, Fu-Yin
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    King, Jung-Tai
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    Lin, Wen-Chieh
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    資訊工程學系
    Auditory alarms that repeatedly interrupt users until they react are common, especially in the context of alarms. However, when an alarm repeats, our brains habituate to it and perceive it less and less, with reductions in both perception and attention-shifting: a phenomenon known as the repetition-suppression effect (RS). To retain users' perception and attention, this paper proposes and tests the use of pitch-and intensity-modulated alarms. Its experimental findings suggest that the proposed modulated alarms can reduce RS, albeit in different patterns, depending on whether pitch or intensity is the focus of the modulation. Specifically, pitch-modulated alarms were found to reduce RS more when the number of repetitions was small, while intensity-modulated alarms reduced it more as the number of repetitions increased. Based on these results, we make several recommendations for the design of improved repeating alarms, based on which modulation approach should be adopted in various situations.
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    Measuring the Influences of Musical Parameters on Cognitive and Behavioral Responses to Audio Notifications Using EEG and Large-scale Online Studies
    (2019-01-01)
    Cherng, Fu-Yin
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    Lee, Yi-Chen
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    King, Jung-Tai
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    Lin, Wen-Chieh
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    資訊工程學系
    Prior studies have evaluated various designs for audio notifications. However, calls for more in-depth research on how such notifications work, especially at the level of users' cognitive states, have gone unanswered; and studies evaluating audio notifications with large numbers of participants in multiple environments have been rare. This study conducted an electroencephalography study (N=20) and an online study (N=967) to enhance understandings of how three musical parameters - melody (simple, complex), pitch (high, low), and tempo (fast, slow) - influenced users' cognition and behaviors. There are eight different notifications with different combinations of these parameters. The online study analyzed the effects of user-specific and environmental information on users' behaviors while they listened to these notifications. The results revealed that tempo and pitch have the main effect on the speed and strength (accuracy) of users' cognition and behaviors. The users' characteristics and environments influenced the effects of these musical parameters.
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    A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection
    (2016-02-01)
    Lo, Chi-Chun
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    Chien, Tsung-Yi
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    Chen, Yu-Chun
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    Tsai, Shang-Ho
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    Fang, Wai-Chi
    Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.
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    How human-computer interface redefines original lifestyle in architecture?
    (2011-01-01)
    Huang, Yu-Chun
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    建築研究所
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    Institude of Architecture
    In the twenty-first century, with the development of technology, architecture is not just a shelter or even "a frozen music" (by Johann Wolfgang van Goethe). Instead, architecture becomes a huge communication channel between people and space. Thus with the integration of computational technology into architecture design, the new term-"smart space" has emerged. Some researchers have applied technology into intelligent space design, such as intelligent kitchen, smart collaboration design studio and smart health care environment. Sometimes creating a fancy or an alternative smart space is not enough, the essential factor of smart space is to satisfy human needs and keep good relationship between human and space. Therefore, this research will find out what the role of human-computer interaction in smart space design is. And the purpose of this research is to present two different cases of smart space: "Time home pub" and "BCI studio" and make the analysis of the transition from traditional architecture to smart space design. Also this research further discusses how HCI redefines the functions and lifestyle of traditional architecture.
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    Polychromatic SSVEP stimuli with subtle flickering adapted to brain-display interactions
    (2017-02)
    Chien, Yu-Yi
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    Lin, Fang-Cheng
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    Zao, John K.
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    Chou, Ching-Chi
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    Huang, Yi-Pai
    Objective. Interactive displays armed with natural user interfaces (NUIs) will likely lead the next breakthrough in consumer electronics, and brain-computer interfaces (BCIs) are often regarded as the ultimate NUI-enabling machines to respond to human emotions and mental states. Steady-state visual evoked potentials (SSVEPs) are a commonly used BCI modality due to the ease of detection and high information transfer rates. However, the presence of flickering stimuli may cause user discomfort and can even induce migraines and seizures. With the aim of designing visual stimuli that can be embedded into video images, this study developed a novel approach to induce detectable SSVEPs using a composition of red/green/ blue flickering lights. Approach. Based on the opponent theory of colour vision, this study used 32 Hz/40 Hz rectangular red-green or red-blue LED light pulses with a 50% duty cycle, balanced/ equal luminance and 0 degrees/180 degrees phase shifts as the stimulating light sources and tested their efficacy in producing SSVEP responses with high signal-to-noise ratios (SNRs) while reducing the perceived flickering sensation. Main results. The empirical results from ten healthy subjects showed that dual-colour lights flickering at 32 Hz/40 Hz with a 50% duty cycle and 180 degrees phase shift achieved a greater than 90% detection accuracy with little or no flickering sensation. Significance. As a first step in developing an embedded SSVEP stimulus in commercial displays, this study provides a foundation for developing a combination of three primary colour flickering backlights with adjustable luminance proportions to create a subtle flickering polychromatic light that can elicit SSVEPs at the basic flickering frequency.
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    Selective Transfer Learning for EEG-Based Drowsiness Detection
    (2015-01-01)
    Wei, Chun-Shu
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    Lin, Yuan-Pin
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    Wang, Yu-Te
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    Jung, Tzyy-Ping
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    Bigdely-Shamlo, Nima
    On the pathway from laboratory settings to real world environment, a major challenge on the development of a robust electroencephalogram (EEG)-based brain-computer interface (BCI) is to collect a significant amount of informative training data from each individual, which is labor intensive and time-consuming and thereby significantly hinders the applications of BCIs in real-world settings. A possible remedy for this problem is to leverage existing data from other subjects. However, substantial inter-subject variability of human EEG data could deteriorate more than improve the BCI performance. This study proposes a new transfer learning (TL)-based method that exploits a subject\'s pilot data to select auxiliary data from other subjects to enhance the performance of an EEG-based BCI for drowsiness detection. This method is based on our previous findings that the EEG correlates of drowsiness were stable within individuals across sessions and an individual\'s pilot data could be used as calibration/training data to build a robust drowsiness detector. Empirical results of this study suggested that the feasibility of leveraging existing BCI models built by other subjects\' data and a relatively small amount of subject-specific pilot data to develop a BCI that can outperform the BCI based solely on the pilot data of the subject.