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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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    Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task
    (2020-06-01)
    Ko, Li-Wei
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    Komarov, Oleksii
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    Lai, Wei-Kai
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    Liang, Wei-Gang
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    Jung, Tzyy-Ping
    Objective. A passive brain-computer interface recognizes its operator's cognitive state without an explicitly performed control task. This technique is commonly used in conjunction with consumer-grade EEG devices for detecting the conditions of fatigue, attention, emotional arousal, or motion sickness. While it is easy to mount the sensors in the forehead area, which is not covered with hair, the recorded signals become greatly contaminated with eyeblink and movement artifacts, which makes it a challenge to acquire the data of suitable for analysis quality, particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms. In this article, we demonstrate that by combining the features associated with electrocortical activities and eyeblink recognition analysis, it becomes feasible to design an accurate system for the inattention state prediction using just a single EEG sensor. Approach. Fifteen healthy 22-28 years old participants took part in the experiment that implemented a realistic sustained attention task of nighttime highway driving in a virtual environment. The EEG data were collected using a portable wireless Mindo-4 device, which constitutes an adjustable elastic strip with foam-based sensors, a data-acquisition module, an amplification and digitizing unit, and a Bluetooth (R) module. Main results. The spectral analysis of the EEG samples that immediately preceded the lane departure events revealed alterations in the tonic power spectral density, which accompanied elongations in the drivers' reaction times. The RMSE of the predicted reaction times, which are based on a combination of the brain-related and eyeblink features, is 0.034 +/- 0.019 s, and the r(2) is 0.885 +/- 0.057 according to a within-session leave-one-trial-out cross-validation. Significance. The drowsiness prediction from a frontal single-channel setup can achieve a comparable performance with using an array of occipital EEG sensors. As a direct result of utilizing a dry sensor placed in the non-covered with hair head area, the proposed approach in this study is low-cost and user-friendly.
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    Design of a Mobile Brain Computer Interface-Based Smart Multimedia Controller
    (2015-03-01)
    Tseng, Kevin C.
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    Lin, Bor-Shing
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    Wong, Alice May-Kuen
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    Lin, Bor-Shyh
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    影像與生醫光電研究所
    Music is a way of expressing our feelings and emotions. Suitable music can positively affect people. However, current multimedia control methods, such as manual selection or automatic random mechanisms, which are now applied broadly in MP3 and CD players, cannot adaptively select suitable music according to the user's physiological state. In this study, a brain computer interface-based smart multimedia controller was proposed to select music in different situations according to the user's physiological state. Here, a commercial mobile tablet was used as the multimedia platform, and a wireless multi-channel electroencephalograph (EEG) acquisition module was designed for real-time EEG monitoring. A smart multimedia control program built in the multimedia platform was developed to analyze the user's EEG feature and select music according his/her state. The relationship between the user's state and music sorted by listener's preference was also examined in this study. The experimental results show that real-time music biofeedback according a user's EEG feature may positively improve the user's attention state.
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    Beamformer Source Estimation Improves Accuracy of Motor-Imagery-based Brain Computer Interface
    (2018-01-01)
    Chan, Hui-Ling
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    Wang, Jung-Wei
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    Chen, Yong-Sheng
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    交大名義發表
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    分子醫學與生物工程研究所
    Motor-imagery-based brain computer interface has advantages in high information transition rate and has potential to achieve high portability without using a display for visual stimulation. However, electroencephalographic data is a mixture of neural activity from various brain locations and also external artifacts. This paper presents a novel method utilizing source level features computed based on maximum contrast beamformer. The estimated source activity has the maximum contrast between the power during the reference period and the period that event-related synchronization or desynchronization emerges. Moreover, this method utilizes the classification structure based on divide-and-conquer concept to identify four classes of motor imageries, including left hand, right hand, tongue, and foot. The experimental results demonstrate the improvement in classification accuracy by using the proposed method.
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    Design of Wearable Brain Computer Interface Based on Motor Imagery
    (2014-01-01)
    Lin, Chuan-Lung
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    Chu, Tso-Yao
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    Wu, Pei-Jung
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    Wang, Chen-An
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    Lin, Bor-Shyh
    Brain computer interface (BCI) is a communication system to establish a communication interface between the brain and the device. Motor imagery-based brain computer interface is one of asynchronous BCIs, and have been widely developed in recent years. However, most of motor imagery-based BCI require bulk EEG machines, and lots of EEG data have to be transmitted to the back-end computer for analysis. This also causes the limitation of daily applications. In this study, a wearable motor-imagery-based BCI system with the least number of EEG channels was developed. In this system, the wearable mechanical design and the wireless EEG acquisition module were designed for measuring real time EEG in daily life. The modified BCI algorithm was also developed, and here only three channels are used to distinguish different events of motor imagery in this study. Finally, the proposed BCI system was also validated by the hand-motor imagery experiment.
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    Wearable and Wireless Brain-Computer Interface and Its Applications
    (2009)
    Lin, Chin-Teng
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    Ko, Li-Wei
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    Chang, Che-Jui
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    Wang, Yu-Te
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    Chung, Chia-Hsin
    This study extends our previous work on mobile & wireless EEG acquisition to a truly wearable and wireless human-machine interface, NCTU Brain-Computer-Interface-headband,(BCI-headband), featuring: (1) dry Micro-Electro-Mechanical System (MEMS) EEG electrodes with 400 ganged contacts for acquiring signals from non-hairy sites without use Of gel or skin preparation; (2) a miniature data acquisition circuitry, (3) wireless telemetry and (4) online signal processing on a commercially available cell phone or a lightweight, wearable digital signal processing module. The applicability of the NCTU BCI headband to EEG monitoring in real-world environments was demonstrated in a sample study: cognitive-state monitoring and management of participants performing normal tasks.