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Discovery Projects - Grant ID: DP150101645

Title
Discovery Projects - Grant ID: DP150101645
Funding
ARC | Discovery Projects
Contract (GA) number
DP150101645
Start Date
2015/01/01
End Date
2017/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP150101645

 

  • Sustained Attention in Real Classroom Settings: An EEG Study

    Ko, Li-Wei; Komarov, Oleksii; Hairston, W. David; Jung, Tzyy-Ping; Lin, Chin-Teng (2017)
    Projects: ARC | Discovery Projects - Grant ID: DP150101645 (DP150101645)
    Sustained attention is a process that enables the maintenance of response persistence and continuous effort over extended periods of time. Performing attention-related tasks in real life involves the need to ignore a variety of distractions and inhibit attention shifts to irrelevant activities. This study investigates electroencephalography (EEG) spectral changes during a sustained attention task within a real classroom environment. Eighteen healthy students were instructed to recognize as fa...

    Resting-state EEG power and coherence vary between migraine phases

    Cao, Zehong; Lin, Chin-Teng; Chuang, Chun-Hsiang; Lai, Kuan-Lin; Yang, Albert C.; Fuh, Jong-Ling; Wang, Shuu-Jiun (2016)
    Projects: ARC | Discovery Projects - Grant ID: DP150101645 (DP150101645)
    Background: Migraine is characterized by a series of phases (inter-ictal, pre-ictal, ictal, and post-ictal). It is of great interest whether resting-state electroencephalography (EEG) is differentiable between these phases. Methods: We compared resting-state EEG energy intensity and effective connectivity in different migraine phases using EEG power and coherence analyses in patients with migraine without aura as compared with healthy controls (HCs). EEG power and isolated effective coherence...

    New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability

    Yu, Yi-Hsin; Chen, Shih-Hsun; Chang, Che-Lun; Lin, Chin-Teng; Hairston, W. David; Mrozek, Randy A. (2016)
    Projects: ARC | Discovery Projects - Grant ID: DP150101645 (DP150101645)
    This study investigates alternative material compositions for flexible silicone-based dry electroencephalography (EEG) electrodes to improve the performance lifespan while maintaining high-fidelity transmission of EEG signals. Electrode materials were fabricated with varying concentrations of silver-coated silica and silver flakes to evaluate their electrical, mechanical, and EEG transmission performance. Scanning electron microscope (SEM) analysis of the initial electrode development identif...

    EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features

    Wu, Dongrui; Lance, Brent J.; Lawhern, Vernon J.; Gordon, Stephen; Jung, Tzyy-Ping; Lin, Chin-Teng (2017)
    Projects: ARC | Discovery Projects - Grant ID: DP150101645 (DP150101645)
    Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the...

    Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources

    Yu-Ting Liu; Nikhil R. Pal; Amar R. Marathe; Yu-Kai Wang; Chin-Teng Lin (2017)
    Projects: ARC | Discovery Projects - Grant ID: DP150101645 (DP150101645)
    A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a ...

    Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)

    Wu, Dongrui; Lawhern, Vernon J.; Gordon, Stephen; Lance, Brent J.; Lin, Chin-Teng (2017)
    Projects: ARC | Discovery Projects - Grant ID: DP150101645 (DP150101645)
    One big challenge that hinders the transition of brain-computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on cl...

    Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

    Wu, Dongrui; King, Jung-Tai; Chuang, Chun-Hsiang; Lin, Chin-Teng; Jung, Tzyy-Ping (2017)
    Projects: ARC | Discovery Projects - Grant ID: DP150101645 (DP150101645)
    Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noises, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial ...
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