Browsing by Author "Chuang, Ya-Ting"

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EEG-based Motion Sickness Classification system with Genetic Feature Selection
(2013)
Ko, Li-Wei
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Lee, Hua-Chin
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Tsai, Shu-Fang
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Shih, Tsung-Chin
;
Chuang, Ya-Ting
People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Many previous studies indicated that motion sickness sometimes led to traffic accidents, so it becomes an important issue in our daily life. In this study, we designed a VIZ-based motion-sickness platform with a 32-channel EEG system and a joystick which is used to report the motion sickness level (MSL) in real time during experiments. The results show it is feasible to estimate subject's MSL based on re-sampling frequency band proved by the high test accuracy. A comparison between general prediction models (such as LDA, QDA, KNN) and IBCGA shows that the IBCGA can be effectively increase the accuracy. In this paper, an extended-IBCGA (e-IBCGA) is proposed and it provides more accuracy than the prior-art research. The test results show that e-IBCGA increases at least 10% to 20% test accuracy in 6 subjects.
Neural Mechanisms of Inhibitory Response in a Battlefield Scenario: A Simultaneous fMRI-EEG Study
(2016-05-02)
Ko, Li-Wei
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Shih, Yi-Cheng
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Chikara, Rupesh Kumar
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Chuang, Ya-Ting
;
Chang, Erik C.
The stop-signal paradigm has been widely adopted as a way to parametrically quantify the response inhibition process. To evaluate inhibitory function in realistic environmental settings, the current study compared stop-signal responses in two different scenarios: one uses simple visual symbols as go and stop signals, and the other translates the typical design into a battlefield scenario (BFS) where a sniper-scope view was the background, a terrorist image was the go signal, a hostage image was the stop signal, and the task instructions were to shoot at terrorists only when hostages were not present but to refrain from shooting if hostages appeared. The BFS created a threatening environment and allowed the evaluation of how participants' inhibitory control manifest in this realistic stop-signal task. In order to investigate the participants' brain activities with both high spatial and temporal resolution, simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings were acquired. The results demonstrated that both scenarios induced increased activity in the right inferior frontal gyrus (rIFG) and presupplementary motor area (preSMA), which have been linked to response inhibition. Notably, in right temporoparietal junction (rTPJ) we found both higher blood-oxygen-level dependent (BOLD) activation and synchronization of theta-alpha activities (4-12 Hz) in the BFS than in the traditional scenario after the stop signal. The higher activation of rTPJ in the BFS may be related to morality judgments or attentional reorienting. These results provided new insights into the complex brain networks involved in inhibitory control within naturalistic environments.
Statistical Analysis and Classification of EEG-based Attention Network Task Using Optimized Feature Selection
(2014)
Lee, Hua-Chin
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Ko, Li-Wei
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Huang, Hui-Ling
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Wu, Jui-Yun
;
Chuang, Ya-Ting
This research incorporates optimized feature selection using an inheritable bi-objective combinatorial genetic algorithm (IBCGA) and mathematic modeling for classification and analysis of electroencephalography (EEG) based attention network. It consists of two parts. 1) We first design the attention network experiments, record the EEG signals of subjects from NeuronScan instrument, and filter noise from the EEG data. We use alerting scores, orienting scores, and conflict scores to serve as the efficiency evaluation of the attention network. 2) Based on an intelligent evolutionary algorithm as the core technique, we analyze the large-scale EEG data, identify a set of important frequency-channel factors, and establish mathematical models for within-subject, across-subject and leave-one-subject-out evaluation using a global optimization approach. The results of using 10 subjects show that the average classification accuracy of independent test in the within-subject case is 86.51%, the accuracy of the across-subject case is 68.44%, and the accuracy of the leave-one-subject-out case is 54.33%