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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
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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.
New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability
(2016-11-01)
Yu, Yi-Hsin
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Chen, Shih-Hsun
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Chang, Che-Lun
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Lin, Chin-Teng
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Hairston, W. David
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 identified some weak points in the sensors' construction, including particle pull-out and ablation of the silver coating on the silica filler. The newly-developed sensor materials achieved significant improvement in EEG measurements while maintaining the advantages of previous silicone-based electrodes, including flexibility and non-toxicity. The experimental results indicated that the proposed electrodes maintained suitable performance even after exposure to temperature fluctuations, 85% relative humidity, and enhanced corrosion conditions demonstrating improvements in the environmental stability. Fabricated flat (forehead) and acicular (hairy sites) electrodes composed of the optimum identified formulation exhibited low impedance and reliable EEG measurement; some initial human experiments demonstrate the feasibility of using these silicone-based electrodes for typical lab data collection applications.
A Novel 16-Channel Wireless System for Electroencephalography Measurements With Dry Spring-Loaded Sensors
(2014-06-01)
Liao, Lun-De
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Wu, Shang-Lin
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Liou, Chang-Hong
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Lu, Shao-Wei
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Chen, Shi-An
Understanding brain function using electroencephalography (EEG) is an important issue for cerebral nervous system diseases, especially for epilepsy and Alzheimer\'s disease. Many EEG measurement systems are used reliably to study these diseases, but their bulky size and the use of wet sensors make them uncomfortable and inconvenient for users. To overcome the limitations of conventional EEG measurement systems, a wireless and wearable multichannel EEG measurement system is proposed in this paper. This system includes a wireless data acquisition device, dry spring-loaded sensors, and a size-adjustable soft cap. We compared the performance of the proposed system using dry versus conventional wet sensors. A significant positive correlation between readings from wet and dry sensors was achieved, thus demonstrating the performance of the system. Moreover, four different features of EEG signals (i.e., normal, eye-blinking, closed-eyes, and teeth-clenching signals) were measured by 16 dry sensors to ensure that they could be detected in real-life cognitive neuroscience applications. Thus, we have shown that it is possible to reliably measure EEG signals using the proposed system. This paper presents novel insights into the field of cognitive neuroscience, showing the possibility of studying brain function under real-life conditions.
EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment
(2013-10-01)
Lin, Chin-Teng
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Tsai, Shu-Fang
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Ko, Li-Wei
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生物資訊及系統生物研究所
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資訊工程學系
Motion sickness is a common experience for many people. Several previous researches indicated that motion sickness has a negative effect on driving performance and sometimes leads to serious traffic accidents because of a decline in a person's ability to maintain self-control. This safety issue has motivated us to find a way to prevent vehicle accidents. Our target was to determine a set of valid motion sickness indicators that would predict the occurrence of a person's motion sickness as soon as possible. A successful method for the early detection of motion sickness will help us to construct a cognitive monitoring system. Such a monitoring system can alert people before they become sick and prevent them from being distracted by various motion sickness symptoms while driving or riding in a car. In our past researches, we investigated the physiological changes that occur during the transition of a passenger's cognitive state using electroencephalography (EEG) power spectrum analysis, and we found that the EEG power responses in the left and right motors, parietal, lateral occipital, and occipital midline brain areas were more highly correlated to subjective sickness levels than other brain areas. In this paper, we propose the use of a self-organizing neural fuzzy inference network (SONFIN) to estimate a driver's/passenger's sickness level based on EEG features that have been extracted online from five motion sickness-related brain areas, while either in real or virtual vehicle environments. The results show that our proposed learning system is capable of extracting a set of valid motion sickness indicators that originated from EEG dynamics, and through SONFIN, a neuro-fuzzy prediction model, we successfully translated the set of motion sickness indicators into motion sickness levels. The overall performance of this proposed EEG-based learning system can achieve an average prediction accuracy of similar to 82%.
Design, Fabrication and Experimental Validation of a Novel Dry-Contact Sensor for Measuring Electroencephalography Signals without Skin Preparation
(2011-06-01)
Liao, Lun-De
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Wang, I-Jan
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Chen, Sheng-Fu
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Chang, Jyh-Yeong
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Lin, Chin-Teng
In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain low skin-sensor interface impedance. Additionally, the flexible substrate also initiates a sensor buffer effect, eliminating pain when force is applied. The proposed dry EEG sensor was reliable in measuring EEG signals without any skin preparation or conductive gel usage, as compared with the conventional wet electrodes.
Noninvasive neural prostheses using mobile and wireless EEG
(2008-07-01)
Lin, Chin-Teng
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Ko, Li-Wei
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Chiou, Jin-Chern
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Duann, Jeng-Ren
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Huang, Ruey-Song
Neural prosthetic technologies have helped many patients by restoring vision, hearing, or movement and relieving chronic pain or neurological disorders. While most neural prosthetic systems to date have used invasive or implantable devices for patients with inoperative or malfunctioning external body parts or internal organs, a much larger population of "healthy", people who suffer episodic or progressive cognitive impairments in daily life can benefit from noninvasive neural prostheses. For example, reduced alertness, lack of attention, or poor decision-making during monotonous, routine tasks can have catastrophic consequences. This study proposes a noninvasive mobile prosthetic platform for continuously monitoring high-temporal resolution brain dynamics without requiring application of conductive gels on the scalp. The proposed system features dry micro-electromechanical system electroencephalography sensors, low-power signal acquisition, amplification and digitization, wireless telemetry, online artifact cancellation, and signal processing. its implications for neural prostheses are examined in two sample studies: 1) cognitive-state monitoring of participants performing realistic driving tasks in the virtual-reality-based dynamic driving simulator and 2) the neural correlates of motion sickness in driving. The experimental results of these studies provide new insights into the under standing of complex brain functions of participants actively performing ordinary tasks in natural body positions and situations within real operational environments.

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