• The Journal of the Korea Contents Association
• /
• v.5 no.1
• /
• pp.19-26
• /
• 2005

This paper introduces a new design method of realizing the machine control interface by using bio-signals(EEG/EOG). This method can be further expanded to be applied to the computer system responding to EEG or EOG signals and the general bio-feedback system. For this reason, we made the remotely controlled toy system controlled by the EEG spectrums, their combination indexes, and EOG parameters. And the headset that has bio-signal processing modules built-in offers convenience for users, and this make much more advanced system than any other existing BCI and BMI system.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.343-350
• /
• 2024

Mobile electroencephalography (EEG) can continuously and objectively monitor construction workers' psychological stress, thereby contributing to enhanced safety and health. Traditional EEG-based stress assessment techniques utilize headset-type devices that cover the scalp, including the frontal area, which is the most relevant brain part to stress. Yet, the invasiveness of such devices may pose a potential barrier to their field application. In response, ear-EEG technology presents a less intrusive alternative for continuous monitoring, potentially overcoming the limitations of scalp-EEG. The temporal regions monitored by ear-EEG hold anatomical and functional significance in the brain's response to stress, suggesting that ear-EEG could effectively detect stress. Despite its advantage, the effectiveness of ear-EEG in stress detection remains underexplored, largely due to the existing literature's focus on frontal brain regions. To address this gap, the authors aim to evaluate ear-EEG's effectiveness in measuring stress and compare it to high-density scalp-EEG. EEG signals were collected with ear- and scalp-EEGs from 10 subjects in a controlled laboratory while they performed the mental arithmetic tasks under time pressure and socio-evaluative threats to induce stress at different levels (high vs. low). Subsequently, the authors performed t-tests and point-biserial analysis to analyze differences between high and low-stress conditions in the most reliable stress biomarkers in literature: high-beta power in temporal regions for ear-EEG, and alpha asymmetry in frontal regions for scalp-EEG. The results indicate that both EEG techniques could effectively differentiate between stress levels, with statistical significance (p <0.001 for both) and moderate effect size. Furthermore, the results demonstrate ear-EEG's comparable effectiveness to scalp-EEG in detecting stress-induced brain activity given the comparable statistical metrics, such as p-value and effect size. This study provides a groundwork for further explorations into leveraging ear-EEG as a practical tool for the early detection of stress, aiming to enhance stress management strategies within the construction industry.

• Annual Conference of KIPS
• /
• 2014.11a
• /
• pp.1139-1141
• /
• 2014

뇌파 수집과 분석을 위한 상용 장비인 모바일 헤드셋 Emotiv를 이용한 BCI 연구가 있었다. 특히 Emotiv에서 제공되는 학습기능을 사용한 사례들에서 다양한 패턴을 학습한 경우는 인식률이 떨어지고 학습하는데 많은 시간이 소비된다. 본 논문에서는 Emotiv의 학습기능을 한 가지만 사용해서 인식률을 높이고 자이로센서를 이용하여 로봇을 4가지 방향으로 제어해서 원하는 경로로 이동가능 한 기능을 구현했다. 구현한 결과는 평균 85.67%를 보여 성공적이었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.813-815
• /
• 2016

This paper implements a device control system based on the brain wave data. Brain-Computer Interface (BCI) technology can pass directly to the system without going through the operation of the language or body. By controlling the device to detect brain waves in real time according to the change of status it helps to ease life for a variety of services, such as disabled people with limited mobility or students, people who need multi-tasking. In addition, it is possible to develop an application service such as the home device control system. A device control system implemented in the paper based on the data collected from the EEG Headset associated to control the power of the smart phone and audio. Control the power ON / OFF operation by the Attention, and support service functions to control the audio by the Meditation and Eye blink. It was confirmed that the device control using the brain wave data to be operated through a laboratory test successfully.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.1049-1056
• /
• 2024

Human-robot collaboration (HRC) is an emerging form of work anticipated to improve construction productivity by integrating robotic capabilities with human expertise. With the expected transition towards tasks that demand more cognitive efforts for human workers, considering the cognitive status of each co-worker, such as task engagement and vigilance, can become crucial to achieve high-quality human performance during HRC, potentially contributing to a more productive HRC in construction. However, the potential cognitive changes of each co-worker have remained unclear during HRC, as studies have primarily focused on identifying general trends from aggregated cognitive responses of people, in which an individual's response can be overlooked. In this study, we examine the cognitive response of each co-worker during HRC for a construction task. We observed the cognitive responses of 18 people while they were experiencing different collaborating conditions, such as the robot's different movement speed, during a bricklaying task with an arm-type collaborative robot. For each participant, we analyzed electroencephalogram (EEG) signals to identify the changes in cognitive status by using a wearable EEG headset. The results present that the cognitive responses of almost all the participants were significantly and differently affected during HRC, impacting the estimated productivity of their human-robot teams. The findings of the study present the importance of considering each co-worker's potentially unique cognitive response as a way to achieve cognitive wellbeing while pursuing high productivity within human-robot teams, potentially contributing to overall productive HRC in construction.

• Journal of Advanced Navigation Technology
• /
• v.22 no.5
• /
• pp.472-477
• /
• 2018

In this paper, we develop interactive content services for preventing depression of users through an intelligent Internet of Things(IoT) mirror system. For interactive content services, an IoT mirror device measures attention and meditation data from an EEG headset device and also measures facial expression data such as "sad", "angery", "disgust", "neutral", " happy", and "surprise" classified by a multi-layer perceptron algorithm through an webcam. Then, it sends the measured data to an oneM2M-compliant IoT server. Based on the collected data in the IoT server, a machine learning model is built to classify three levels of depression (RED, YELLOW, and GREEN) given by a proposed merge labeling method. It was verified that the k-nearest neighbor (k-NN) model could achieve about 93% of accuracy by experimental results. In addition, according to the classified level, a social network service agent sent a corresponding alert message to the family, friends and social workers. Thus, we were able to provide an interactive content service between users and caregivers.