• Journal of the Korean Society for Library and Information Science
• /
• v.56 no.1
• /
• pp.95-117
• /
• 2022

This study developed and evaluated audio-visual (AV) semantics-based video summarization methods using eye tracking and electroencephalography (EEG) data. For this study, twenty-seven university students participated in eye tracking and EEG experiments. The evaluation results showed that the average recall rate (0.73) of using both EEG and pupil diameter data for the construction of a video summary was higher than that (0.50) of using EEG data or that (0.68) of using pupil diameter data. In addition, this study reported that the reasons why the average recall (0.57) of the AV semantics-based personalized video summaries was lower than that (0.69) of the AV semantics-based generic video summaries. The differences and characteristics between the AV semantics-based video summarization methods and the text semantics-based video summarization methods were compared and analyzed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.11a
• /
• pp.7-10
• /
• 2007

많은 과학자들은 인간의 사고를 functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG)등을 이용해서 두뇌 활동 영역을 연구하고 있다. 주로 의학 분야와 심리학의 영역에서 두뇌의 활동을 연구하여 간질이나 발작을 알아내고 거짓말 탐지 분야에서도 사용된다. 본 논문에서는 사람의 두뇌활동을 측정하여 인간의 감정을 인식하는 연구에 중점을 두었다. 특히 fMRI와 TRS 그리고 EEG를 이용해서 사람의 두뇌활동을 측정하는 연구를 하였다. 많은 연구자들이 한 가지 측정 장치만을 사용하여서 측정하거나 fMRI와 EEG를 동시에 측정하는 연구를 진행하고 있다. 현재에는 단순히 두뇌의 활동을 측정하거나 측정시 발생하는 잡음들을 제거하는 연구들에 중점을 두고 진행되고 있다. 본 연구에서는 fMRI와 TRS를 동시에 측정하여 얻은 두뇌 활동 데이터를 가지고 감정에 따른 활동영역의 EEG신호를 측정하였다. EEG 신호분석에 있어서 기존의 뇌파만을 가지고 특정을 찾아내는 것을 넘어서 각각의 채널에서 기록되는 뇌파의 파형을 주파수에 따라서 분류하고 정확한 측정을 위해 낮은 주파수를 제거하고 연구자가 필요한 부분의 뇌파를 분석하였다.

• Science of Emotion and Sensibility
• /
• v.2 no.1
• /
• pp.137-145
• /
• 1999

열 두 명의 피험자가 안정상태 일 때, 백색 소음에 노출되었을 때, 백색 소음 하에서 기억과제에 주의를 기울일 때, 백색 소음 하에서 기억 검사를 받을 때, 기록된 뇌파에 대해 relative power spectrum 분석을 하였다. 뇌파는 전두, 측두, 후두 영역에서 단극 유도법으로 기록되었다. 분석 결과, 백색 소음에만 노출되었을 때나, 백색 소음 하에서 기억과제에 주의를 기울일 때나 비슷한 전기피질(electrocortical) 반응이 나타났다. 즉, delta power의 증가, 알파 blocking, fast beta power의 증가, 스트레스를 일으킨다고 피험자들이 평정한 배색 소음 하에서 기억검사를 받을 때에도 동일한 뇌파 패턴이 나타났지만 그 크기가 유의하게 컸다. 정보를 지각할 때 전형적으로 나타나는 반응을 유발하는 스트레스원에 수동적으로 노출되었을 때("intaki"상황)의 생리 반응과 스트레스 상황에 적극적으로 대처할 때("rejection")의 생리 반응을 구분하는 이론 틀 아래서 데이터를 해석하였다. 스트레스 후 기간에 대부분의 뇌파 변수들이 기저선 수준으로 회복된 것으로 보아 사용한 스트레스 유발 모델은 단기적 스트레스 반응만을 유발한 것으로 보인다. 이는, 더 장기적으로 지속되는 스트레스원을 사용하게 되면, tonic상태의 전기피질 반응이 나타날 것을 시사한다.기피질 반응이 나타날 것을 시사한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.11a
• /
• pp.1079-1082
• /
• 2017

최근에 각종 생체전위를 활용한 연구가 활발히 이루어 지고 있다 학습효과를 위해서 집중력 향상에 대한 연구는 교육분야에서 중요한 과제이다. 따라서 뇌파(eletroencephalogram)를 활용한 집중력 향상에 필요한 소프트웨어적 연구는 가치가 있는 연구로서 판단할 수 있다. 따라서 금번 연구에서는 뇌파와 컴퓨터 간 신호처리에 중점을 두고 효율적으로 데이터 프로세스를 할 수 있는 게임이라는 매체를 활용한 학습효과 증진 프로그램을 개발하고자 한다. 금번 연구에서는 학습에 필요한 인터페이스 프로그램과 뇌파 수집에 필요한 뇌파수집기를 중점으로 개발하였다.

• The KIPS Transactions:PartB
• /
• v.16B no.1
• /
• pp.63-70
• /
• 2009

BCI technology is to control communication systems or machines by brain signal among biological signals followed by signal processing. For the implementation of BCI systems, it is required that the characteristics of brain signal are learned and analyzed in real-time and the learned characteristics are applied. In this paper, we detect feature vector of EEG signal on left and right hand movements based on incremental approach and dimension reduction using the detected feature vector. In addition, we show that the reduced dimension can improve the classification performance by removing unnecessary features. The processed data including sufficient features of input data can reduce the time of processing and boost performance of classification by removing unwanted features. Our experiments using K-NN classifier show the proposed approach 5% outperforms the PCA based dimension reduction.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.11
• /
• pp.166-175
• /
• 2017

This study examined children from two families exposed to domestic violence and had psychological counseling in July 2017 at KOVA, a support organization for crime victims. The subjects were exposed to family violence in excess of 10 years and was protected by the shelter with their mothers who had filed complaints with the local police. Victims of domestic violence often face difficulty in avoiding the source of aggression, and thus experience repetitive attacks. This research was conducted at the Buddhism Brain Research Facility, Seoul University, to identify and quantify the emotional characteristics of the affected children in which it is difficult to escape from their living conditions. Data was collected by BrainMaster, a 19-channel examination kit, and analyzed by NeuroGuide. As a result of analyzing the emotional characteristics of the affected children through Quantitative EEG and brain topographical map, we found an increase of slow wave and problems with abnormality of Alpha, High Beta in the left and right Frontal area asymmetry.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38C no.7
• /
• pp.623-629
• /
• 2013

This paper presents the implementation result of the EEG(electroencephalogram) signal transmission protocol and its test platform. EEG measured by a dry-type electrode is directly converted into digital signal by ADC(analog-to-digital converter). Thereafter it is transferred DSP(digital signal processor) platform by $I^2C$(inter-integrated circuit) protocol. DSP conducts the pre-processing of EEG and extracts feature vectors of EEG. In this work, we implement the $I^2C$ protocol with 16 channels by using 10 or 12-bit ADC. In the implementation results, the overhead ratio for the 4 bytes data burst transmission measures 2.16 and the total data rates are 345.6 kbps and 414.72 kbps with 10-bit and 12-bit 1 ksps ADC, respectively. Therefore, in order to support a high speed mode of $I^2C$ for 400 kbps, it is required to use 16:1 and $(8:1){\times}2$ ratios for slave:master in 10-bit ADC and 12-bit ADC, respectively.

• Journal of Power System Engineering
• /
• v.14 no.4
• /
• pp.29-36
• /
• 2010

This study was performed with 5 subjects and used three kinds of music and vibroacoustic stimuli wave based upon each kinds of music. Executing music stimulation, vibro tactile and acoustic wave stimulation to human body were performed. Then measured brain waves were analyzed under each condition including before stimulation, stimulation 1, and stimulation 2. Effects by stimulation results could be studied with experiments and summarized results are followings. 1. It may be concluded that effects on brain waves by music and vibroacoustic stimulation might differ under different situations such as stimulation types with vibroacoustic equipment, human body and mental conditions when measuring, etc.. 2. During stimuli by using music A, B, and C, the effect of $\alpha$ wave, $\beta$ wave, and SMR wave power values show same tendency to the subject c but music C had very different tendency during vibroacoustic stimuli. 3. During vibroacoustic stimuli by applying the signals of music C, because SMR wave power value was continually increased with consistency comparing to Bst, this can be estimated that an application of inducing mind concentration condition would be possible under relaxed body and mind conditions. 4. To secure data significance, all measured data need to be tested statistically whether data would be interrelated or not.

• Journal of Digital Convergence
• /
• v.19 no.10
• /
• pp.295-301
• /
• 2021

Individual mental fatigue not only reduces cognitive ability and work performance, but also becomes a major factor in large and small accidents occurring in daily life. In this paper, a CNN model for EEG-based mental fatigue discrimination was proposed. To this end, EEG in the resting state and task state were collected and applied to the proposed CNN model, and then the model performance was analyzed. All subjects who participated in the experiment were right-handed male students attending university, with and average age of 25.5 years. Spectral analysis was performed on the measured EEG in each state, and the performance of the CNN model was compared and analyzed using the raw EEG, absolute power, and relative power as input data of the CNN model. As a result, the relative power of the occipital lobe position in the alpha band showed the best performance. The model accuracy is 85.6% for training data, 78.5% for validation, and 95.7% for test data. The proposed model can be applied to the development of an automated system for mental fatigue detection.

• Journal of Internet of Things and Convergence
• /
• v.9 no.6
• /
• pp.37-44
• /
• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.