• Journal of the Korean Society of Radiology
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• v.17 no.7
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• pp.1179-1187
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• 2023

Recent advancements in modern transportation have led to the active development of various biomedical signal and medical imaging technologies. Particularly, in the field of cognitive/neuroscience, the importance of electroencephalography (EEG) measurement and the development of accurate EEG measurement technology in moving vehicles represent a challenging area. This study aims to extensively investigate and analyze the trends in technology research utilizing EEG during driving. For this purpose, the Scopus database was used to explore EEG-related research conducted since the year 2000, resulting in the selection of about 40 papers. This paper sheds light on the current trends and future directions in signal processing technology, EEG measurement device development, and in-vehicle driver state monitoring technology. Additionally, a ultra compact 32-channel EEG measurement module was designed. By implementing it simply and measuring and analyzing EEG signals, in-vehicle EEG module's functionality was checked. This research anticipates that the technology for measuring and analyzing biometric signals during driving will contribute to driver care and health monitoring in the era of autonomous vehicles.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.1
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• pp.443-464
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• 2015

This study examined the feasibility of using EEG signals and ERP P3b for extracting video key-frames based on users' cognitive responses. Twenty participants were used to collect EEG signals. This research found that the average amplitude of right parietal lobe is higher than that of left parietal lobe when relevant images were shown to participants; there is a significant difference between the average amplitudes of both parietal lobes. On the other hand, the average amplitude of left parietal lobe in the case of non-relevant images is lower than that in the case of relevant images. Moreover, there is no significant difference between the average amplitudes of both parietal lobes in the case of non-relevant images. Additionally, the latency of MGFP1 and channel coherence can be also used as criteria to extract key-frames.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.1001-1004
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• 2014

IT 기술과 의료 기술이 발전함에 따라 뇌파를 이용한 많은 연구들이 진행되고 있다. 컴퓨터로 사용자가 뇌파를 측정하고, 측정된 뇌파를 실시간으로 모니터링 할 수 있는 고속 데이터 처리 알고리즘을 소개하고, 측정된 뇌파를 통하여 우울증을 진단할 수 있는 시스템을 구현하였다. 특히 실시간 뇌파지표 분석을 통하여 뇌파의 기본파형이 분류되고, 분류된 신호에서 개발된 알고리즘에 따라 주의/이완/집중/우울의 4가지 지표가 실시간으로 도출된다.

• Review of KIISC
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• v.27 no.1
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• pp.39-46
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• 2017

본 논문에서는 생체인식 인증의 한 가지 방법인 뇌파 기반 사용자 인증기술의 최신 기술동향에 대해 고찰하고 해당기술의 실용화를 위해 해결해야 할 기술적 문제점과 요구사항에 대해 분석한다. 뇌파 기반 사용자 인증기술은 최근에 스마트폰, 금융 등 다양한 분야에서 사용되고 있는 기존의 생체인식 인증기술과 비교해볼 때 가변성, 유출 저항성 등의 장점이 있지만, 사용자들로부터 뇌파를 수집하기 위해 필요한 장비의 경제성, 뇌파 수집 행위의 사용자 편의성, 현재까지 발표된 뇌파 기반 사용자 식별 기법들의 안정성 등이 개선되어야 하는 것으로 파악된다. 이와 관련하여 뇌파 측정 장비들의 발전 동향을 살펴보고 해당 장비들의 간소화와 인증정확도 간 트레이드오프(trade-off)와 최신 기계학습 및 인공지능 기술들을 활용한 뇌파 기반 사용자 식별 기법들의 안정성을 위해 해결되어야 할 뇌파의 시간차 문제 및 이에 따른 인증정확도 저하 문제를 규명하고 분석한다.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.377-396
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• 2015

This study proposed a key-frame automatic extraction method for video storyboard surrogates based on users' cognitive responses, EEG signals and discriminant analysis. Using twenty participants, we examined which ERP pattern is suitable for each step, assuming that there are five image recognition and process steps (stimuli attention, stimuli perception, memory retrieval, stimuli/memory comparison, relevance judgement). As a result, we found that each step has a suitable ERP pattern, such as N100, P200, N400, P3b, and P600. Moreover, we also found that the peak amplitude of left parietal lobe (P7) and the latency of FP2 are important variables in distinguishing among relevant, partial, and non-relevant frames. Using these variables, we conducted a discriminant analysis to classify between relevant and non-relevant frames.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.3
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• pp.361-381
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• 2016

This study proposed two topical relevance models, simple and complex models, using EEG/ERP techniques. In the simple model regarding simple search tasks, N300 and P3b components are used. The N300 is specific to the semantic processing of pictures and the P3b reflects mechanisms involved in the decision about whether an external stimulus matches or does not match an internal representation of a specific category. In the complex model regarding complex search tasks, on the other hand, N400 and P600 components are used. The N400 reflects activation of an amodel system that integrates both image-based and conceptual representations into a context, whereas the P600 is related to complex cognitive processes. Our research results can be used as a source to design an EEG-based interactive multimedia system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.383-395
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• 2017

Brainwave-based user authentication technology has advantages such as changeability, shoulder-surfing resistance, and etc. comparing with conventional biometric authentications, fingerprint recognition for instance which are widely used for smart phone and finance user authentication. Despite these advantages, brainwave-based authentication technology has not been used in practice because of the price for EEG (electroencephalography) collecting devices and inconvenience to use those devices. However, according to the development of simple and convenient EEG collecting devices which are portable and communicative by the recent advances in hardware technology, relevant researches have been actively performed. However, according to the experiment based on EEG samples collected by using a single-channel EEG measurement device which is the most simplified one, the authentication accuracy decreases as the number of channels to measure and collect EEG decreases. Therefore, in this paper, we analyze technical problems that need to be solved for practical use of brainwave-based use authentication and propose an incremental elimination method of collected EEG samples for each user to consist a set of EEG samples which are effective to authentication users.

• Journal of IKEEE
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• v.18 no.3
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• pp.368-375
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• 2014

Brain computer interface(BCI) technology has been continuously developed due to the continuous development of interface technology and the promotion of brain wave research. In this paper, we propose a driving habit management system by adopting BCI to transportation. The proposed system consists of the electroencephalogram(EEG) measuring unit, the EEG analysis unit, the memory section for storing the state information of drivers, the speed controller unit and the alarming section for generating warnings. Our proposed system can reduce the drowsy driving, improve the driving habits of users and help to prevent traffic accidents.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.525-530
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• 2019

Electroencephalography(EEG) studies that have been in clinical research since the discovery of brainwave have recently been developed into brain-computer interface studies. Currently, research is underway to manipulate robot arms and drones by analyzing brainwave. However, resolution and reliability of EEG information is still limited. Therefore, it is required to develop various technologies necessary for measuring and interpreting brainwave more accurately. Pioneering new applications with these technologies is also important. In this paper, we propose development of a personal authentication system fit for lifecaretainment based on EEG. The proposed system guarantees the resolution and reliability of EEG information by using the Electrooculogram and Electromyogram(EMG) together with EEG.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.15-16
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• 2014

본 논문은 음악 치료 전문가들로부터 수집한 음악 치료 프로그램에 관한 지식과 규칙을 수집하여 구성된 전문가 시스템을 도입하여 자동으로 우울증 환자를 위한 추천 음악 치료 시스템을 설계하는 것을 목표로 한다. 제안한 시스템은 음악 치료 전문가들로부터 수집한 수많은 음악 치료 프로그램 중 뇌파 측정을 통해 환자에게 가장 효과적인 치료 프로그램을 선별하고 환자에게 제공하여 치료 효과를 극대화하는 것을 목표로 한다. 제안 시스템은 우울증 환자들의 치료를 위해 뇌파 측정을 입력 받아 분석하여 환자의 증상을 완화하고 치료 효과가 가장 좋은 음악 치료 프로그램을 선별하기 위해 인공 지능 기술들인 전문가 시스템(Expert System) 기법에 기반 한 음악 치료 시스템을 설계하고 제안한다.