• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.140-147
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• 2021

EEG(electroencephalogram) are measured to accurately determine the level of sleep in various sleep examinations. In general, measurements are more accurate as the number of sensor channels increases. EEG can interfere with sleep by attaching electrodes to the skin when measuring. It is necessary for self sleep care to select the minimum number of EEG channels that take into account both the user's discomfort and the accuracy of the measurement data. In this paper, we proposed a sleep stage analysis model based on machine learning and conducted experiments for using from one channel to four channels. We obtained estimation accuracy for sleep stage as following 82.28% for one channel, 85.77% for two channels, 80.33% for three channels and 68.87% for four channels. Although the measurement location is limited, the results of this study compare the accuracy according to the number of channels and provide information on the selection of channel numbers in the EEG sleep analysis.

• Journal of IKEEE
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• v.22 no.4
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• pp.1175-1179
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• 2018

In this paper, we propose recurrent CNN(Convolutional Neural Networks) for detecting seizures among patients using EEG signals. In the proposed method, data were mapped by image to preserve the spectral characteristics of the EEG signal and the position of the electrode. After the spectral preprocessing, we input it into CNN and extracted the spatial and temporal features without wavelet transform. Results from the Children's Hospital of Boston Massachusetts Institute of Technology (CHB-MIT) dataset showed a sensitivity of 90% and a false positive rate (FPR) of 0.85 per hour.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.482-492
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• 2019

This study began with the assumption that the phonetics-centered interpretation of 100 Chinese characters would enhance thinking ability and comprehension. For this purpose, two experimental groups and a comparative group were recruited from the graduate students from June 3, 2017 to February 22, 2018. The experimental group participated in the phonetics-centered Chinese character lecture for 4 hours per week for 6 weeks for a total of 24 hours. QEEG were measured before and after the phonetics-centered Chinese character lecture. A total of 18 subjects ( nine subjects in the experimental group and nine comparative subjects) were included in the study, and the difference between before and after the QEEG of the experimental and comparative groups was analyzed, respectively. The conclusions drawn from this study are as follows. First, the Chinese character lecture changed brain waves. Second, the LORETA analysis before and after the lecture in the experimental group significantly decreased the delta wave in the brain region (Broadmann 40) associated with the meaning of language and phonology. This study result is meaningful because it shows the significant changes of EEG via the lecture.

• Korean Institute of Interior Design Journal
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• v.25 no.2
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• pp.30-40
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• 2016

This study analyzed the attention concentration tendencies of one(1) subject who showed convergent exploratory acts actively through the gaze-brainwave measurement experiment of cafe space images and our research findings are as follows. First, the areas of interest (AOIs) that the subject gazed visually by paying attention to it and concentrating on it at a cafe space include counter&menu area, sign area, partition area, image wall area, stairs area, and movable furniture area, and built-in furniture area: seven areas in total. Second, conscious gaze frequency appeared the highest in counter&menu area, and conscious gaze appeared more later than in initial times. Third, conscious gaze pattern was divided into the zone that explored various areas dispersely (distributed exploratory zone) and the zone that explored between particular areas concentratedly (intensive exploratory zone). Fourth, as a result of analyzing the brainwave attention concentration, it was found that the attention concentration in prefrontal lobe (Fp1, Fp2) and frontal lobe (F3, F4) rose to a higher level in the zone of 15 to 16 seconds and this time zone was considered to be a zone where gazing at counter&menu area was very active. In addition, the attention concentration appeared higher in the initial zone than in the later zone, among the entire experimental time zones. Finally, as a result of analyzing the changes in activation by brain portion of the SMR wave expressed when maintaining the arousal and attention concentration, it was found that the right prefrontal lobe and the frontal lobe became activated in the time zone when the intensive exploration of "counter&menu area" and "movable furniture${\leftrightarrow}$built-in furniture area" had occurred and the time zone when the intensive exploration of "image wall${\leftrightarrow}$partition area" and "counter&menu${\leftrightarrow}$sign area" had occurred.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1397-1398
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• 2015

본 연구에서는 뇌파를 이용하여 자동차에 HUD를 적용하였을 때, HUD의 위치와 이미지 컬러 변경 시 뇌 활성화 지수(Brain Activity Quotient)의 변화를 관찰하였다. 뇌파데이터는 Fp1, Fp2, O1, O2 총 4채널에서 얻었고, HUD위치는 좌측, 중앙, 우측으로 구성하였고 컬러는 초록, 주황, 빨강 총 3가지 색상을 사용하였다. HUD이미지의 크기는 $8{\times}4.5cm^2$로 구성하였다. 뇌 활성화 지수는 상대파워분석(Relative power analysis)를 사용하여 Slow beta wave(13~20 Hz)/alpha wave(4~7.99 Hz)로 계산하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.821-823
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• 2016

본 논문은 다차원 정서모델 기반 영상, 음성, 뇌파를 이용한 멀티모달 복합 감정인식 시스템을 제안한다. 사용자의 얼굴 영상, 목소리 및 뇌파를 기반으로 각각 추출된 특징을 심리학 및 인지과학 분야에서 인간의 감정을 구성하는 정서적 감응요소로 알려진 다차원 정서모델(Arousal, Valence, Dominance)에 대한 명시적 감응 정도 데이터로 대응하여 스코어링(Scoring)을 수행한다. 이후, 스코어링을 통해 나온 결과 값을 이용하여 다차원으로 구성되는 3차원 감정 모델에 매핑하여 인간의 감정(단일감정, 복합감정)뿐만 아니라 감정의 세기까지 인식한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1242-1244
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• 2021

한국교통안전공단이 발표한 자료에 따르면 교통사고로 사망한 원인의 70%가 졸음운전이다. 최근에는 졸음운전을 예방하기 위해 눈 깜박임 인식 등의 운전자의 생체 데이터를 활용한 방법들이 대두되고 있다. 특히 운전자의 졸음운전 판단 기술로 뇌파를 이용하는 연구가 활발히 진행되고 있다. 본 논문에서는 뇌파를 사용하여 효과적으로 졸음 상태를 판단할 수 있는 딥러닝 알고리즘을 제안한다. 졸음 상태인 경우, 아닌 경우인 2가지의 운전자 상태를 85%의 정확도로 판단한다. 또한 제안한 알고리즘을 활용해 졸음운전 감지 시스템과 더불어 졸음운전 예방 시스템을 제안하고자 한다.

• Journal of Biomedical Engineering Research
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• v.24 no.2
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• pp.99-106
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• 2003

In this paper, a method of discriminating EEG for diagnoses of mental activity is proposed. The proposed method for classification of schizophrenia and normal EEG is based on the wavelet transform and the artificial neural network. The wavelet coefficients of $\alpha$ band, $\beta$ band, $\theta$ band, and $\delta$ band are obtained using the wavelet transform. The magnitude, mean, and variance of wavelet coefficients for each EEG band are applied to the input data of the system's ANN. The architecture of the ANN s a four layered feedforward network with two hidden layer which implements the error back propagation learning algorithm. Through the classification of schizophrenia composed of 19 ANNs corresponding to 19 channels, the classifying system show that it can classify the 100% of the normal EEG group and the 86.67% of the schizophrenia EEG group.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.29-34
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• 2015

Brain-computer interface (BCI) is being studied for convenient life in various application fields. The purpose of this study is to investigate a changing electroencephalography (EEG) for precise motion of a robot or an artificial arm. Three subjects who participated in this experiment performed three-task: Grip, Move, Relax. Acquired EEG data was extracted feature data using two feature extraction algorithm (power spectrum analysis and multi-common spatial pattern). Support vector machine (SVM) were applied the extracted feature data for classification. The classification accuracy was the highest at Grip class of two subjects. The results of this research are expected to be useful for patients required prosthetic limb using EEG.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.518-524
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• 2022

In this paper, we propose a method for combining convolutional neural networks and attention mechanism to improve the recognition performance of emotional stress from Electroencephalogram (EGG) signals. In the proposed method, EEG signals are decomposed into five frequency domains, and spatial information of EEG features is obtained by applying a convolutional neural network layer to each frequency domain. As a next step, salient frequency information is learned in each frequency band using a gate transformer-based attention mechanism, and complementary frequency information is further learned through inter-frequency mapping to reflect it in the final attention representation. Through an EEG stress recognition experiment involving a DEAP dataset and six subjects, we show that the proposed method is effective in improving EEG-based stress recognition performance compared to the existing methods.