• Journal of the Ergonomics Society of Korea
• /
• v.22 no.4
• /
• pp.1-13
• /
• 2003

This study has investigated psychological responses to various TV advertisements by using EEG(electroencephalogram). In particular, it was assumed that the brain wave would show different patterns depending upon the style of the advertisement that could be categorized into two such as 'information transfer' type and 'emotional reaction' type. Ten healthy males participated in the experiment. EEG signal was extracted from six lobes such as right and left frontal, right and left occipital, right and left temporal while the subjects were watching TV advertisements. Alpha and beta relative power spectrum, and beta/alpha parameter were calculated to compare two kinds of advertisement each other. Additionally, subjective questionnaire was used to examine subject's response by using adjective words and preference test. In result, significant differences were found in left frontal and right occipital lobe in terms of beta/alpha between two different advertisements. And, subjects showed different preference between two advertisements. It was shown that the current method could analyze the brain reaction to advertisement quantitatively, that presented the possibility of using it to marketing research.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.11
• /
• pp.459-463
• /
• 1997

In this paper, we present the elimination of ECG artifact from the polysomnographic EEG and EOG. The idea of this method is that the ECG synchronized EEG segment is detected from ECG and regard samples of that segment a missing signal. After this, we used two interpolation methods to recover the missing segment. One is the Lagrange Polynomial Interpolation Method and the other is the Least Square Error AR Interpolation method. We tested those methods by applying to simulated signals. AR methods works well enough to reject the artifact about 10% of the main artifact level. We practically applied to real EEG and EOG signals. We also developed the algorithm to detect whether the artifact level is high or not. If the artifact level is high, then the interpolations are applied.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.37 no.1
• /
• pp.1-8
• /
• 2014

This paper proposes an emotion classifier from EEG signals based on Bayes' theorem and a machine learning using a perceptron convergence algorithm. The emotions are represented on the valence and arousal dimensions. The fast Fourier transform spectrum analysis is used to extract features from the EEG signals. To verify the proposed method, we use an open database for emotion analysis using physiological signal (DEAP) and compare it with C-SVC which is one of the support vector machines. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the accuracy of the valence and arousal estimation is 67% and 66%, respectively. For the three-level class case, the accuracy is 53% and 51%, respectively. Compared with the best case of the C-SVC, the proposed classifier gave 4% and 8% more accurate estimations of valence and arousal for the two-level class. In estimation of three-level class, the proposed method showed a similar performance to the best case of the C-SVC.

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

• The Journal of the Acoustical Society of Korea
• /
• v.41 no.2
• /
• pp.115-121
• /
• 2022

In this paper, we propose a music classification system according to user emotions using Electroencephalogram (EEG) features that appear when listening to music. In the proposed system, the relationship between the emotional EEG features extracted from EEG signals and the auditory features extracted from music signals is learned through a deep regression neural network. The proposed system based on the regression model automatically generates EEG features mapped to the auditory characteristics of the input music, and automatically classifies music by applying these features to an attention-based deep neural network. The experimental results suggest the music classification accuracy of the proposed automatic music classification framework.

• Journal of Digital Contents Society
• /
• v.19 no.4
• /
• pp.821-827
• /
• 2018

Affective computing has gained increasing interest in the recent years with the development of potential applications in Human computer interaction (HCI) and healthcare. Although momentous research has been done on human emotion recognition, however, in comparison to speech and facial expression less attention has been paid to physiological signals. In this paper, Electroencephalogram (EEG) signals from different brain regions were investigated using modified wavelet energy features. For minimization of redundancy and maximization of relevancy among features, mRMR algorithm was deployed significantly. EEG recordings of a publically available "DEAP" database have been used to classify four classes of emotions with Multi class Support Vector Machine. The proposed approach shows significant performance compared to existing algorithms.

• The Journal of the Korea Contents Association
• /
• v.11 no.4
• /
• pp.42-50
• /
• 2011

Using EEG, human physiological signal, as part of research which investigates the state of student learning and provides appropriate feedback to maximize learning efficiency, the relationship of learning attitude and analysis of EEG for female college student is presented. We study the reaction of learner's EEG using the concentration level extracted from the EEG power spectrum when students learn at various learning attitude. The experiment was conducted for the concentrating on learning and, as a control group, erratic attitude and closed eyes state. The attitude of concentrated Learning shows high concentration index and low relaxation index, where as the erratic attitude, such as eye movement and clicking, shows high level of attention index and noisy wave ratio. Especially, the state of closed eyes shows the ratio of alpha and theta wave under 1. This is distinct with open eyes cases.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.3
• /
• pp.527-532
• /
• 2020

In this paper, we presented the results of analysis through EEG measurement for the purpose of checking the frequency band of EEG signals that can be used for personal authentication. The measurement status was divided into the open-eye state and the closed-eye state depending on the presence or absence of an optical task. The data measured in the EEG experiments was divided into seven frequency bands : delta waves, theta waves, alpha waves, SMR waves, mid-beta waves, beta waves and gamma waves to identify the frequency band with the smallest power fluctuation over time. In our results, there was no significant difference between the open-eye state and the closed-eye state, and the SMR waves and mid-beta waves related to human concentration had the smallest fluctuation in power over time, and were a highly reproducible frequency band.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.924-926
• /
• 2015

In this paper, we use the interface, which many countries concentrates research of Brain - Computer Interface with the device and MyndPlay based on the IoT intelligent Arduino. Finally we will make the Brain - Computer Connection environment, the purpose of Brain - Computer Interface. Recognizes the EEG of a person who wearing the equipment, analyze, classify, and we did a research to design an intelligent thing to suit user's condition. In addition, we use the XBee, and Bluetooth to communicate to other devices, such as smart phone. In conclusion, this paper check users current status via brain waves, and it allows to control the power and other objects by using the EEG(Electroencephalography).

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.3
• /
• pp.103-108
• /
• 2016

In daily life, humans get stress very often. Stress is one of the important factors of healthy life and closely related to the quality of life. Too much stress is known to cause hormone imbalance of our body, and it is observed by the brain and bio signals. Based on this, the relationship between brain signal and stress is explored, and brain signal based stress index is proposed in our work. In this study, an EEG measurement device with 32 channels is adopted. However, only two channels (FP1, FP2) are used to this study considering the applicability of the proposed method in real enveironment, and to compare it with the commercial 2 channel EEG device. Frequency domain features are power of each frequency bands, subtraction, addition, or division by each frequency bands. Features in time domain are hurst exponent, correlation dimension, lyapunov exponent, etc. Total 6 subjects are participated in this experiment with English sentence reading task given. Among several candidate features, ${\frac{{\theta}\;power}{mid\;{\beta}\;power}}$ shows the best test performance (70.8%). For future work, we will confirm the results is consistent in low price EEG device.