• Proceedings of the KOSOMBE Conference
• /
• v.1995 no.11
• /
• pp.117-120
• /
• 1995

The electronencephalogram (EEG) is a complex electrical signal which reflects generalized brain activity. The EEG is utilized in the clinical assesment of many neurological and psychiatric disorders and offers promise for monitoring of patients undergoing operation. This paper describes a technique for quantitative analysis of EEG signals which is based on an eigenspace. Examples of the application approach to simulated and clinical EEG data illustrate the capabilities.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.1
• /
• pp.39-42
• /
• 2002

For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.879-880
• /
• 2006

The subject of this paper is to recognize the stable state of EEG using wavelet transform and power spectrum analysis. An alpha wave, showed in stable state, is dominant wave for a human EEG and a beta wave displayed excited state. We decomposed EEG signal into an alpha wave and a beta wave in the process of wavelet transform. And we calculated each power spectrum of EEG signal, an alpha wave and a beta wave using Fast Fourier Transform. We recognized the stable state by making a comparison between power spectrum ratios respectively.

• The Journal of the Korea Contents Association
• /
• v.11 no.4
• /
• pp.27-32
• /
• 2011

This paper presents a methodology for classifying sleep disturbance using electroencephalogram (EEG) signal at sleep stage 2 and principal component analysis. For extracting initial features, fast Fourier transforms(FFT) were carried out to remove some noise from EEG signal at sleep stage 2. In the second phase, we used principal component analysis to reduction from EEG signal that was removed some noise by FFT to 5 features. In the final phase, 5 features were used as inputs of NEWFM to get performance results. The proposed methodology shows that accuracy rate, specificity rate, and sensitivity were all 100%.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.44 no.5
• /
• pp.824-836
• /
• 2020

This study investigated the effects of textile attachments on electroencephalogram using 20 persons (10 males and 10 females). Four types of attachment cushions were manufactured by changing the shell fabric (cotton and microfiber) and interlining (synthetic loose fiber and buckwheat). This was done using BIOS-S8 (BioBrain Inc., Korea), an 8-channel polygraph for multi-body signal measurement, to measure EEG. Data were analyzed using the SPSS 24.0 statistical program. EEG values were significantly activated according to gender, particularly when the subjects' eyes were open. For the male cases, 'RT', 'RAHB' values were highly activated and for the female cases, 'RA', 'RB', 'RG', 'RFA', 'RST', 'RLB', 'RMB', 'RST', 'RMT' values were highly activated. Examining the differences in EEG according to type of attachment indicated no significant difference in both sexes. However, in cases of females with their eyes closed, the 'RSA' index was quite different in the left occipital lobe (O1), and when their eyes were open, the 'RFA' in the right frontal lobe (F4) showed a significant difference. However, there was no obvious correlation between the activation of EEG and the subjective preference of textile attachments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.260-263
• /
• 2003

EEC is an electrical signal, which occurs during information processing in the brain. These EEG signals has been used clinically, but nowadays we are mainly studying Brain-Computer Interface(BCI) such as interfacing with a computer through the EEG controlling the machine through the EEG The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. A BCI has to perform two tasks, the parameter estimation task, which attemps to describe the properties of the EEG signal and the classification task, which separates the different EEC patterns based on the estimated parameters. First, we have to do parameter estimation of EEG to embody BCI system. It is important to improve performance of classifier, But, It is not easy to do parameter estimation by reason of EEG is sensitivity and undergo various influences. Therefore, this research should do parameter estimation and classification of the EEG to use various analysis algorithm.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.3
• /
• pp.16-24
• /
• 2019

Recently, research on emotion recognition based on EEG has attracted great interest from human-robot interaction field. In this paper, we propose a method of labeling using image-based EEG topography instead of evaluating emotions through self-assessment and annotation labeling methods used in MAHNOB HCI. The proposed method evaluates the emotion by machine learning model that learned EEG signal transformed into topographical image. In the experiments using MAHNOB-HCI database, we compared the performance of training EEG topography labeling models of SVM and kNN. The accuracy of the proposed method was 54.2% in SVM and 57.7% in kNN.

• 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 Korea Society of Industrial Information Systems
• /
• v.22 no.1
• /
• pp.41-51
• /
• 2017

This Paper Describes a Method for the Evaluation of Sleep Apnea, Namely, the Peak Signal-to-noise ratio (PSNR) of Wavelet Transformed Electroencephalography (EEG) Data. The Purpose of this Study was to Investigate EEG Properties with Regard to Differences between Sleep Spindles and K-complexes and to Characterize Obstructive Sleep Apnea According to Sleep Stage. We Examined Non-REM and REM Sleep in 20 Patients with OSA and Established a New Approach for Detecting Sleep Apnea Base on EEG Frequency Changes According to Sleep Stage During Sleep Apnea Events. For Frequency Bands Corresponding to A3 Decomposition with a Sampling Applied to the KC and the Sleep Spindle Signal. In this Paper, the KC and Sleep Spindle are Ccalculated using MSE and PSNR for 4 Types of Mother Wavelets. Wavelet Transform Coefficients Were Obtained Around Sleep Spindles in Order to Identify the Frequency Information that Changed During Obstructive Sleep Apnea. We also Investigated Whether Quantification Analysis of EEG During Sleep Apnea is Valuable for Analyzing Sleep Spindles and The K-complexes in Patients. First, Decomposition of the EEG Signal from Feature Data was Carried out using 4 Different Types of Wavelets, Namely, Daubechies 3, Symlet 4, Biorthogonal 2.8, and Coiflet 3. We Compared the PSNR Accuracy for Each Wavelet Function and Found that Mother Wavelets Daubechies 3 and Biorthogonal 2.8 Surpassed the other Wavelet Functions in Performance. We have Attempted to Improve the Computing Efficiency as it Selects the most Suitable Wavelet Function that can be used for Sleep Spindle, K-complex Signal Processing Efficiently and Accurate Decision with Lesser Computational Time.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.1
• /
• pp.7-12
• /
• 2020

Various parameters are used to measure anesthetic depth during surgery using brain waves, and in actual clinical use, the linear analysis SEF is widely used. However, with recent studies showing that biological signals including EEG, contain nonlinear properties interest in nonlinear analysis of brain signals is increasing and parameters based on these are being developed. In this study, we are going to develop a parameter that can measure EEG using the nonlinear analysis method and extract noise that can be mixed with external electronic equipment and EEG instrumentation by comparing it with the data from the bispectrum analysis of static waves.