• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.27-32
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• 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 Biomedical Engineering Research
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• v.29 no.5
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• pp.355-363
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• 2008

In order to extract only the alpha activity related signals from EEG recordings, we have applied Constrained Independent Component Analysis (cICA), a new extension of ICA in which some a priori knowledge of the alpha activity is utilized to extract only desired components. Its extraction (or filtering) performance has been compared to that of the conventional band-pass filtering via the scalp alpha power maps and cortical source maps of the alpha activity. Our results demonstrate that the alpha power maps and cortical source maps from the cICA-extracted alpha signals reveal more focalized alpha generating regions of the brain than those from the band-pass filtered alpha EEG signals. Furthermore they match more closely the activated regions of the brain mapped using fMRI, validating our results. We believe that the cICA-based filtering approach of EEG signals is a more effective means of extracting a specific brain activity reflected in EEG signals that will result in more accurate source localization or imaging maps.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.1
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• pp.35-41
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• 2006

Although reachers have studied for a long time, they don't make criteria for anesthesia depth. anesthetists can't make a prediction about patient's reaction. Therefor, patients have potential risk such as poisonous side effect late-awake, early-awake and strain reaction. EEG are received from twenty-five patients who agreed to investigate themselves during operation with Enflurane-anesthesis in progress of anesthesia. EEG are divided pre-anesthesia, before incision of skin, operation 1, operation 2, awaking, post-anesthesia by anesthesia progress step. EEG is applied pre-processing, base line correct, linear detrend to get more reliable data. EEG data are handled by electronic processing and the EEG data are calculated by bicoherence. During pre-anesthesia and post anesthesia, appearance rate of bicoherence value is observed strong appearance rate in high frequency range($15\~30Hz$). During the anesthesia of patient, a strong appearance rate is revealed the low frequency area(0~10Hz). After bicoherence is calculated by percentage of a appearance rate, that is, Bicpara$\#$1, Bicpara$\#$2, Bicpara$\#$3 and Bicpara$\#$4 parameter are extracted. In result of bicoherence analysis, Bicpara$\#$2 and Bicpara#4 are considered that the best parameter showed progress of anesthesia effectively. And each separated bicoherence are calculated by average bicoherence's numerical value, divide by 2 area, appear by each BicHz$\#$1, BicHz$\#$2, and observed BicHz$\#$1/BicHz$\#$2's change. In result of bicoherence analysis, BicHz$\#$1, BicHz$\#$2 and BicHz$\#$1/BicHz$\#$2 are considered that the best parameter showed progress of anesthesia effectively. In conclusion, I confirmed the anesthesia progress phase, concluded to usefulness of parameter on bispectrum and bicoherence analysis and evaluated the depth of anesthesia. In the future, it is going to use for doctor's diagnosis and apply to protect an medical accident owing to anesthesia.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.114-117
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• 1990

EEG signal is analyzed by two methods, analysis by visual inspection of EEG recording sheets and analysis by quantative method. Generally visual inspection method is used in the clinical field. But this method has its limitation because EEG signal is random signal. Therefore it is necessary to analyze EEG signals quantatively to obtain more precise and objective information of neural and brain. In this paper, power spectrum of EEG signal was estimated by AR(AutoRegressive) model in the frequency domain. This process is useful as a preprocessing stage for tomographic brain mapping (TBM) at each frequency, band. As a method for estimating power spectral density of EEG signals, periodogram method, autocorrelation method. covariance method, modified covariance method, and Burg method are tested in this paper.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.9_10 s.146
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• pp.1230-1240
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• 2005

The purpose of this study was to apply a scientific and systematic method for assessing fashion goods' TV ads effects by EEG and questionnaires as determined by the type of ads appeal. Ads stimulants used in the survey were limited to underwear and sportswear that were advertised during $2000{\sim}2002$ on TV: 4 information-transferring and 4 emotion-evoking ads were used. Subjects were thirty healthy male and female college students. EEG was extracted from six lobes and the recorded EEG was analyzed by the range of frequency of ${\theta},\;{\alpha}\;and\;{\beta}$ waves. Data were analyzed by SPSS 11.0 with reliability test, $x^2$-analysis, t-test and frequency analysis. The emotion-evoking ads showed higher scores in memory, recall and attitude towards the ads. The responses of ${\theta}\;and\;{\alpha}$ wave were active throughout the ads but the response of ${\beta}$ wave was not. The results by the survey and the EEG test showed high similarities, indicating the EEG tests could be used as the supplementary tool for measuring ads effects.

• Journal of the Ergonomics Society of Korea
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• v.18 no.1
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• pp.13-24
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• 1999

The objective of the study is to discriminate EEG(electroencephalogram) due to emotional changes. Emotion was evoked by the series of auditory stimuli which were selected from the natural sounds in the sound effect collection of compact disc. Seventeen university students participated and experienced positive or negative emotions by six auditory stimuli with intermission between stimuli. Temporal EEG ($T_3$, $T_4$, $T_5$, and $T_6$) was recorded at the same time and a subjective test was performed on the eleven point scales after the experiment. The maximum and minimum scores of the EEG among six stimuli EEG were analyzed for discrimination of emotion. The EEG signals were transformed into feature objects based on scalar intervention model coefficients. Auditory stimulus was considered as intervention variable. They were classified by Discriminant Analysis for each channel. The features showed results with the best classification accuracy of 91.2 % in $T_4$ for auditory stimuli. This study could be extended to establish an algorithm which quantifies and classifies emotions evoked by auditory stimulus using time-series models.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.725-730
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• 2013

This paper proposed a novel method for an analysis feature of an Electroencephalogram (EEG) at all channels simultaneously. In a BCI (Brain-Computer Interface) system, EEGs are used to control a machine or computer. The EEG signals were weak to noise and had low spatial resolution because they were acquired by a non-invasive method involving, attaching electrodes along with scalp. This made it difficult to analyze the whole channel of EEG signals. And the previous method could not analyze multiple stimuli, the result being that the BCI system could not react to multiple orders. The method proposed in this paper made it possible analyze multiple-stimuli by grouping the channels. We searched the groups making the largest correlation coefficient summation of every member of the group with a BHS (Binary Harmony Search) algorithm. Then we assumed the EEG signal could be written in linear summation of groups using concentration parameters. In order to verify this assumption, we performed a simulation of three subjects, 60 times per person. From the simulation, we could obtain the groups of EEG signals. We also established the types of stimulus from the concentration coefficient. Consequently, we concluded that the signal could be divided into several groups. Furthermore, we could analyze the EEG in a new way with concentration coefficients from the EEG channel grouping.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1331-1338
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• 2007

A new method for detecting and eliminating the Electrocardiogram(ECG) artifact from the scalp Electroencephalogram(EEG) is proposed. Based on the single channel EEG, the proposed method consists of 4 procedures: emphasizing the R-wave of ECG artifact from EEG using the least squares acceleration(LSA) filter, detecting the R-wave from the LSA filtered EEG using the phase space method and R-R interval, generating the delayed impulse synchronized to the R-wave and elimination of the ECG artifacts based on the adaptive digital filter using the impulse and raw EEG. The performance of the proposed method was evaluated in the two separating parts of R-wave detection and, ECG estimation and elimination from EEG. In the R-wave detection, the proposed method showed the mean error rate of 6.285(%). In the ECG estimation and elimination using simulated and/or real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, in which independent component analysis and ensemble average method are used. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifact from single channel EEG and simple for ambulatory/portable EEG monitoring system.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.52.5-52
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• 2002

In this paper, we develop analysis models for classification of temporal data from human subjects. The study focuses on the analysis of electroencephalogram (EEG) signals obtained during various emotional states. We demonstrate a generally applicable method of removing EOG and EMG artifacts from EEGs based on independent component analysis (ICA). All EEG channel maps were interpolated from 10 EEG subbands. ICA methods are based on the assumptions that the signals recorded on the scalp are mixtures of signals from independent cerebral and artifactual sources, that potentials arising from different parts of the brain, scalp and body are summed linearly at the electrodes and that prop...

• International Journal of Contents
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• v.14 no.4
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.