• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.124-132
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• 2023

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• Research in Community and Public Health Nursing
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• v.28 no.2
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• pp.107-117
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• 2017

Purpose: The purpose of this study is to examine the cognitive function in the elderly and to identify the influencing factors. Methods: The design of this study was descriptive research design. A total of 139 elderly people (aged 65 years and over) visiting the electroencephalogram (EEG) center in Seoul, Korea were evaluated. Data were assessed by self-administered questionnaires and CNS Vital Signs (CNSVS). Data were analyzed using SPSS Statistics 23.0 for Windows. Results: There were significant differences in the Korean Mini-Mental State Examination (K-MMSE), executive functions and reasoning according to education level. K-MMSE, visual memory and executive functions were different depending on the jobs. Age was highly correlated with cognitive function. In addition, stepwise multiple regression analyses showed that the factor significantly associated with reaction time and visual memory was depression. Depression and Trait-Anxiety had significant impacts on executive functions and K-MMSE. Conclusion: CNSVS enabled the accurate and objective measurement of cognitive function. Therefore, this study provides useful data to improve cognitive function of the community-dwelling elderly. The results suggested that there is need for comprehensive interventional programs that manage cognitive impairment.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.19-23
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• 1996

A full automatic interpretation of awake electroencephalogram (EEG) had been developed by the authors and presented at the past KACCs in series. The automatic EEG interpretation consists of four main parts: quantitative EEG interpretation, EEG report making, preprocessing of EEG data and adaptable EEG interpretation. The automatic EEG interpretation reveals essentially the same findings as the electroencephalographer's (EEG's), and then would be applicable in clinical use as an assistant tool for EEGer. The method had been developed through collaboration works between the engineering field (Saga University) and the medical field (Kyoto University). This work can be understood as an artificial realization of human expert skill. The procedure for the artificial realization was summarized in a methodology for artificial realization of human skill which will be applicable in other fields of systems control.

• Journal of the Korean Society of Safety
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• v.23 no.3
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• pp.65-70
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• 2008

Electroencephalogram(EEG) would be the most objective psychophysiological research technique on human errors though few research has been taken yet. This study aimed to get characteristics of human error while committing simple Odd-Ball tasks by utilizing the power spectrum technique of EEG data. Each experiment was composed of 3 tasks with different rules, and three young undergraduate students participated in this study as paid subjects. The result showed that subject and the interaction of subject and task factors were statistically significant on variation of power of $\alpha$ and $\beta$ bands which implied there would exist groups with homogeneity in their response. And though the variation of band powers due to task factors were not so great as to get statistical significance, it implied that the task requiring decoding process would be more strange to human beings than the task merely requiring psychological recall process.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.75-78
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• 1994

To represent the overall potential distribution on the entire scalp it is necessary to interpolate between sampled EEG(electroencephalogram) values, we describe a method to interpolate between scalp recorded EEG data which obtained from electrodes irregularly disposed on the scalp, using polynomial interpolation. This method can analyze the variance of source temporally or spatially and present continuous distributed topographic mapping of the EEG records. In the result, we obtained the overall potentials distribution on the entire scalp from the EEG records of a patient which was known to epilepsy.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.265-268
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• 1995

Signal averaging technique to improve signal-to-noise ratio has widely been used in various fields, especially in electrophysiology. Estimation of the EP(evoked potential) signal using the conventional averaging method fails to correctly reconstruct the original signal under EEG(electroencephalogram) noise especial]y when the latency times of the evoked potential are not identical. Therefore, a technique based on the bispectrum averaging was proposed for recovering signal waveform from a set o noisy signals with variable signal dalay. In this paper an improved bispectrum estimation technique of the RP signal is proposed using a confidence thresholding of the EP signal in frequency domain in which energy distribution of the EP signal is usually not uniform. The suggested technique is coupled with the conventional bispectrum estimation technique such as least square method and recursive method. Some results with simulated data and real EP signal are shown.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.736-740
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• 1991

EEG(Electroencephalogram) background signals can be represented as the sun of a conventional AR(Autoregressive) process and an innovation process, or a prediction error process. We have seen that conventional estimation techniques. such as least square estimates(LSE) or Gaussian maximum likelihood estimates(MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. But when the data are contaminated by outliers, or artifacts, these assumptions are not met and conventional estimation techniques can badly fall and be strongly biased. It is known that EEG can be easily affected by artifacts. So we suggest a robust estimation technique which considerably performs well against those artifacts.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1190-1195
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• 1990

A new system for automatic interpretation of the awake electroencephalogram(EEG) was developed in this work. We first clarified all the necessary items for EEG interpretation in accordance with an analysis of visual inspection of the rhythms by a qualified electroencephalographer (EEGer), and then defined each item quantitatively. Concerning the automatic interpretation, we made an effort to find out specific EEG parameters which faithfully represent the procedure of visual interpretation by the qualified EEGer. Those specific EEG parameters were calculated from periodograms of the EEG time series. By using EEG data of 14 subjects, the automatic EEG interpretation system was constructed and compared with the visual interpretation done by the EEGer. The automatic EEG interpretation thus established was proved to be in agreement with the visual interpretation by the EEGer.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.127-130
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• 2000

Classification of Electroencephalogram(EEG) makes one of key roles in the field of clinical diagnosis, such as detection for epilepsy. Spectrum analysis using the fourier transform(FT) uses the same window to signals, so classification rate decreases for nonstationary signals such as EEG's. In this paper, wavelet power spectrum method using wavelet transform which is excellent in detection of transient components of time-varying signals is applied to the classification of three types of Event Related Potential(EP) and compared with the result by fourier transform. In the experiments, two types of photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. After choosing a specific range of scales, scale-averaged wavelet spectrums extracted from the wavelet power spectrum is used to find features by Back-Propagation(13P) algorithm. As a result, wavelet analysis shows superiority to fourier transform for nonstationary EEG signal classification.