• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.1-8
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• 2019

Motion sickness is dizziness symptom that occurs when movement detected in the vestibular organ and movement detected visually are collide with each other. When dizziness occurs, user complains of symptoms such as nausea and vomiting, sense of direction abnormality, and fatigue. These causes of dizziness are various and difficult to differentiate and treat the symptoms. Especially, among the types of dizziness VIMS(Visually Induced Motion Sickness) is a problem to solve in developing VR industry. These VIMS analysis can be done through user's vital signs measurement and feature analysis, and EEG characteristics analysis. Therefore, this paper is discuss the minimization of motion sickness caused by visual information based on EEG signal and present research trends related to it.

• Journal of Biomedical Engineering Research
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• v.2 no.2
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• pp.83-88
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• 1981

A method of detecting abnormal spikes occuring in the EEG of subjects suffering from epilepsy is studied. The detection scheme is to take the first derivative of EEG and to determine if it exceed some threshold value. This study is focused on the digital signal processing for detecting abnormal spikes using microcomputer.

• 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 Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.595-600
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• 2014

Nowadays our society is rapidly transforming into an aging society. A better understanding of dementia is a high priority in the aging society. Therefore our study is basically aimed at understanding characteristics of EEG signals from dementia patients. Firstly, we analyzed spontaneous EEG signals from normal persons and dementia patients to distinguish their characteristics. The EEG signals are recorded with 16 electrodes and we classified the EEG signals form the signals according to frequency band. To obtain the clean EEG signals, we used cross correlation function between two channels. From the analysis results, we can observe that the EEG characteristics from dementia patients are distinctly different from that from normal persons.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.282-285
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• 1995

In this paper, we present a feedback structured adaptive RF filter based on the recursive modified Gram-Schmidt algorithm for short-term prediction of EEG signal. And the performance of this proposed filter is compared with those of linear AR model, RF filter, Volterra filter and RBF neural network as single-step prediction and multi-step prediction. The results show the superiority of this proposed filter in prediction of EEG signals.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.228-236
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• 2011

Emotion recognition plays an important role in the research area of human-computer interaction, and it allows a more natural and more human-like communication between humans and computer. Most of previous work on emotion recognition focused on extracting emotions from face, speech or EEG information separately. Therefore, a novel approach is presented in this paper, including face, speech and EEG, to recognize the human emotion. The individual matching scores obtained from face, speech, and EEG are combined using a weighted-summation operation, and the fused-score is utilized to classify the human emotion. In the experiment results, the proposed approach gives an improvement of more than 18.64% when compared to the most successful unimodal approach, and also provides better performance compared to approaches integrating two modalities each other. From these results, we confirmed that the proposed approach achieved a significant performance improvement and the proposed method was very effective.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.4
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• pp.31-37
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• 1982

A microcomputerized biomedical signal processing system has been designed and fabricated. Softwares for this system have also been developed to record and analyze ECG and EEG waveforms. In this systenm, the vectorcardiogram of ECG waveforms is formed automatically and displayed on CRT with of her usefull cardiac information. The frequency components of EEG waveform can also be analyzed in this system and the analyzed spectrum is displayed on CRT.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1856-1864
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• 2017

The ultimate goal of multimedia in bio-signal research is to approach multimedia contents through bio-signal. Hence it is important to interpret user's emotions by analyzing his or her bio-signals. In this paper, we construct ERP task of oddball component to analyze EEG signal between normal stimuli and fear stimuli and measure EEG during ERP task. The results from extracted ERP component show that there is a difference in N200 in visual stimuli, P300 in auditory stimuli, and N100 and P300. Moreover, there are larger changes in audiovisual stimuli, indicating that users recognize greater fear of crime when visual and auditory stimuli are simultaneously presented.

• Journal of IKEEE
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• v.11 no.1 s.20
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• pp.24-29
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• 2007

In this paper, we developed a Mac-Yule measurement system which consider psychological stable state of patience. The developed system consist with a hardware device that can derive a EEG, respiration and pulse wave, and a software which acquire a biological signal and signal processing The EEGs are derived with bipolar method from frontal head. The respiration signals obtain from nasal front with a transducer which consist with thermistor bridge. The pulse waves are detected from earlobe with photoplethysmograph method. A power spectrum of EEG are used as the decision parameters of psychological stable state of patience. The decision of Mac-Yule are defined as origin text method that of numbers of pulse to 1 respiration period. As the results of experiment with developed system, we could have a spectrum band discretion of EEG signal, stable respiration signal detection and automatic gain controlled pulse signal with realtime. And then, we could detect Mac-Yules from processed signals.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2170-2178
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• 2014

In this paper, electroencephalographic (EEG) signal of one among subjects measured biosignal with visual evoked stimuli inducing the concentration was analyzed to detect the changes in the attention status during attention task fulfillment from January to February, 2011. The independent component analysis (ICA) was applied to EEG signals to isolate the attention related innate source signal within the brain and Electroculogram (EOG) artifact from measured EEG signals at the scalp. The consecutive accumulation of short time Fourier transformed (STFT) attention source signal with excluded EOG artifact can enhance the regular depiction of EPOCH graph and spectral color map representing time-varying pattern. The extracted attention indices associated with somatosensory rhythm (SMR: 12-15 Hz), and theta wave (4-7 Hz) increase marginally over time. Throughout experimental observation, the ICA with STFT can be used for the assessment of participants' status of attention.