• Journal of Korean Elementary Science Education
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• v.25 no.spc5
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• pp.556-566
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• 2007

The purpose of this study was to develop an electroencephalogram (EEG) based differential-scale for scientifically gifted students in elementary school. For this study, signals of EEG with 19 channels were recorded during the generation of our scientific hypothesis using 22 scientifically gifted students, and with 49 average students being used as the control group. IQ, TCT and knowledge generation (KG) as constructs of the scientifically gifted were administered for both the scientifically gifted and the normal, control group elementary students. A 'gifted' value was added to paper test scores of the IQ, TCT, and KG constructs in order to make a personal standardization score for the gifted students. As a dependent variable, the groups were divided by means of the standardization scores thus produced and as an autonomous variable, various EEG parameters were presented through linear analysis, nonlinear analysis, and interdependency measures of the EEG. Multiple linear regression analysis was applied successfully to explain the EEG parameters and to show the characteristics of the scientifically-gifted. The discrimination analysis was administered through the results of multiple linear regression of the EEG parameters thus produced. This study represents the foundation of the development of an EEG based discriminant-scale for scientifically gifted students in elementary school, because it will be able to faithfully discriminate between scientifically-gifted and average students. The results of this study indicates that most of the EEG parameters produced can contribute to predicting the characteristics of the scientifically-gifted in that they express the degree of mutual information and the coherence of mutuality. Accordingly, mutual connectivity which appears to originate in the brain seems to the core of discrimination.

• Biomedical Engineering Letters
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• v.8 no.4
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• pp.373-382
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• 2018

Since epileptic seizure is unpredictable and paroxysmal, an automatic system for seizure detecting could be of great significance and assistance to patients and medical staff. In this paper, a novel method is proposed for multichannel patient-specific seizure detection applying the earth mover's distance (EMD) in scalp EEG. Firstly, the wavelet decomposition is executed to the original EEGs with five scales, the scale 3, 4 and 5 are selected and transformed into histograms and afterwards the distances between histograms in pairs are computed applying the earth mover's distance as effective features. Then, the EMD features are sent to the classifier based on the Bayesian linear discriminant analysis (BLDA) for classification, and an efficient postprocessing procedure is applied to improve the detection system precision, finally. To evaluate the performance of the proposed method, the CHB-MIT scalp EEG database with 958 h EEG recordings from 23 epileptic patients is used and a relatively satisfactory detection rate is achieved with the average sensitivity of 95.65% and false detection rate of 0.68/h. The good performance of this algorithm indicates the potential application for seizure monitoring in clinical practice.