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Jang, Hwan Young;Jang, Bong Seok
- Journal of Practical Engineering Education
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- v.12 no.1
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- pp.41-47
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- 2020
This study aims to investigate effects of brain-based learning. 27 primary studies were selected through rigorous search process and analyzed through meta-analytic methods. Research findings are as follows. First, the total effect size was .67. Second, the effect of dependent variables was academic achievement, cognitive domain, and affective domain in order. Third, with respect to types of cognitive domain, the effect was self-regulation, creativity, competence, communication, and research ability in order. Fourth, the effect of affective domains was sociality, learning interest, and subject attitude in order. Fifth, regarding development of cognitive ability, the effect size was combined, brain training, learning environments, and right brain activities in order. Sixth, the effect of learning activities was memory improvement and attention enhancement in order.
https://doi.org/10.14702/JPEE.2020.041 인용 PDF KSCI

Han Yeji;Park Hyun Wook
- Journal of the Institute of Electronics Engineers of Korea SC
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- v.41 no.1
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- pp.55-62
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- 2004
A semi-automatic registration process of determining specified points is presented, which is required to register brain MR images based on Talairach atlas. Generally, ten specified points that define Talairach coordinates are anterior commissure(AC), posterior commissure (PC), anterior feint (AP), posterior point (PP), superior point (SP), inferior point (IP), left point (LP), right point (RP) and two points for the midline of the brain. The suggested method reduces user interaction for S points, and finds the necessary points for registration in a more stable manner by finding AC and PC using two-level shape matching of the corpus callosum (CC) in an edge-enhanced brain M image. Remaining points are found using the intensity information of cutview.
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Kwon, Junmo;Park, Hyunjin
- Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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- 2022.10a
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- pp.297-299
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- 2022
Malignant glioma has a poor prognosis with the reported median survival of between 6 months to 14 months. Thus, it is crucial to predict the accurate survival of patients with malignant glioma. In this paper, we propose a convolutional neural network to predict the overall survival and age of the patients. A total of four MRI modalities, T1, T1-contrast enhanced, T2, and fluid-attenuated inversion recovery, which effectively capture spatial characteristics of malignant glioma, were used as input images. Age is an important factor impacting the overall survival, thus incorporating it into the model will thereby improve the performance of the proposed model. Our model successfully predicted overall survival and age of the patients with pearson correlation coefficients of 0.1748 and 0.3056, respectively.
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Song, Yoon-Kyu
- Journal of the Institute of Electronics and Information Engineers
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- v.50 no.4
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- pp.229-235
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- 2013
In this paper, we investigate several important issues on the implementation of a totally implantable microsystem for brain-machine interface that has been attracting a lot of attention recently. So far most of the scientific research has been focused on the high performance, low power electronics or systems such as neural signal amplifiers and wireless signal transmitters, but the real application of the implantable microsystem is affected significantly by a number of factors, ranging from design of the encapsulation structure to physiological and anatomical characteristics of the brain. In this work, we discuss on the thermal effect of the system, the detecting volume of the neural probes, wireless data transmission and power delivery, and physiological and anatomical factors that are critically important for the actual implementation of a totally brain implantable neural interface microsystem.
https://doi.org/10.5573/ieek.2013.50.4.229 인용 PDF KSCI