• The Korean Society of Law and Medicine
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• v.21 no.3
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• pp.145-178
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• 2020

This paper examines discussions surrounding cognitive liberty, neuro-privacy, and mental integrity from the perspective of Neuro-rights. The right to control one's neurological data entails self-determination of collection and usage of one's data, and the right to object to any way such data may be employed to negatively impact oneself. As innovations in neurotechnologies bear benefits and downsides, a novel concept of the neuro-rights has been suggested to protect individual liberty and rights. In Oct. 2020, the Chilean Senate presented the 'Proyecto de ley sobre neuroderechos' to promote the recognition and protection of neuro-rights. This new bill defines all data obtained from the brain as neuronal data and outlaws the commerce of this data. Neurotechnology, especially when paired with big data and artificial intelligence, has the potential to turn one's neurological state into data. The possibility of inferring one's intent, preferences, personality, memory, emotions, and so on, poses harm to individual liberty and rights. However, the collection and use of neural data may outpace legislative innovation in the near future. Legal protection of neural data and the rights of its subject must be established in a comprehensive way, to adapt to the evolving data economy and technical environment.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.188-195
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• 1999

The fMRI signals are composed of many various signals. It is very difficult to find the accurate parameter for the model of fMRI signal containing only neural activity, though we may estimating the signal patterns by the modeling of several signal components. Besides the nose by the physiologic motion, the motion of object and noise of MR instruments make it more difficult to analyze signals of fMRI. Therefore, it is not easy to select an accurate reference data that can accurately reflect neural activity, and the method of an analysis of various signal patterns containing the information of neural activity is an issue of the post-processing methods for fMRI. In the present study, fMRI data was analyzed with the Independent Component Analysis(ICA) method that doesn't need a priori-knowledge or reference data. ICA can be more effective over the analytic method using cross-correlation analysis and can separate the signal patterns of the signals with delayed response or motion related components. The Principal component Analysis (PCA) threshold, wavelet spatial filtering and analysis of a part of whole images can be used for the reduction of the freedom of data before ICA analysis, and these preceding analyses may be useful for a more effective analysis. As a result, ICA method will be effective for the degree of freedom of the data.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.826-829
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• 2016

뇌파는 뇌신경 세포의 활동으로 발생하는 전기적 변화를 두피 표면에서 측정하여 기록한 것이다. 뇌활동의 변화는 공간적 시각적으로 파악할 수 있는 지표이며, 바이오 센싱을 통해 측정된 데이터를 활용한 미디어 컨텐츠 적용이 최근 시도되고 있다. 본 연구는 미디어 컨텐츠 평가를 위한 감성 지표로 뇌파 데이터를 인간의 오감으로 느낄 수 있는 표현으로 변환하는 프로세스를 구현하는 함으로써 현재 행해지고 있는 많은 미디어 아트에 대해 뇌파를 활용한 실시간 객관적 감성지표를 완성하는데 목적이 있다. 이에 대한 사전 연구로 본 논문에서는 측정된 뇌파 데이터를 인터렉티브 컨테츠적인 요소인 사운드로 청각화 하는 과정을 구현하였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.21-26
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• 2011

Feature ranking is useful to gain knowledge of data and identify relevant features. In this study, we proposed a use of feature ranking for classification of neuro-degeneration and vascular dementia in micro-Raman spectra of platelet. The entire region of the spectrum is divided into local region including several peaks, followed by Gaussian curve fitting method in the region to be modeled. Local minima select from the subregion and then remove the background based on the position by using interpolation method. After preprocessing steps, significant features were selected by feature ranking method to improve the classification accuracy and the computational complexity of classification system. PCA (principal component analysis) transform the selected features and the overall features that is used classification with the number of principal components. These were classified as MAP (maximum a posteriori) and it compared with classification result using overall features. In all experiments, the computational complexity of the classification system was remarkably reduced and the classification accuracy was partially increased. Particularly, the proposed method increased the classification accuracy in the experiment classifying the Parkinson's disease and normal with the average 1.7 %. From the result, it confirmed that proposed method could be efficiently used in the classification system of the neuro-degenerative disease and vascular dementia of platelet.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.161-175
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• 2022

Oscillatory magnetic fields produced in the brain due to neuronal activity can be measured by the sensor. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution, which gives information about the brain's functional activity. Potential utilization of high spatial resolution in MEG is likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in some diseases under resting state or task state. This review is a comprehensive report to introduce statistical models from MEG data including graphical network modelling. It is also meaningful to note that statisticians should play an important role in the brain science field.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.925-934
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• 2016

As we enter the 21st century, interests in health and quality of life have grown gradually. In this study, we analyzed the data in response to each questionnaire for life management and sub-health among targeted members of a particular group. The results of the analysis of life management have found no difference between genders at the 5% of significance level. In respect to gender, a differential analysis of sub-health, however, has shown a gender difference in which female students had significantly worse health conditions than male students in the areas of immune system, intestine, cerebral nerve, hormone, and urinary system. Moreover, we also have found no significant difference among colleges in terms of life management and sub-health. In conclusion, it was shown that sub-health is closely related with life management.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1912-1913
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• 2007

뇌 기계 인터페이스는 뇌에 직접 연결을 시도하는 인터페이스로서 인간의 의지 또는 생각을 컴퓨터가 인식할 수 있는 디지털 신호로 바꾸는 새로운 휴먼 컴퓨터 인터페이스 중 하나이다. 뇌신경의 신호 전달 과정이 전기적, 화학적 특성을 지닌다는 사실에 착안하여 뇌의 활동을 측정하는 많은 기술들이 개발되어 왔다. PET, fMRI, MEG, EEG 등을 포괄하는 brain functional imaging 기술 중 뇌 기계 인터페이스에서 가장 주목하고 있는 것이 바로 EEG 이다. 본 연구에서는 뇌기계 인터페이스 시스템 개발에 필요한 무선 EEG 측정 장치를 설계하고, 무선 EEG 측정 장치와 컴퓨터간에 데이터 전송과 EEG 신호를 FFT 분석 하였다.

• Journal of IKEEE
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• v.25 no.4
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• pp.679-688
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• 2021

Gait is an evaluation index used in various clinical area including brain nervous system diseases. Signal source localizing and time-frequency analysis are mainly used after extracting independent components for Electroencephalogram data as a method of measuring and analyzing brain activation related to gait. Existing treadmill-based walking EEG analysis performs signal preprocessing, independent component analysis(ICA), and source localizing by merging data after the multiple EEG measurements, and extracts representative component clusters through inter-subject clustering. In this study we propose an analysis method, without merging to single dataset, that performs signal preprocessing, ICA, and source localization on each measurements, and inter-subject clustering is conducted for ICs extracted from all subjects. The effect of data merging on the IC clustering and time-frequency analysis was investigated for the proposed method and two conventional methods. As a result, it was confirmed that a more subdivided gait-related brain signal component was derived from the proposed "non-merging" method (4 clusters) despite the small number of subjects, than conventional method (2 clusters).

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.51-75
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• 2022

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation that is able to alter neuronal activity in particular brain regions. Many studies have researched how tDCS modulates neuronal activity and reorganizes neural networks. However it is difficult to conclude the effect of brain stimulation because the studies are heterogeneous with respect to the stimulation parameter as well as individual difference. It is not fully in agreement with the effects of brain stimulation. In particular few studies have researched the reason of variability of brain stimulation in response to time so far. The study investigated individual variability of brain stimulation based on circadian rhythm and chronotype. Participants were divided into two groups which are morning type and evening type. The experiment was conducted by Zoom meeting which is video meeting programs. Participants were sent experiment tool which are Muse(EEG device), tdcs device, cell phone and cell phone holder after manuals for experimental equipment were explained. Participants were required to make a phone in frount of a camera so that experimenter can monitor online EEG data. Two participants who was difficult to use experimental devices experimented in a laboratory setting where experimenter set up devices. For all participants the accuracy of 98% was achieved by SVM using leave one out cross validation in classification in the the effects of morning stimulation and the evening stimulation. For morning type, the accuracy of 92% and 96% was achieved in classification in the morning stimulation and the evening stimulation. For evening type, it was 94% accuracy in classification for the effect of brain stimulation in the morning and the evening. Feature importance was different both in classification in the morning stimulation and the evening stimulation for morning type and evening type. Results indicated that the effect of brain stimulation can be explained with brain state and trait. Our study results noted that the tDCS protocol for target state is manipulated by individual differences as well as target state.

• Korean Journal of Cognitive Science
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• v.22 no.4
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• pp.347-364
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• 2011

In this paper, we propose a automatic image bases extraction method for visual image reconstruction from brain activity using Non-negative Matrix Factorization (NMF). Image bases are basic elements to construct and present a visual image. Previous method used brain activity that evoked by predefined 361 image bases of four different sizes: $1{\times}1$, $2{\times}1$, $1{\times}2$, $2{\times}2$, and $2{\times}2$. Then the visual stimuli were reconstructed by linear combination of all the results from these image bases. While the previous method used 361 predefined image bases, the proposed method automatically extracts image bases which represent the image data efficiently. From the experiments, we found that the proposed method reconstructs the visual stimuli better than the previous method.