RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      KCI등재 SCOPUS

      Investigation of Functional Brain Connectivity by Electroencephalogram Signals using Data Mining Technique

      한글로보기

      https://www.riss.kr/link?id=A105965660

      • 0

        상세조회
      • 0

        다운로드
      서지정보 열기
      • 내보내기
      • 내책장담기
      • 공유하기
      • 오류접수

      부가정보

      다국어 초록 (Multilingual Abstract)

      Human brain is one of the most complex and the most vital human body organs with different parts of it being interconnected even if these parts are anatomically separate. It is essential to consider the brain function as an integrated system in order ...

      Human brain is one of the most complex and the most vital human body organs with different parts of it being interconnected even if these parts are anatomically separate. It is essential to consider the brain function as an integrated system in order to get insight into the complex structure and function of the cerebral network as a key concept in neuroscience. The patterns obtained from the function of different brain areas and their processing techniques yield a complete set of information about the available relationship between brain areas and make it possible to analyze the function of the brain system correctly using new modeling tools. In this regard, in the present study, brain function was analyzed in 6 subjects using the picture-naming test (148 images of animals D249 and tools D250) and EEG signal recording method through 256 channels. In this two-session test (with 74 stimuli), 12 signals related to the brain function of these subjects were recorded and analyzed in delta, theta, beta, alpha and non-filter state. Furthermore, the pattern of relationship between the channels and the brain communication network in different areas was calculated and elicited in two modes of D249 and D250 (the test stimuli included animals and tools pictures) using the available tools and calculation methods (correlation coefficient, t-test and association rule mining). The obtained results showed that the frontal and temporal areas had the highest activity in comparison with other areas. The brain behavioral patterns in these subjects were very similar in the three bands of theta, beta and alpha.

      더보기

      참고문헌 (Reference)

      1 Antonenko, P., "Using electroencephalography to measure cognitive load" 22 (22): 425-438, 2010

      2 Allen, E. A., "Tracking whole-brain connectivity dynamics in the resting state" 24 (24): 663-676, 2014

      3 Horwitz, B., "The elusive concept of brain connectivity" 19 (19): 466-470, 2003

      4 Barahimi, S., "STUDIES ON SCHIZOPHRENIA AND DEPRESSIVE DISEASES BASED ON FUNCTIONAL NEAR-INFRARED SPECTROSCOPY" 1830002-, 2018

      5 Sakkalis, V., "Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG" 41 (41): 1110-1117, 2011

      6 Schendan, H. E., "Neurophysiological evidence for visual perceptual categorization of words and faces within 150 ms" 35 (35): 240-251, 1998

      7 Sporns, O., "Networks of the Brain" MIT press 2010

      8 Honey, C. J., "Network structure of cerebral cortex shapes functional connectivity on multiple time scales" 104 (104): 10240-10245, 2007

      9 Zhou, D., "MATLAB toolbox for functional connectivity" 47 (47): 1590-1607, 2009

      10 Einalou, Z., "Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS" 4 (4): 041407-, 2017

      1 Antonenko, P., "Using electroencephalography to measure cognitive load" 22 (22): 425-438, 2010

      2 Allen, E. A., "Tracking whole-brain connectivity dynamics in the resting state" 24 (24): 663-676, 2014

      3 Horwitz, B., "The elusive concept of brain connectivity" 19 (19): 466-470, 2003

      4 Barahimi, S., "STUDIES ON SCHIZOPHRENIA AND DEPRESSIVE DISEASES BASED ON FUNCTIONAL NEAR-INFRARED SPECTROSCOPY" 1830002-, 2018

      5 Sakkalis, V., "Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG" 41 (41): 1110-1117, 2011

      6 Schendan, H. E., "Neurophysiological evidence for visual perceptual categorization of words and faces within 150 ms" 35 (35): 240-251, 1998

      7 Sporns, O., "Networks of the Brain" MIT press 2010

      8 Honey, C. J., "Network structure of cerebral cortex shapes functional connectivity on multiple time scales" 104 (104): 10240-10245, 2007

      9 Zhou, D., "MATLAB toolbox for functional connectivity" 47 (47): 1590-1607, 2009

      10 Einalou, Z., "Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS" 4 (4): 041407-, 2017

      11 Einalou, Z., "Functional near infrared spectroscopy to investigation of functional connectivity in schizophrenia using partial correlation" 2 (2): 5-8, 2014

      12 Einalou, Z., "Functional near infrared spectroscopy for functional connectivity during Stroop test via mutual information" 6 (6): 62-67, 2015

      13 Dadgostar, M., "Functional connectivity of the PFC via partial correlation" 127 (127): 4748-4754, 2016

      14 Friston, K. J., "Functional and effective connectivity in neuroimaging: a synthesis" 2 (2): 56-78, 1994

      15 Babiloni, F., "Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function" 24 (24): 118-131, 2005

      16 Einalou, Z., "Effective channels in classification and functional connectivity pattern of prefrontal cortex by functional near infrared spectroscopy signals" 127 (127): 3271-3275, 2016

      17 Hassan, M., "EEG source connectivity analysis: from dense array recordings to brain networks" 9 (9): e105041-, 2014

      18 Salmelin, R., "Dynamics of brain activation during picture naming" 368 (368): 463-, 1994

      19 Rubinov, M., "Complex network measures of brain onnectivity : uses and interpretations" 52 (52): 1059-1069, 2010

      20 Bullmore, E., "Complex brain networks : graph theoretical analysis of structural and functional systems" 10 (10): 186-, 2009

      21 Watts, D. J., "Collective dynamics of ‘small-world’ networks" 393 (393): 440-, 1998

      22 Dadgostar, M., "Classification of schizophrenia using SVM via fNIRS" 30 (30): 1850008-, 2018

      23 Rizkallah, J., "Brain network modules of meaningful and meaningless objects" 2016

      24 Fallani, F. D. V., "Brain network analysis from high-resolution EEG recordings by the application of theoretical graph indexes" 16 (16): 442-452, 2008

      25 Mheich, A., "A new algorithm for spatiotemporal analysis of brain functional connectivity" 242 : 77-81, 2015

      더보기

      동일학술지(권/호) 다른 논문

      동일학술지 더보기

      더보기

      분석정보

      View

      상세정보조회

      0

      Usage

      원문다운로드

      0

      대출신청

      0

      복사신청

      0

      EDDS신청

      0

      동일 주제 내 활용도 TOP

      더보기

      주제

      연도별 연구동향

      연도별 활용동향

      연관논문

      연구자 네트워크맵

      공동연구자 (7)

      유사연구자 (20) 활용도상위20명

      인용정보 인용지수 설명보기

      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2024 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2021-01-01 평가 등재학술지 선정 (해외등재 학술지 평가) KCI등재
      2020-12-01 평가 등재후보 탈락 (계속평가)
      2019-12-01 평가 등재후보로 하락 (계속평가) KCI등재후보
      2016-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2012-01-01 평가 등재 1차 FAIL (등재유지) KCI등재
      2009-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2008-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2006-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
      더보기

      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.08 0.08 0.08
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.06 0.06 0.337 0
      더보기

      이 자료와 함께 이용한 RISS 자료

      나만을 위한 추천자료

      해외이동버튼