Patterns Analysis of Prefrontal Brain Waves of Cancer Patients using Brain-Computer-Interface

뇌-컴퓨터-인터페이스를 이용한 암환자들의 전전두엽 뇌파 분석

  • 한영수 (서울벤처정보대학원대학교 정보경영학과) ;
  • 채명신 (서울벤처정보대학원대학교 정보경영학과) ;
  • 박병운 (서울불교대학원대학교 심신통합치유학과) ;
  • 박종기 (에덴요양병원)
  • Published : 2008.03.15

Abstract

Cancer patients have been suffered from the instability of mind/body and unbalanced homeostasis because of cancer progression and medical treatment such as chemotherapy, It is very important that appropriated actions can be promptly taken by monitoring cancer patients' mental conditions. For this reason, it is crucial to develop a monitoring method which is convenient and not harmful to their body. Brain-computer-interface(BCI) system is introduced for the purpose in this paper. Prefrontal brain waves of cancer patients and control groups have been measured by a portable neurofeedback(NF) system based on self-regulation of the human electroencephalogram(EEG). The NF system consists of the portable EEG amplifier and a headband with dry electrodes placed on Fp1 and Fp2 sites. Patterns of the prefrontal brain waves taken by computer are correlated to brain quotients by EEG-analysis program. Basic rhythm quotient, attention quotient, emotional quotient, anti-stress quotient and correlation quotient of control group have shown high significant level compared with the cancer patients group. On the other hand, the EEG patterns analysis is shown its possibility to be an important methodology of monitoring cancer patients' condition.

암환자들은 암의 진행과 항암화학요법 등의 치료로 인해 심신의 불안정과 항상성의 저하로 큰 고통을 겪고 있다. 간편하면서 인체에 아무 해를 주지 않는 뇌파를 기반으로 하는 뇌-컴퓨터-인터페이스(BCI) 기술로서 암 환자의 상태를 모니터링하여 적절한 처치를 취할 수 있다는 것은 매우 중요한 일이다. 암환자들의 전전두엽에 헤드밴드 형태의 건성전극단자를 부착하고, 컴퓨터와 연결된 휴대용 뇌파측정 장치로 전전두엽 뇌파(Fp1, Fp2)를 측정하였다. 컴퓨터를 통하여 파장대 별로 얻어진 뇌파를 상호 연관성에 따라 뇌지수로 구분한 후 통계 처리하여 유의성을 검증하였다. 암환자군과 정상대조군을 비교한 결과 암환자군에 비하여 정상대조군이 기초율동지수, 주의지수, 정서지수, 항스트레지수와 좌우뇌균형지수에서 유의하게 높은 차이를 나타냈다. 따라서 뇌파 측정이 환자의 상태를 모니터링하는 중요한 도구로서의 가능성을 보였다.

Keywords

References

  1. 신승철, 류창수, 송윤선, 남승훈, "뇌-컴퓨터-인터페이스를 위한 EEG 기반의 피험자 반 응시간 감지", 정보과학회논문지: 소프트웨어 및 응용, 제29권, 제11호, pp. 837-850, 2002
  2. 신정훈, 서은미, "BCI 기반 Entertainic 기술개발 동향", 전자공학회지, 제34권, 제6호, pp. 679-690, 2007
  3. 음태완, 김응수, "뇌파기반 뇌-컴퓨터 인터페이스 기술", 정보과학회지, 제22권, 제2호, 2004
  4. Miller, K. E., Cohen, D. J., "An Integrative Theory of Prefrontal Cortex Function," Annual Review of Neuroscience, Vol.24, pp. 167-202, 2001 https://doi.org/10.1146/annurev.neuro.24.1.167
  5. Nuwer Mostofsky, S.H., Reiss, A.L., Lockhart, P., and Penckla, M.B., "Evaluation of Cerebellar Size in ADHD," Journal of Child. Neurology, Vol.13, pp. 434-439, 1998 https://doi.org/10.1177/088307389801300904
  6. 정용안, 유이영, 강봉주, 채정호, 이혜원, 문현진, "치료 저항성 우울증 환자에서 반복적 경두개 자기자극 후 국소뇌혈류 변화", Nucl. Med. Mol. Imaging, Vol.41, No.1, pp. 9-15, 2007
  7. Raz, N., Aging of the Brain and Its Impact on Cognitive Performance: Integration of Structural and Functional Findings. In F. I. M. Craik & T. A. Salthouse(Eds.), Handbook of Aging and Cognition II. NJ: Erlbaum. 2000
  8. West, R. L., "An application of prefrontal cortex function theory to cognitive aging," Psychological Bulletin, Vol.120, pp. 272-292, 1996 https://doi.org/10.1037/0033-2909.120.2.272
  9. Osselton, J.W., "Electroencephalographic Monitoring in Epilepsy," Clin. Phys. Physiol. Meas, Vol.12, No.3, pp. 203-217, 1991 https://doi.org/10.1088/0143-0815/12/3/001
  10. Monastra, V.J., Lubar, J.F., Linden, M., Deusen, P.V., Green, G., and Wing, W, "Assessing Atten tion Deficit Hyperactivity Disorder via Quantitative Electroencephalography: An Initial Validation Study," Neuropsychology, Vol.13, No.3, pp. 424-433, 1999 https://doi.org/10.1037/0894-4105.13.3.424
  11. 곽용태, "알쯔하이머병의 진행에 따른 정량적 뇌파검사의 변화", J Korean Neurol Assoc, Vol.23 No.3, pp. 356-362, 2005
  12. Fingelkurts, A.A., Fingelkurts, An.A., Rytsala, H., Suominen, K., Isometsa, E., and Kahkonen, S. "Composition of Brain Oscillations in Ongoing EEG during Major Depression Disorder," Neuroscience Research, Vol.56, No.2, pp. 133-144, 2006 https://doi.org/10.1016/j.neures.2006.06.006
  13. Soikkeli R., Partanen J., "Slowing of EEG in Parkinson's disease," Electroencephalogram Clin. Neurophysiology, Vol.79, pp. 159-165, 1991 https://doi.org/10.1016/0013-4694(91)90134-P
  14. Ferrier, C.H., Eleanora Aronica, E., Leijten, F.S.S., Spliet, W.G.M., Huffelen, A.C., and Rijen, P.C., "Electrocorticographic Discharge patterns in Glioneuronal Tumors and Focal Cortical," Dysplasia Epilepsia, Vol.47, No.9, pp. 1477-1486, 2006 https://doi.org/10.1111/j.1528-1167.2006.00619.x
  15. Walter, D.O., Leuchter, A.F. A Tourial on Classical Computer Analysis of EEGs: Spectra and Coherences in Analysis of the Electrical Activity of the Brain, ed. by Angeleri F., Butler S., Giaquinto S., Majkowski J. Wiley & Sons pp. 105-124. 1997
  16. Bablyoantz, A., Salazar, J.M., and Nicolis, C., "Evidence of Chaotic Dynamics of Brain Activity during the Sleep Cycle," Phys. Lett., Vol. 111A, pp. 152-156, 1985
  17. Jeong, J., Kim, S.Y., and Han, S.H., "Nonlinear Analysis of Chaotic Dynamics underlying EEGs in Patients with Alzheimer's Disease," Electroencephalography and Clinical Neurophysiology, Vol.106, No.3, pp. 220-228, 1998 https://doi.org/10.1016/S0013-4694(97)00079-5
  18. Comon, P., "Independent Component Analysis - A New Concept?," Signal Processing, Vol.36, No.3, pp. 287-314, 1994 https://doi.org/10.1016/0165-1684(94)90029-9
  19. 박병운, 뇌파해석기법, 한국정신과학연구소, 2005
  20. Ryu, C.S, An, M.H, Na, Y.C, Cho, J.O, Han, Y.S, Kim, K.H, and Park, P.W., "A Portable Neurofeedback System and EEG-analysis Methods for Evaluation," World Congress on Medical physics and Biomedical Engineering Proceeding, pp. 1060- 1062, 2006
  21. 김대식, 최장욱, 뇌파 검사학, pp. 89-90, 2001
  22. 이강희, 민윤기, 이방형, 민병찬, "뇌파유도 및 모니터링 인터페이스 시스템 개발 및 효 과성", 한국감성학회 춘계학술대회 및 국제 감성공학 심포지움 발표자료, pp. 91-96, 2000
  23. Jasper, H.H, "The ten-twenty electrode system of the International Federation," Electroencephalography and Clinical Neurophysiology, Vol.56, No.6, pp. 898-902, 1958
  24. Bruce, J. Fisch. Fisch and Spehlmann's EEG Primer. 3rd Ed., pp. 141-198, Elsevier, 1999
  25. Lubar, J.F, Swartwood, M.O, Swartwood, J.N, and O'Donnell, P.H., "Evaluation of the Effectiveness of EEG Neurofeedback Training for ADHD in a Clinical Setting as Measured by Changes in T.O.V.A. Scores, Behavioral Rating, and WISC-R Performance," Biofeedback & Self Regulation, Vol. 20, pp. 83-99, 1995 https://doi.org/10.1007/BF01712768
  26. Lubar, J.O., Lubar, J.F., "Electroencephalographic Biofeedback of SMR and Beta for Treatment of Attention Deficit Disorders in a Clinical Setting," Biofeedback & Self Regulation, Vol.9, pp. 1-23, 1984 https://doi.org/10.1007/BF00998842
  27. Gray, JA., "Brain Systems that Mediate both Emotion and Cognition. Special Issue: Development of Relationships between Emotion and Cognition," Cognition and Emotion, Vol.4, pp. 269-288, 1990 https://doi.org/10.1080/02699939008410799
  28. Gotlib, I.A, Ranganath, C, and Rosenfeld, J.P., "Frontal EEG Alpha Asymmetry, Depression and Cognitive Functioning," Cognition and Emotion, Vol.12, pp. 449-478, 1998 https://doi.org/10.1080/026999398379673
  29. Sutton, S.K, Davidson, R.J., "Prefrontal Brain Asymmetry: A Biological Substrate of the Behavioral Approach and Inhibition Systems," Psychological Science, Vol.8, pp. 204-210, 1997 https://doi.org/10.1111/j.1467-9280.1997.tb00413.x
  30. Peniston, E.G, Marrinan, D.A, Deming, W.A, and Kulkosky, P.J, "EEG Alpha-theta Brainwave Synchronization in Vietnam Theater Veteran with Combat-related Posttraumatic Stress Disorder and Alcohol Abuse," Medical Psychotherapy: An International Journal, Vol.6, pp. 37-50, 1993