International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Contribution of ERP/EEG Measurements for Monitoring of Neurological Disorders

  • Lamia Bouafif (National Institute of Medical Technologies of Tunis) ;
  • Cherif Adnen (ATSSEE Laboratory, University of Tunis Manar)
  • 투고 : 2024.06.05
  • 발행 : 2024.06.30
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초록

Measurable electrophysiological changes in the scalp are frequently linked to brain activities. These progressions are called related evoked potentials (ERP), which are transient electrical responses recorded by electroencephalography (EEG) in light of tactile, mental, or motor enhancements. This painless strategy is gradually being used as a conclusion and clinical help. In this article, we will talk about the main ways to monitor brain activities in people with neurological diseases like Alzheimer's disease by analyzing EEG signals using ERP. We will also talk about how this method helps to detect the disease at an early stage.

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참고문헌

  1. Niedermeyer, E. (1999). "The beginnings of EEG." Electroencephalography and clinical neurophysiology, 110(6), 1429-1438. 
  2. Adrian, E. D., & Matthews, B. H. (1934). "The Berger rhythm: potential changes from the occipital lobes in man." Brain, 57(4), 355-385. 
  3. Thatcher, R. W., et al. (1996). "Functional neuroimaging using brain electrical activity mapping (BEAM)." Journal of neurotherapy, 1(2), 26-45. 
  4. Thatcher, R. W. (1998). "Normative EEG databases and EEG biofeedback." Journal of neurotherapy, 2(4), 8-39. 
  5. Sarno, J. E. (2002). "Deep brain stimulation for Parkinson's disease." New England Journal of Medicine, 346(22), 1689-1690. 
  6. Debener, S., et al. (2011). "Detection of intentional actions in a paralyzed participant with brain-computer interface." Neurology, 77(13), 1294-1296. 
  7. Liu T, Wang Y, Zhang H, Liu Y, Hao Y, Li L, Li Y, Zhang X, Li X, Zhang T, Gong Q. EEG functional connectivity predicts conversion to Alzheimer's disease. Aging (Albany NY). 2020 ;12 (5):4269-4283. 
  8. Sarno, J. E. (2002). "Deep brain stimulation for Parkinson's disease." New England Journal of Medicine, 346(22), 1689-1690. 
  9. Babiloni, F., et al. (2016). "Functional connectivity of electroencephalographic alpha rhythms in early and late mild cognitive impairment." Journal of Alzheimer's Disease, 51(3), 969-982. 
  10. Das, A., et al. (2019). "EEG-based classification of Alzheimer's disease and mild cognitive impairment using a deep convolutional neural network." Computer Methods and Programs in Biomedicine, 170, 1-9. 
  11. Polich, J. (2007). Updating P300: an integrative theory of P3a and P3b. Clinical neurophysiology, 118(10), 2128-2148. 
  12. Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual review of psychology, 62, 621-647. 
  13. Kuchcinski, G., et al. (2019). "Clinical applications of resting-state functional connectivity MRI in neurological and psychiatric disorders: A review." Clinical Neurophysiology, 129(11), 2222-2236. 
  14. Yoo, Y. H., & Thacker, N. A. (2019). "Magnetic resonance imaging techniques in radiation therapy planning: A review." Journal of Medical Imaging and Radiation Sciences, 50(3), 408-418 
  15. Esteva, A., et al. (2017). "Dermatologist-level classification of skin cancer with deep neural networks." Nature, 542, 115-118. 
  16. Litjens, G., et al. (2017). "A survey on deep learning in medical image analysis." Medical Image Analysis, 42, 60-88. 
  17. McKinney, S. M., et al. (2020). "International evaluation of an AI system for breast cancer screening." Nature, 577(7788), 89-94. 
  18. Liu, Y., et al. (2020). "Artificial intelligence and machine learning in radiology: Opportunities, challenges, pitfalls, and criteria for success." Journal of the American College of Radiology, 17(1), 16-23. 
  19. Caballero, M., et al. (2020). "Machine learning-based prediction of Alzheimer's disease using volumetric MRI data and probabilistic tractography." Frontiers in Neuroscience, 14, 564. 
  20. Li, C., et al. (2020). "Machine learning-based classification of Parkinson's disease from resting-state functional MRI." Neuroscience Letters, 735, 135207. 
  21. Wang, Y., et al. (2017). "Discriminant analysis with neural network for Alzheimer's disease based on multiple features from EEG." Frontiers in Aging Neuroscience, 9, 24 
  22. Pascual-Marqui, R. D., et al. (1999). "Brain electric source imaging from EEG and mega-EEG." IEEE Transactions on Biomedical Engineering, 46(5), 567-571. 
  23. Michel, C. M., et al. (2004). "EEG source imaging." Clinical neurophysiology, 115(10), 2195-2222. 
  24. Makeig, S., et al. (2001). "Independent component analysis ICA of electroencephalographic data." Advances in Neural Information Processing Systems, 13, 145-151. 
  25. Yan, J., et al. (2018). "EEG feature extraction and pattern recognition based on PCA and SVM." In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 2034-2039). IEEE. 
  26. Grosse-Wentrup, M., et al. (2011). "A review on empirical EEG-based brain-computer interfaces: Methods, applications, and challenges." IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(3), 236-258. 
  27. Nunez, P. L., et al. (1997). "EEG coherency II: Experimental comparisons of multiple measures." Clinical Neurophysiology, 108(3), 460-476. 
  28. Weiner, M. W., et al. (2015). "The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial recruitment." Alzheimer's & Dementia, 11(3), 239-242.