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목차 1
EEG 신호의 확장된 공분산 행렬을 이용한 표적 영상 탐지 = Target image detection using extended covariance matrix of EEG signals / 이기배 ; 고건혁 ; 이종현 1
요약 1
Abstract 1
Ⅰ. 서론 1
Ⅱ. EEG 신호 특징 추출 알고리즘 2
1. 확장된 공분산 행렬 2
2. Riemannian geometry 3
3. 특징 추출 3
Ⅲ. 청크 내 표적 탐지 모델 4
Ⅳ. 실험 및 결과 4
1. 뇌파 데이터 수집 4
2. 특징 추출 및 탐지 모델 구현 6
3. 표적 탐지 결과 6
Ⅴ. 결론 8
REFERENCES 9
저자소개 10
번호 | 참고문헌 | 국회도서관 소장유무 |
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1 | F. Lotte, L. Bougrain, A. Cichocki, M. Clerc, M. Conged, A. Rakotomamnjy, and F. Yger, “A review of classification algorithms for EEG-based brain-computer interfaces: a 10-year update,” Journal of Neurla Engineering, vol. 15, April 2018. | 미소장 |
2 | K. Lee, C. H. Lee, J. Bae, and J. Lee, “EEG signal classification algorithm based on DWT and SVM for driving robot contral,” Journal of The Institute of Electronics and Information Engineers(IEIE), vol. 52, no. 8, pp. 1519-1527, August 2015. | 미소장 |
3 | M. Lee, Y. H. Kim, and S. W. Lee, “Motor impairment in stroke patients is associated with network properties during consecutive motor imagery, IEEE Transactions on Biomedical Engineering, vol. 69, no. 8, pp. 2604-2614, August 2022. | 미소장 |
4 | M. S. Treder, and B. Blankerts, “(C)overt attention and visual speller design in an ERP-based brain-computer interface,”Behavioral and Brain Functions, vol. 6, no. 28, May 2010. | 미소장 |
5 | A. R. Marathe, A. J. Ries, and K. McDowell, “Sliding HDCA: Single-trial EEG classification to overcome and quantify temoral variability,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 2, pp. 201-211, March 2014. | 미소장 |
6 | Y. Lee, G. Shin, M. Lee, and S. Lee, “Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running,” Scientific Data, vol. 8, no. 315, December 2021. | 미소장 |
7 | H. Cecotti, M. P. Eckstein, and B. Giesbrecht, “Single-tiral classification of event-related potentials in rapid serial visual presentation tasks using supervised spatial filtering,” IEEE Transactions on Neural Network and Learning Systems, vol. 25 no. 11, pp. 2030-2042, November 2014. | 미소장 |
8 | K. Yu, H. A. Nashash, N. Thakor, and X. Li, “The analytic bilinear discrimination of single-trial EEG signals in rapid image triage,” PLOS ONE, vol. 9, no. 6, June 2014. | 미소장 |
9 | A. R. Marathe, A. J. Ries, V. J. Lawhern, B. J. Lance, J. Touryan, K. McDowell, and H. Cecotti, “The effect of target and non-target similarity on neural classification performance: a boost from confidence,”Frontiers in Neuroscience, vol. 9, August 2015. | 미소장 |
10 | A. R. Marathe, V. J. Lawhern, D. Wu, D. Slayback, and B. J. Lance, “Improved neural signal claissification in a rapid serial visual presentation task using active learning,”IEEE Transactions on Neural Systems aand Rehabilitation Engineering, vol. 24, no. 3, pp. 333-343, March 2016. | 미소장 |
11 | S. Lees, N. Dayan, H. Cecotti, P. McCullagh, L. Maguire, F. Lotte, and D. Coyle, “A review of rapid serial visual presentationbased brain-computer interfaces,” Journal of Neural Engineering, vol. 15, January 2018. | 미소장 |
12 | B. Rivet, A. Souloumiac, V. Attina, and G. Gibert, “xDAWN algorithm to enhance evoked potentials: Application to braincomputer interface,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 8, pp. 2035-2043, August 2009. | 미소장 |
13 | H. Cecotti, and A. J. Ries, “Best practive for single-trial detection of event-related potentials: Application to brain-computer interfaces,” International Journal of Psychophysiology, vol. 111, pp. 156-169, January 2017. | 미소장 |
14 | H. Zhang, Z. Wang, Y. Yu, H. Yin, C. Chen, and H. Wang, “An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task,” Brain Science Advances, vol. 8, no. 2, pp. 111-126, March 2022. | 미소장 |
15 | A. Barachant, and M. Congedo, “A plug&play P300 BCI using information geometry,” arXiv preprint: 1409.0107, August 2014. | 미소장 |
16 | A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, “Multiclass brain-computer interface classification by Riemannian geometry,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, pp. 920-928, April 2012. | 미소장 |
17 | L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Monatavon, W. Samek, M. Kloft, T. G. Dietterich, and K. R. Muller, “A unifying review of deep and shallow anomaly detection,” Proceedings of the IEEE, vol. 109, no. 5, pp. 756-795, February 2021. | 미소장 |
18 | R. M. Rangayyan, Biomedical signal analysis:Acase study approach, John Wiley & Sons, 2001. | 미소장 |
19 | P. Zanini, M. Congedo, C. Jutten, S. Said, and Y. Berthoumieu, “Transfer learning: A Riemannian geometry framework with applications to brain-computer interfaces,”IEEE Transactions on Biomedical Engineering, vol. 65, no. 5, pp. 1107-1116, May 2018. | 미소장 |
20 | A. F. Emmott, S. Das, T. Dietterich, A. Fern, and W. K. Wong, “Systematic construction of anomaly detection benchmarks from real data,” in Proceeding ACM SIGKDD Workshop Outlier Detection Description (ODD), Chicago, IL, USA, pp. 16-21, August 2013. | 미소장 |
21 | P. Zanini, M. Congedo, C. Jutten, S. Said, and Y. Berthoumieu, “Transfer learning: A Riemannian geometry framework with applications to brain-computer interfaces,”IEEE Transactions on Biomedical Engineering, vol. 65, no. 5, pp. 1107-1116, May 2018. | 미소장 |
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