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비지도학습 머신러닝에 기반한 베타파 상관관계 분석모델

Beta-wave Correlation Analysis Model based on Unsupervised Machine Learning

  • 최성자 (가천대학교 소프트웨어 중심대학)
  • Choi, Sung-Ja (Department of software, College of IT, Gachon University)
  • 투고 : 2019.01.07
  • 심사 : 2019.03.20
  • 발행 : 2019.03.28

초록

뇌파 파형중 베타파를 이용한 인간의 인지상태를 판별한다. 베타파는 인간의 인지상태중 스트레스 영역에 해당하는 특성이 있고, 이 영역에서 스트레스의 오버대역폭을 추출하기 위해서 저대역폭과 고대역폭 사이의 베타파간 상관관계를 분석해야 한다. 그러므로 본 논문에서는 효과적으로 베타파 상관관계를 분석하고 추출하기 위해 비지도학습 머신러닝을 이용한 Kmean 클러스터링 분석모델을 제시한다. 제시된 모델은 베타파 영역을 유사한 영역의 클러스터 군으로 분류하고 해당 클러스터링 범주에서 이상파형을 판별한다. 이상파형 판별을 위해 클러스터군의 밀집도와 정상범주 이탈영역을 기준으로 스트레스 위험군을 판별하고 판별된 스트레스 위험군에 대한 대처방안을 제공할 수 있다. 제시된 모델을 활용하면 뇌파파형을 통한 인지상태의 스트레스 지수분별이 가능하고, 개인의 인지상태에 대한 관리 및 응용이 가능하다. 또한 스트레스와 오피스증후군을 갖는 사람들에게 뇌파관리를 통해 개인의 삶에 대한 질적 향상에 도움을 준다.

The characteristic of the beta wave among the EEG waves corresponds to the stress area of human perception. The over-bandwidth of the stress is extracted by analyzing the beta-wave correlation between the low-bandwidth and high-bandwidth. We present a KMeans clustering analysis model for unsupervised machine learning to construct an analytical model for analyzing and extracting the beta-wave correlation. The proposed model classifies the beta wave region into clusters of similar regions and identifies anomalous waveforms in the corresponding clustering category. The abnormal group of waveform clusters and the normal category leaving region are discriminated from the stress risk group. Using this model, it is possible to discriminate the degree of stress of the cognitive state through the EEG waveform, and it is possible to manage and apply the cognitive state of the individual.

키워드

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Fig. 1. Processing for KMeans clustering

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Fig. 2. KMean model for beta-wave corelation

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Fig. 3. Brainwave analyzer on spark

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Fig. 4. Analyzer of beta-wave acquisitions dataset

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Fig. 5. KMeans model applied for beta-wave dataset

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Fig. 6. Verification about KMeans model of optimized k-value in beta-wave dataset

Table 1. Frequency bands of brainwave

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Table 2. Algorithm for beta wave analyze model

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Table 3. Using tools for the platform of construction

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참고문헌

  1. E. A. Larsen. (2011). Classification of EEG signals in a brain-computer interface system, Master's thesis, Institutt for datateknikk og informasjonsvitenskap.
  2. Y. P. Ma et al. (2014, August). Emotional identification during mobile RF Radiation. In Control and System Graduate Research Colloquium (ICSGRC), 2014 IEEE 5th (pp. 285-289). IEEE.
  3. R. S. S. A. Kadir et al. (2009, March). Analysis of correlation between body mass index (BMI) and brain wave using EEG for Alpha and Beta frequency band. In Signal Processing & Its Applications, 278-283.
  4. K. Crowley, A. Sliney, I. Pitt & D. Murphy. (2010, July). Evaluating a brain-computer interface to categorise human emotional response. In Advanced Learning Technologies (ICALT), 2010 IEEE 10th International Conference on (pp. 276-278).
  5. N. Sulaiman et al. (2012, February). Development of EEG-based stress index. In Biomedical Engineering (ICoBE), 2012 International Conference on (pp. 461-466). IEEE.
  6. P. Reanaree, P. Tananchana, W. Narongwongwathana & C. Pintavirooj. (2016, December). Stress and office-syndrome detection using EEG, HRV and hand movement. In Biomedical Engineering International Conference (BMEiCON), (pp. 1-4). IEEE.
  7. T. Teramae, D. Kushida, F. Takemori & A. Kitamura. (2010, August). Estimation of Feeling Based on EEG by Using NN and k-means Algorithm for Massage System. In SICE Annual Conference 2010, Proceedings of (pp. 1542-1547). IEEE.
  8. A. Azhari & L. Hernandez. (2016). Brainwaves feature classification by applying K-Means clustering using single-sensor EEG. International Journal of Advances in Intelligent Informatics, 2(3), 167-173. https://doi.org/10.26555/ijain.v2i3.86
  9. J. Han, J. Pei & M. Kamber. (2011). Data mining: concepts and techniques. Elsevier.
  10. S. J. Choi & B. G. Kang. (2014). Prototype design and implementation of an automatic control system based on a BCI. Wireless personal communications, 79(4), 2551-2563. https://doi.org/10.1007/s11277-014-1861-5
  11. S. J. Choi, B. G. Kang & G. J. Kim. (2018). The Brainwave Analyzer of Server System Applied Security Functions. Journal of Digital Convergence, 16(12), 343-349. https://doi.org/10.14400/JDC.2018.16.12.343
  12. L. Li & D. Yao. (2007). A new method of spatio-temporal topographic mapping by correlation coefficient of k-means cluster. Brain topography, 19(4), 161-176. https://doi.org/10.1007/s10548-006-0017-7
  13. J. H. Yang, Y. S. Park & S. H. Lee. (2017). Text extraction in images using simplify color and edges pattern analysis. Journal of the Korea Convergence Society, 8(8), 33-40. https://doi.org/10.15207/JKCS.2017.8.8.033
  14. I. K. Lim, D. J. Park & H. J. Cho. (2018). Development of Procurement Announcement Analysis Support System. Journal of the Korea Convergence Society, 9(8), 53-60. https://doi.org/10.15207/JKCS.2018.9.8.053
  15. M. A. Alsheikh, D. Niyato, S. Lin, H. P. Tan & Z. Han. (2016). Mobile big data analytics using deep learning and apache spark. IEEE network, 30(3), 22-29. https://doi.org/10.1109/MNET.2016.7474340
  16. D. Aloise, A. Deshpande, P. Hansen & P. Popat. (2009). NP-hardness of Euclidean sum-of-squares clustering. Machine learning, 75(2), 245-248. https://doi.org/10.1007/s10994-009-5103-0
  17. S. Dasgupta & Y. Freund. (2009). Random projection trees for vector quantization. IEEE Transactions on Information Theory, 55(7), 3229-3242. https://doi.org/10.1109/TIT.2009.2021326