Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Beta-wave Correlation Analysis Model based on Unsupervised Machine Learning

비지도학습 머신러닝에 기반한 베타파 상관관계 분석모델

  • Choi, Sung-Ja (Department of software, College of IT, Gachon University)
  • 최성자 (가천대학교 소프트웨어 중심대학)
  • Received : 2019.01.07
  • Accepted : 2019.03.20
  • Published : 2019.03.28
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Abstract

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.

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.

Keywords

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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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