[논문]Elastic net 기반 특징 선택을 적용한 fNIRS 기반 뇌-컴퓨터 인터페이스 데이터셋 분류 정확도 평가

신재영

doi:10.9718/jber.2021.42.6.268

[국내논문] Elastic net 기반 특징 선택을 적용한 fNIRS 기반 뇌-컴퓨터 인터페이스 데이터셋 분류 정확도 평가
Assessment of Classification Accuracy of fNIRS-Based Brain-computer Interface Dataset Employing Elastic Net-Based Feature Selection 원문보기

Journal of biomedical engineering research : the official journal of the Korean Society of Medical & Biological Engineering, v.42 no.6, 2021년, pp.268 - 276

신재영 (원광대학교 전자공학과)

Abstract ▼ AI-Helper

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

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표/그림 (11)

그림 그림 1. 16개의 fNIRS 채널 구성 (광원: 빨간색, 광검출기: 파란색) Fig. 1. Sixteen fNIRS channel configuration (source: red, detector: blue)
그림 그림 2. 일반화된 분류 정확도를 계산하기 위한 LOOCV Fig. 2. LOOCV for estimating generalized classification accuracy
그림 그림 3. Ch15의 과제 관련 (a) 산소 헤모글로빈(ΔHbO)과 (b) 환원 헤모글로빈(ΔHbR) 농도 변화의 피험자 전체 평균. 음영은 과제 수행 구간(0-10 초)를 나타냄 Fig. 3. Grand average of task-related (a) ΔHbO and (b) ΔHbR in Ch15. Shades indicate a task period (0-10 s)
표 표 1. Elastic net 모수 α의 변화에 따른 분류 정확도(단위: %) Table 1. Classification accuracies according to parameter α in elastic net (unit: %)
표 표 2. Elastic net 모수 α의 변화에 따라 특징이 선택된 평균 비율(단위: %) Table 2. Average of feature selection ratio according to parameter α in elastic net(unit: %)
표 표 3. 선택된 특징의 개수에 따른 분류 정확도 차이의 유의성을 확인하기 위한 일원배치 분산분석 결과표 Table 3. One-way ANOVA result table to test the significance of difference in classification accuracy according to the number of selected features
표 표 4. 특징 선택 방법에 따른 분류 정확도 차이의 유의성을 확인하기 위한 일원배치 분산분석 결과표 Table 4. One-way ANOVA result table to test the significance of difference in classification accuracy according to the feature selection method
그림 그림 4. 모수 α 값에 대한 분류 정확도 상자 그림 Fig. 4. Boxplot of classification accuracies according to parameter α value
그림 그림 5. 특징 선택 방법에 따른 분류 정확도 상자 그림 Fig. 5. Boxplot of classification accuracies according to feature selection method
표 표 5. Tukey HSD사후 분석 결과표(이상치 포함) Table 5. Tukey HSD post-hoc analysis result table (with outlier results)
표 표 6. Duncan 사후 검정 결과에 따른 동질적 부분 집합(이상치 포함) Table 6. Homogeneous subsets according to Duncan post-hoc analysis result(with outlier results)

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