지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiv- ing 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 for- ward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.
목차
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참고문헌 (36)
참고문헌 신청
Boas DA / 2014 / Twenty years of functional near-infrared spectroscopy: introduction for the special issue 85(Part 1):1~5
Scholkmann F / 2014 / A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology 85(Part 1):6~27
Quaresima V / 2019 / A mini-review on functional nearinfrared spectroscopy (fNIRS): where do we stand, and where should we go? 6(3):87
Nazeer H / 2020 / Enhancing classification accuracy of fNIRS-BCI using features acquired from vector-based phase analysis 17(5):056025
Qureshi NK / 2017 / Enhancing classification performance of functional near-infrared spectroscopy-brain–computer interface using adaptive estimation of general linear model coefficients 11(33)
Scholkmann F / 2014 / A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology 85(Part 1):6~27
Quaresima V / 2019 / A mini-review on functional nearinfrared spectroscopy (fNIRS): where do we stand, and where should we go? 6(3):87
Nazeer H / 2020 / Enhancing classification accuracy of fNIRS-BCI using features acquired from vector-based phase analysis 17(5):056025
Qureshi NK / 2017 / Enhancing classification performance of functional near-infrared spectroscopy-brain–computer interface using adaptive estimation of general linear model coefficients 11(33)
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