Recovering fNIRS brain signals: physiological interference suppression with independent component analysis

Y. Zhang; M. Shi; J. Sun; C. Yang; Yajuan Zhang; F. Scopesi; P. Makobore; C. Chin; G. Serra; Y. A. B. D. Wickramasinghe; P. Rolfe

doi:10.1117/12.2083483

6 March 2015 Recovering fNIRS brain signals: physiological interference suppression with independent component analysis

Y. Zhang, M. Shi, J. Sun, C. Yang, Yajuan Zhang, F. Scopesi, P. Makobore, C. Chin, G. Serra, Y. A. B. D. Wickramasinghe, P. Rolfe

Author Affiliations +

Proceedings Volume 9446, Ninth International Symposium on Precision Engineering Measurement and Instrumentation; 944604 (2015) https://doi.org/10.1117/12.2083483
Event: International Symposium on Precision Engineering Measurement and Instrumentation, 2014, Changsha/Zhangjiajie, China

Abstract

Brain activity can be monitored non-invasively by functional near-infrared spectroscopy (fNIRS), which has several advantages in comparison with other methods, such as flexibility, portability, low cost and fewer physical restrictions. However, in practice fNIRS measurements are often contaminated by physiological interference arising from cardiac contraction, breathing and blood pressure fluctuations, thereby severely limiting the utility of the method. Hence, further improvement is necessary to reduce or eliminate such interference in order that the evoked brain activity information can be extracted reliably from fNIRS data. In the present paper, the multi-distance fNIRS probe configuration has been adopted. The short-distance fNIRS measurement is treated as the virtual channel and the long-distance fNIRS measurement is treated as the measurement channel. Independent component analysis (ICA) is employed for the fNIRS recordings to separate the brain signals and the interference. Least-absolute deviation (LAD) estimator is employed to recover the brain activity signals. We also utilized Monte Carlo simulations based on a five-layer model of the adult human head to evaluate our methodology. The results demonstrate that the ICA algorithm has the potential to separate physiological interference in fNIRS data and the LAD estimator could be a useful criterion to recover the brain activity signals.

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Y. Zhang, M. Shi, J. Sun, C. Yang, Yajuan Zhang, F. Scopesi, P. Makobore, C. Chin, G. Serra, Y. A. B. D. Wickramasinghe, and P. Rolfe "Recovering fNIRS brain signals: physiological interference suppression with independent component analysis", Proc. SPIE 9446, Ninth International Symposium on Precision Engineering Measurement and Instrumentation, 944604 (6 March 2015); https://doi.org/10.1117/12.2083483

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KEYWORDS

Independent component analysis

Brain

Monte Carlo methods

Hemodynamics

Near infrared spectroscopy

Digital filtering

Head

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