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Neurosci Lett. 2014 Sep 19;580:130-6. doi: 10.1016/j.neulet.2014.07.058. Epub 2014 Aug 8.

Reduction of physiological effects in fNIRS waveforms for efficient brain-state decoding.

Author information

1
Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, Republic of Korea. Electronic address: malik@pusan.ac.kr.
2
Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, Republic of Korea; School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 609-735, Republic of Korea. Electronic address: kshong@pusan.ac.kr.

Abstract

This paper presents a methodology for online estimation of brain activities with reduction in the effects of physiological noises in functional near-infrared spectroscopy signals. The input-output characteristics of a hemodynamic response are modeled as an autoregressive moving average model together with exogenous physical signals (i.e., ARMAX). In contrast to the fixed design matrix in the conventional general linear model, the proposed model incorporates the temporal variations in the experimental paradigm as well as in the hemodynamics. The performance of the proposed method has been tested by using box-car type functions followed by individual tapping tasks. The results and their significance were verified using t-statistics indicating that ARMAX seems to be better able to track/reveal the hemodynamic response. Also, online brain-activation maps were generated for localizing brain activities. Experimental results are compared with those of the existing conventional GLM-based method.

KEYWORDS:

Auto regressive moving average model with exogenous signals; Functional near-infrared spectroscopy; Hemodynamics; Recursive least squares estimate

PMID:
25111978
DOI:
10.1016/j.neulet.2014.07.058
[Indexed for MEDLINE]

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