Louis Gagnon

TL;DR: The results show that it is always better to correct for motion artifacts than reject trials, and that wavelet filtering is the most effective approach to correcting this type of artifact, reducing the area under the curve where the artifact is present in 93% of the cases, which support previous studies that have shown wave let filtering to be the most promising and powerful technique for the correction of motion artifacts in fNIRS data.

TL;DR: In this article, the authors compare the utility of a variety of motion correction techniques using a simulated functional activation signal added to 20 real NIRS datasets which contain motion artifacts, including spline interpolation, wavelet analysis, and Kalman filtering.

TL;DR: The routine application of motion correction techniques (particularly spline interpolation or wavelet analysis) are recommended to minimize the impact of motion artifacts on functional NIRS data.

TL;DR: This paper measured arbitrage opportunities by comparing the intraday prices and quotes of American Depositary Receipts (ADRs) and other types of cross-listed shares in US markets with synchronous prices of their home-market shares on a currency-adjusted basis for a sample of 506 US cross listed stocks from 35 different countries.

TL;DR: It is shown that the systemic interference occurring in the superficial layers of the human head is inhomogeneous across the surface of the scalp, suggesting that the short separation measurement must be located as close as 1.5 cm from the standard NIRS channel in order to provide an improvement which is of practical use.