fNIRS DATA

Published: 27 November 2020| Version 1 | DOI: 10.17632/jhv73gj2d2.1
Contributor:
shixian liu

Description

The data included stroke patients and normal subjects. When they performed motions of hand, the prefrontal hemodynamic data were recorded.

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Discrete cosine transform (DCT) was used for filtering. The low pass filter was used to remove physiological interference, and the high cut-off frequency was set to 0.1 Hz. The high pass filter was used to remove baseline drift, and the low cut-off frequency was set to 0.004 Hz. Concentration was calculated by Modified Beer-lambert Law (MBLL). Further, after paired-sample t-test, there was no significant difference between the left and right hands of normal subjects, so the two were compared as a group. And then activation rate of affected hand, activation rate of unaffected hand and activation rate of hand of normal subjects were compared with one-way ANOVAs. Bonferroni’s multiple comparisons test was used to compare the differences between any two of three groups. All statistical significance was set at p<0.05. Finally, time series relative change values, ΔHbO_2 and ΔHbT was divided into several blocks with the length of one second. Classification using SVM with RBF kernel was performed to distinguish 5 tasks. Two-thirds of the data was used for training, and the rest for prediction. Because the number of tasks is 5, the BCI needed to be designed as a multi-classifier. However, SVM is a two-classification algorithm, so that we adopted a one-versus-one strategy, which is a designed SVM between any two samples. Therefore, a N(N-1)/2 SVM should be designed for N class samples. When a predicted sample is classified, the category with the most votes is the class of the predicted sample.

Categories

Functional near Infrared Spectroscopy

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