Classiï¬cation Tree Performance Analysis on ICA-based Functional Near-infrared Spectroscopy Signals
Abstract
Abstract—This paper proposes a method to classify between the clean and the contaminated signal by motion artifact (MA) signal on functional near-infrared spectroscopy (fNIRS) signals, by extracting the signal features based on independent component analysis(ICA)andstatisticalmodelsusingclassiï¬cationtreeasthe classiï¬er. The extracted features such as kurtosis, skewness, mean, variance, standard deviation, interquartile range, and weight vector are used in a classiï¬cation tree as the prediction model for class classiï¬cation. The result of this paper is to acknowledge the performance of classiï¬cation tree to classify the fNIRS signals, which results in 88.9% accuracy, 81% sensitivity, 100% speciï¬city, and 0.83 value of area under convergence (AUC). Index Terms—fNIRS, ICA, feature extraction, classiï¬cation tree
