Efficient Mental Arithmetic Task Classification using Wavelet Domain Statistical Features and SVM Classifier

Functional Near Infrared Spectroscopy (fNIRS) has been emerged as a potential technique in the research of BCI. In this paper, we proposed a discrete wavelet transform based feature extraction technique to classify mental arithmetic tasks from fNIRS data. In order to investigate the change in brain activities during mental arithmetic task, recorded data are windowed in several frames. DWT has been employed on different channels of each frame and then a number of statistical features are extracted from both the approximate and the detail coefficients of data in order to distinguish the mental arithmetic task and the rest condition. Six-fold cross validation is performed using SVM classifier to examine the effectiveness of DWT based features. Efficacy of oxyhemoglobin, deoxyhemoglobin, and total hemoglobin data from different selected channel combinations are also examined. It is observed that proposed algorithm provides a satisfactory accuracy of 93.26% using DWT based features extracted from 104 channels.