Abstract:
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.
