Systematic Analysis of Optimal Feature Extraction Methods for Developing a Near-Infrared Spectroscopy-Based Brain-Computer Interface System

Jaeyoung Shin, Han-Jeong Hwang

http://doi.org/10.5626/JOK.2018.45.10.1080

In this study, we systematically investigated optimal feature extraction methods for developing a near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) by considering various analysis time periods and feature combinations. While twelve subjects performed mental arithmetic and resting tasks for 10 s 30 times each, NIRS signals were measured. Seven types of different features were extracted from the NIRS signals, and classification accuracies were calculated using individual feature types extracted from 0-10 and 0-15 s single analysis periods and feature combinations extracted from 0-15 s analysis period that was divided into three time periods (0-5, 5-10, 10-15 s), respectively. As a result, the highest classification accuracy was obtained when the combination of different feature types extracted from a 0-15 s analysis period divided into the three periods was used, and it was confirmed that the combinations of mean and slope features were considered the most suitable for developing a NIRS-based BCI system.