Brain machine interface using portable Near-InfraRed spectroscopy - Improvement of classification performance based on ICA analysis and self-proliferating LVQ

• Published in: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 851 - 858
• Main Authors: Ito, Tomotaka, Akiyama, Hideki, Hirano, Tokihisa
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2013
• Subjects: Blood flow; Delays; Fluid flow measurement; Neurons; Probes; Robots; Support vector machine classification
• ISSN: 2153-0858; 2153-0866
• DOI: 10.1109/IROS.2013.6696450

Recently, the Brain-Machine Interface (BMI) has been expected to be applied to robotics and medical science field as a new intuitive interface. BMI measures human cerebral activities and uses them directly as an input signal to various instruments. The future goal of our research is to design a practical BMI system that can be used reliably in daily lives. In this paper, we will discuss a design method of a BMI system using a portable Near-InfraRed Spectroscopy (NIRS) device and then we will consider improving the performance of the learning vector quantization (LVQ) classifier by using the independent component analysis (ICA) and the self-proliferating function of neurons. The effectiveness of the proposed method is investigated in human imagery classification experiments.