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

Author

Ito, Takao ; Akiyama, Hidenori ; Hirano, Takuichi

Author_Institution

Dept. of Mech. Eng., Shizuoka Univ., Hamamatsu, Japan

fYear

2013

fDate

3-7 Nov. 2013

Firstpage

851

Lastpage

858

Abstract

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.

Keywords

brain-computer interfaces; image classification; independent component analysis; BMI system; ICA analysis; NIRS; brain machine interface; classification performance; human cerebral activities; human imagery classification; independent component analysis; learning vector quantization; medical science field; portable near-infrared spectroscopy; robotics; self-proliferating LVQ classifier; Blood flow; Delays; Fluid flow measurement; Neurons; Probes; Robots; Support vector machine classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on

Conference_Location

Tokyo

ISSN

2153-0858

Type

conf

DOI

10.1109/IROS.2013.6696450

Filename

6696450