用于P300脑机接口系统的单试验独立成分分析

Single trial independent component analysis for P300 BCI system.

作者信息

Li Kun, Sankar Ravi, Arbel Yael, Donchin Emanuel

机构信息

Electrical Engineering Department, University of South Florida, Tampa, FL 33620-5350, USA.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4035-8. doi: 10.1109/IEMBS.2009.5333745.

DOI:10.1109/IEMBS.2009.5333745

PMID:19964338

Abstract

A Brain Computer Interface (BCI) is a device that allows the user to communicate with the world without utilizing voluntary muscle activity (i.e., using only the electrical activity of the brain). It makes use of the well-studied observation that the brain reacts differently to different stimuli, as a function of the level of attention allotted to the stimulus stream and the specific processing triggered by the stimulus. In this article we present a single trial independent component analysis (ICA) method that is working with a BCI system proposed by Farwell and Donchin. It can dramatically reduce the signal processing time and improve the data communicating rate. This ICA method achieved 76.67% accuracy on single trial P300 response identification.

摘要

脑机接口（BCI）是一种允许用户在不利用自主肌肉活动（即仅使用大脑的电活动）的情况下与外界进行通信的设备。它利用了经过充分研究的观察结果，即大脑对不同刺激的反应不同，这取决于分配给刺激流的注意力水平以及刺激触发的特定处理过程。在本文中，我们提出了一种单试验独立成分分析（ICA）方法，该方法与法威尔和唐钦提出的BCI系统配合使用。它可以显著减少信号处理时间并提高数据通信速率。这种ICA方法在单试验P300反应识别上达到了76.67%的准确率。