Sparse factorization proprecessing-based offline analysis for a cursor control experiment

Yuanqing Li, Andrzej Cichocki, Cuntai Guan, Jianzhao Qin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

As a communication interface translating brain activities into a control signal for devices like computers, brain-computer interfaces (BCI) have received more and more attentions in recent years due to many potential applications. It is well known that preprocessing (e.g., filtering, etc.) of EEG signals plays an important role in EEG based BCI. In this paper, a sparse factorization approach is presented as a new kind of preprocessing method for BCI. Next, we define power feature vectors related to μ and β frequency bands of these components, and use regularized Fisher discriminant method for classification. Our off line analysis based on the data of a cursor control experiment shows that sparse factorization preprocessing can improve considerably accuracy rate in comparison to PCA or ICA preprocessing.

Original languageEnglish
Title of host publication2004 IEEE International Workshop on Biomedical Circuits and Systems
PagesS3.5.INV-5-8
Publication statusPublished - 2004
Externally publishedYes
Event2004 IEEE International Workshop on Biomedical Circuits and Systems - Singapore, Singapore
Duration: 1 Dec 20043 Dec 2004

Publication series

Name2004 IEEE International Workshop on Biomedical Circuits and Systems

Conference

Conference2004 IEEE International Workshop on Biomedical Circuits and Systems
Country/TerritorySingapore
CitySingapore
Period1/12/043/12/04

Fingerprint

Dive into the research topics of 'Sparse factorization proprecessing-based offline analysis for a cursor control experiment'. Together they form a unique fingerprint.

Cite this