Fractal dimension for detection of ERD/ERS patterns in asynchronous brain computer interface

Elnaz Banan Sadeghian, Mohammad Hassan Moradi

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

10 Scopus citations

Abstract

Detection of motor-related events is the key issue in asynchronous Brain-Computer Interface design. In this study we exploited for the first time Katz's fractal dimension for detection of motor related changes characterized by ERD/ERS patterns in Electroencephalogram signal. Our observation was that the activation/deactivation of brain's cortical neural systems, during occurrence of motor activity, changes the complexity or randomness of spontaneous EEG and can be quantified accurately with fractal dimension. Furthermore, we applied a Cross-Correlation Template Matching (CCTM) method on the extracted features to combine the energy changes of both ERD and ERS patterns. This combination boosts the system capability and rapidity in motor activity detection. Evaluations of our proposed method shows advantage compared to entropy features extracted in [2], and reveals true positive rates of 90%-100% with corresponding false positive rates of 16.12%-0%, respectively.

Original languageEnglish
Title of host publication2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008
Pages560-563
Number of pages4
DOIs
StatePublished - 2008
Event2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008 - Shanghai, China
Duration: 16 May 200818 May 2008

Publication series

Name2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008

Conference

Conference2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008
Country/TerritoryChina
CityShanghai
Period16/05/0818/05/08

Keywords

  • Asynchronous brain-controlled switch
  • ERD/ERS patterns
  • Fractal dimension

Fingerprint

Dive into the research topics of 'Fractal dimension for detection of ERD/ERS patterns in asynchronous brain computer interface'. Together they form a unique fingerprint.

Cite this