What does clean EEG look like?

Author

Daly, Ian ; Pichiorri, F. ; Faller, Josef ; Kaiser, V. ; Kreilinger, A. ; Scherer, Rafal ; Muller-Putz, G.

Author_Institution

Lab. of Brain-Comput. Interfaces, Graz Univ. of Technol., Graz, Austria

fYear

2012

fDate

Aug. 28 2012-Sept. 1 2012

Firstpage

3963

Lastpage

3966

Abstract

Lack of a clear analytical metric for identifying artifact free, clean electroencephalographic (EEG) signals inhibits robust comparison of different artifact removal methods and lowers confidence in the results of EEG analysis. An algorithm is presented for identifying clean EEG epochs by thresholding statistical properties of the EEG. Thresholds are trained on EEG datasets from both healthy subjects and stroke/spinal cord injury patient populations via differential evolution (DE).

Keywords

diseases; electroencephalography; injuries; medical signal processing; neurophysiology; statistical analysis; EEG; artifact removal methods; differential evolution; signal cleaning; spinal cord injury; statistical properties; stroke; Accuracy; Electrodes; Electroencephalography; Noise; Pollution measurement; Standards; Artifacts; Electroencephalography; Female; Humans; Male; Middle Aged; Signal Processing, Computer-Assisted; Young Adult;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE

Conference_Location

San Diego, CA

ISSN

1557-170X

Print_ISBN

978-1-4244-4119-8

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/EMBC.2012.6346834

Filename

6346834