What does clean EEG look like?
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).
Institute for Knowledge Discovery, Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology, Krenngasse 37/IV, 8010, Graz, Austria i.daly@tugraz.at
Ian
I
Daly
Floriana
F
Pichiorri
Josef
J
Faller
Vera
V
Kaiser
Alex
A
Kreilinger
Reinhold
R
Scherer
Gernot
G
Müller-Putz
D016477:Artifacts; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult