title: Combined process automation for large-scale EEG analysis.
abstract: Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis.
authors.affiliation: Neuroscience Center of Excellence, Louisiana State University Health Sciences Center, 2020 Gravier Street, New Orleans, LA 70112, USA.
authors.forename: John L
authors.initials: JL
authors.lastname: Sfondouris
authors.forename: Tabitha M
authors.initials: TM
authors.lastname: Quebedeaux
authors.forename: Chris
authors.initials: C
authors.lastname: Holdgraf
authors.forename: Alberto E
authors.initials: AE
authors.lastname: Musto
mesh_terms: D000465:Algorithms; D000818:Animals; D046690:Deep Brain Stimulation; D004569:Electroencephalography / Q000379:methods*; D008297:Male; D051381:Rats; D017208:Rats, Wistar; D012640:Seizures / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted