title: Human ECoG analysis during speech perception using matching pursuit: a comparison between stochastic and dyadic dictionaries.
abstract: We use the matching pursuit (MP) algorithm to detect induced gamma activity in human EEG during speech perception. We show that the MP algorithm is particularly useful for detecting small power changes at high gamma frequencies (> 70 Hz). We also compare the performance of the MP using a stochastic versus a dyadic dictionary and show that despite the frequency bias the time-frequency power plot (averaged over 100 trials) generated by the dyadic MP is almost identical (> 98.5%) to the one generated by the stochastic MP. However, the dyadic MP is computationally much faster than the stochastic MP.
authors.affiliation: Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, 338 Krieger Hall, Room 253, Baltimore, MD 21218, USA. sray@bme.jhu.edu
authors.forename: Supratim
authors.initials: S
authors.lastname: Ray
authors.forename: Christophe C
authors.initials: CC
authors.lastname: Jouny
authors.forename: Nathan E
authors.initials: NE
authors.lastname: Crone
authors.forename: Dana
authors.initials: D
authors.lastname: Boatman
authors.forename: Nitish V
authors.initials: NV
authors.lastname: Thakor
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Franaszczuk
mesh_terms: D000465:Algorithms; D001303:Auditory Cortex / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D013067:Speech Perception / Q000502:physiology*