title: Spike separation from EEG/MEG data using morphological filter and wavelet transform.
abstract: In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a circular structuring element. Our experimental results have shown that this method is highly effective in spike separation. Comparisons with the wavelet, bandpass filter, empirical mode decomposition (EMD), and independent component analysis (ICA) methods show that the new method is more effective in estimating both spike amplitudes and locations.
authors.affiliation: Lab. for Comp. Neuroscience, Dept. of Neurosurg., Pittsburgh Univ., Pittsburgh, PA 15260, USA.
authors.forename: Wenyan
authors.initials: W
authors.lastname: Jia
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Sclabassi
authors.forename: Lin-Sen
authors.initials: LS
authors.lastname: Pon
authors.forename: Mark L
authors.initials: ML
authors.lastname: Scheuer
authors.forename: Mingui
authors.initials: M
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001931:Brain Mapping; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods; D004827:Epilepsy / Q000175:diagnosis; D006801:Humans; D015225:Magnetoencephalography / Q000295:instrumentation* / Q000379:methods; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013997:Time Factors