title: Spectral Spatio-Temporal template extraction from EEG signals.
abstract: Analysis of Event Related Potentials (ERPs) produced by brain activities can provide insight into the timing of underlying brain function. ERPs can be classified by their time/frequency characteristics and spatial location on the scalp. Traditionally, ERPs are manually located by temporally and spatially averaged EEG signals. This process is error prone and sensitive to a priori assumptions. Our proposed algorithm is a general neuroscience-focused data mining algorithm that performs time and frequency analysis on ERPs and automatically extracts templates corresponding to Spectral Spatio-Temporal (SST) regions exhibiting significant differences between experimental outcomes. The method uses time-aligned templates, which preserve the characteristics of the signal important to cognitive researchers. The ability of the selected signal templates to differentiate between stimulus responses has been verified using a pattern recognition procedure. SST template extraction is tested on data taken from a Go/NoGo task and shown to both find relationships consistent with published neuroscience literature as well as novel relationships.
authors.affiliation: Embedded Systems and Signal Processing Lab, Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA. sxo081000@utdallas.edu
authors.forename: Sarah
authors.initials: S
authors.lastname: Ostadabbas
authors.forename: Roozbeh
authors.initials: R
authors.lastname: Jafari
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity