title: Optimal causal filtering for 1 /fα-type noise in single-electrode EEG signals.
abstract: Understanding the mode of generation and the statistical structure of neurological noise is one of the central problems of biomedical signal processing. We have developed a broad class of abstract biological noise sources we call hidden simplicial tissues. In the simplest cases, such tissue emits what we have named generalized van der Ziel-McWhorter (GVZM) noise which has a roughly 1/fα spectral roll-off. Our previous work focused on the statistical structure of GVZM frequency spectra. However, causality of processing operations (i.e., dependence only on the past) is an essential requirement for real-time applications to seizure detection and brain-computer interfacing. In this paper we outline the theoretical background for optimal causal time-domain filtering of deterministic signals embedded in GVZM noise. We present some of our early findings concerning the optimal filtering of EEG signals for the detection of steady-state visual evoked potential (SSVEP) responses and indicate the next steps in our ongoing research.
authors.forename: Alan
authors.initials: A
authors.lastname: Paris
authors.forename: George
authors.initials: G
authors.lastname: Atia
authors.forename: Azadeh
authors.initials: A
authors.lastname: Vosoughi
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Berman
mesh_terms: D062207:Brain-Computer Interfaces; D004566:Electrodes; D004569:Electroencephalography; D005074:Evoked Potentials, Visual; D006801:Humans; D012815:Signal Processing, Computer-Assisted