Optimal Causal Filtering For 1 /FΑ-Type Noise In Single-Electrode Eeg Signals

Keywords

1f noise; EEG; Hidden Markov models; Ion channels; Neurological noise; SSVEP

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.

Publication Date

10-13-2016

Publication Title

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volume

2016-October

Number of Pages

997-1001

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/EMBC.2016.7590870

Socpus ID

85009105046 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85009105046

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