A New Statistical Model Of Electroencephalogram Noise Spectra For Real-Time Brain-Computer Interfaces

Keywords

1f noise; brain-computer interface (BCI); electroencephalogram; ion channels; maximum entropy; neural noise; neurological noise; periodogram; SSVEP

Abstract

Objective: A characteristic of neurological signal processing is high levels of noise from subcellular ion channels up to whole-brain processes. In this paper, we propose a new model of electroencephalogram (EEG) background periodograms, based on a family of functions which we call generalized van der Ziel-McWhorter (GVZM) power spectral densities (PSDs). To the best of our knowledge, the GVZM PSD function is the only EEG noise model that has relatively few parameters, matches recorded EEG PSD's with high accuracy from 0 to over 30 Hz, and has approximately 1/fθ behavior in the midfrequencies without infinities. Methods: We validate this model using three approaches. First, we show how GVZM PSDs can arise in a population of ion channels at maximum entropy equilibrium. Second, we present a class of mixed autoregressive models, which simulate brain background noise and whose periodograms are asymptotic to the GVZM PSD. Third, we present two real-time estimation algorithms for steady-state visual evoked potential (SSVEP) frequencies, and analyze their performance statistically. Results: In pairwise comparisons, the GVZM-based algorithms showed statistically significant accuracy improvement over two well-known and widely used SSVEP estimators. Conclusion: The GVZM noise model can be a useful and reliable technique for EEG signal processing. Significance: Understanding EEG noise is essential for EEG-based neurology and applications such as real-time brain-computer interfaces, which must make accurate control decisions from very short data epochs. The GVZM approach represents a successful new paradigm for understanding and managing this neurological noise.

Publication Date

8-1-2017

Publication Title

IEEE Transactions on Biomedical Engineering

Volume

64

Issue

8

Number of Pages

1688-1700

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TBME.2016.2606595

Socpus ID

85027377518 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85027377518

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