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Frequency tracking and variable bandwidth for line noise filtering without a reference

Kelly, JW and Collinger, JL and Degenhart, AD and Siewiorek, DP and Smailagic, A and Wang, W (2011) Frequency tracking and variable bandwidth for line noise filtering without a reference. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 7908 - 7911. ISSN 1557-170X

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Abstract

This paper presents a method for filtering line noise using an adaptive noise canceling (ANC) technique. This method effectively eliminates the sinusoidal contamination while achieving a narrower bandwidth than typical notch filters and without relying on the availability of a noise reference signal as ANC methods normally do. A sinusoidal reference is instead digitally generated and the filter efficiently tracks the power line frequency, which drifts around a known value. The filter's learning rate is also automatically adjusted to achieve faster and more accurate convergence and to control the filter's bandwidth. In this paper the focus of the discussion and the data will be electrocorticographic (ECoG) neural signals, but the presented technique is applicable to other recordings. © 2011 IEEE.

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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Kelly, JW
Collinger, JLcollinger@pitt.eduCOLLINGR
Degenhart, ADadd19@pitt.eduADD19
Siewiorek, DP
Smailagic, A
Wang, Wwangwei3@pitt.eduWANGWEI3
Centers: Other Centers, Institutes, Offices, or Units > Human Engineering Research Laboratories
Date: 26 December 2011
Date Type: Publication
Journal or Publication Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Page Range: 7908 - 7911
DOI or Unique Handle: 10.1109/iembs.2011.6091950
Schools and Programs: School of Health and Rehabilitation Sciences > Rehabilitation Science and Technology
Refereed: No
ISSN: 1557-170X
MeSH Headings: Artifacts; Electroencephalography--methods; Reference Standards; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio
PubMed ID: 22256174
Date Deposited: 30 Jan 2013 20:57
Last Modified: 05 Oct 2020 13:55
URI: http://d-scholarship.pitt.edu/id/eprint/17145

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