Please use this identifier to cite or link to this item: https://doi.org/10.1088/1741-2560/11/2/026017
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dc.titleA fast, robust algorithm for power line interference cancellation in neural recording
dc.contributor.authorKeshtkaran, M.R.
dc.contributor.authorYang, Z.
dc.date.accessioned2014-10-07T04:22:39Z
dc.date.available2014-10-07T04:22:39Z
dc.date.issued2014
dc.identifier.citationKeshtkaran, M.R., Yang, Z. (2014). A fast, robust algorithm for power line interference cancellation in neural recording. Journal of Neural Engineering 11 (2) : -. ScholarBank@NUS Repository. https://doi.org/10.1088/1741-2560/11/2/026017
dc.identifier.issn17412552
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/81868
dc.description.abstractObjective. Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. Approach. The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated by using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. Main results. The algorithm does not require any reference signal, and can track the frequency, phase and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (30 dB) in different conditions of interference strengths (input SNR from -30 to 30 dB), power line frequencies (45-65 Hz) and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power and the sampling rate. A hardware prototype was fabricated in a 65 nm CMOS process and tested. Software implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. Significance. The proposed algorithm features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement and straightforward parameter adjustment. These features render the algorithm suitable for wearable and implantable sensor applications, where reliable and real-time cancellation of the interference is desired. © 2014 IOP Publishing Ltd.
dc.sourceScopus
dc.subject50 or 60 Hz noise
dc.subjectadaptive filtering
dc.subjectelectrocorticography
dc.subjectelectroencephalography
dc.subjectextracellular neural recording
dc.subjectgamma-band oscillations
dc.subjectpower line interference
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1088/1741-2560/11/2/026017
dc.description.sourcetitleJournal of Neural Engineering
dc.description.volume11
dc.description.issue2
dc.description.page-
dc.identifier.isiut000333419400022
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