A fast, robust algorithm for power line interference cancellation in neural recording

Abstract

Abstract.Objective 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 through 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 (< 100 ms) and substantial interference rejection (output SNR > 30 dB) in different conditions of interference strengths (input SNR from −30 dB 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 prototype was fabricated in a 65-nm CMOS process and tested. The MATLAB 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.

Topological classifier for detecting the emergence of epileptic seizures

“…The length of each is N , the number of samples. For each the preprocessing performs two actions: Filtering the EEG reduces the noise by using a bandpass filter between 1–70 Hz, and removes the power line using a notch filter, between 8 and 52 Hz [28–30]. …”

“…Filtering the EEG reduces the noise by using a bandpass filter between 1–70 Hz, and removes the power line using a notch filter, between 8 and 52 Hz [28–30]. …”

ZapLine: a simple and effective method to remove power line artifacts

“…A wide range of approaches have been proposed to attenuate or remove line artifacts, including lowpass and notch filters (Luck, 2005), frequency domain filters (Mitra and Pesaran, 1999;Mullen, 2012;Keshtkaran and Yang, 2014;Leske and Dalal, 2019), regression based on a reference signal (Vrba and Robinson, 2001;de Cheveigné and Simon, 2007), independent component analysis (ICA) (Barbati et al, 2004;Escudero et al, 2007) or other spatial filtering techniques (de Cheveigné and Parra, 2014). These methods are well known and widely used, and there are ongoing efforts to develop new ones (Leske and Dalal, 2019).…”

Effective Biopotential Signal Acquisition: Comparison of Different Shielded Drive Technologies

“…For instance, Tomasini et al used an adaptive power line interference filter to estimate the fundamental frequency and harmonics of power line interference, and the estimated interference was subtracted from the noise-affected biosignal [22]. Keshtkaran et al presented a scalable very-large-scale integration architecture of a robust algorithm for power line interference cancelation in multichannel biopotential recordings [23], and they also proposed an adaptive notch filter to estimate the contents of power line interference with a modified recursive least squares algorithm [41]. Although these filtering technologies showed great performance and robustness, the real time computation load is still high for low-power embedded systems, especially when the sampling frequency is high.…”