Automated Filtering of Common-Mode Artifacts in Multichannel Physiological Recordings

Abstract

The removal of spatially correlated noise is an important step in processing multi-channel recordings. Here, a technique termed the adaptive common average reference (ACAR) is presented as an effective and simple method for removing this noise. The ACAR is based on a combination of the well-known common average reference (CAR) and an adaptive noise canceling (ANC) filter. In a convergent process, the CAR provides a reference to an ANC filter, which in turn provides feedback to enhance the CAR. This method was effective on both simulated and real data, outperforming the standard CAR when the amplitude or polarity of the noise changes across channels. In many cases the ACAR even outperformed independent component analysis (ICA). On 16 channels of simulated data the ACAR was able to attenuate up to approximately 290 dB of noise and could improve signal quality if the original SNR was as high as 5 dB. With an original SNR of 0 dB, the ACAR improved signal quality with only two data channels and performance improved as the number of channels increased. It also performed well under many different conditions for the structure of the noise and signals. Analysis of contaminated electrocorticographic (ECoG) recordings further showed the effectiveness of the ACAR.

Iterative Covariance-Based Removal of Time-Synchronous Artifacts: Application to Gastrointestinal Electrical Recordings

“…Another related approach is the method of Kelly et al, which bootstraps a common average reference technique with adaptive noise cancellation to remove common mode artifacts (ACAR) [38]. Whereas ACAR subtracts the noise template in a single pass within a sliding window, our method works iteratively, making multiple passes to remove residual interference.…”

“…Similar to ACAR, our method uses a common reference that is further filtered-although not adaptively—to generate a noise template. While the ACAR method was shown to increase the SAR of noise corrupted data sets from -5 to about 10.5 dB (Δ SAR ≈ 15.5 dB), it was also reported to be poorly suited for cleaning sudden, large artifacts because the learning rate is typically too slow to appropriately adapt the filter tap coefficients [38]. By contrast, COBRA was demonstrated to be very effective and efficient for this task.…”

Adaptive common average reference for in vivo multichannel local field potentials

“…In order to increase the quality of the CAR reference, for each channel, we calculate the CAR with the remaining channels instead of all the channels. The candidate channel is normalized by the z-score, but the mean is not removed since the artifacts could have a DC component [16]. To improve the speed and consistency of convergence, the reference data is smoothed by a moving average filter with the span of 5.…”

“…The vector was then normalized to provide a specified average SNR to ensure the value was a constant. In keeping with theoretical analysis, the average SNR was calculated as the mean signal power over the mean noise power [16]:…”

“…This study presents a simple processing method, the adaptive common average reference (ACAR), based on a combination of the classic common average reference (CAR) and an adaptive noise canceling (ANC). The CAR has been always used in EEG/ECoG to identify the small signal sources in noisy recordings [16,17]. Ludwig et al [18] has studied its effectiveness in single-unit neural recordings.…”