Trial-to-trial noise cancellation of cortical field potentials in awake macaques by autoregression model with exogenous input (ARX)

▶ In light of the recent flurry of publications on the behavioral significance of surrounding gamma band (30-90hz) frequencies, the ability to remove 50/60 hz line noise from within this significant signal-laden frequency range has become a pressing issue. An adaptive noise cancellation (ANC) method which is implemented with AutoRegressive model with eXogenous Input (ARX) is described to remove power line noise in cortical local field potential (LFP) recordings in primary visual cortex of awake animals. ▶ We compare the accuracy and efficiency of such trial-by-trial noise cancellation method to the conventional notch filter. The ARX implementation has great computation efficiency and simplified algorithm, and is automated across thousands of trials data set. ▶ We demonstrate that this procedure, combined with wavelet time-frequency analysis, effectively removes the mains noise from synthetic data as well as real experimental data acquired from awake monkey.