BLASST: Band Limited Atomic Sampling With Spectral Tuning With Applications to Utility Line Noise Filtering.

OBJECTIVE: In this paper, we present and test a new method for the identification and removal of nonstationary utility line noise from biomedical signals.

METHODS: The method, band limited atomic sampling with spectral tuning (BLASST), is an iterative approach that is designed to 1) fit nonstationarities in line noise by searching for best-fit Gabor atoms at predetermined time points, 2) self-modulate its fit by leveraging information from frequencies surrounding the target frequency, and 3) terminate based on a convergence criterion obtained from the same surrounding frequencies. To evaluate the performance of the proposed algorithm, we generate several simulated and real instances of nonstationary line noise and test BLASST along with alternative filtering approaches.

RESULTS: We find that BLASST is capable of fitting line noise well and/or preserving local signal features relative to tested alternative filtering techniques.

CONCLUSION: BLASST may present a useful alternative to bandpass, notch, or other filtering methods when experimentally relevant features have significant power in a spectrum that is contaminated by utility line noise, or when the line noise in question is highly nonstationary.

SIGNIFICANCE: This is of particular significance in electroencephalography experiments, where line noise may be present in the frequency bands of neurological interest and measurements are typically of low enough strength that induced line noise can dominate the recorded signals. In conjunction with this paper, the authors have released a MATLAB toolbox that performs BLASST on real, vector-valued signals (available at https://github.com/VisLab/blasst).