# Time-frequency analysis of event-related brain recordings: Effect of noise on power

## TL;DR

This paper studies how noise affects time-frequency analysis in brain recordings, helping neuroscientists better interpret their data.

## Contribution

The study introduces a systematic analysis of how various factors influence noise effects on two time-frequency measures in neuroscience.

## Key findings

- Additive noise significantly impacts avgPOW and POWavg in time-frequency analysis.
- Factors like noise variance, number of trials, and sampling rate influence noise effects.
- The type of noise and frequency of interest also play critical roles in analysis outcomes.

## Abstract

In neuroscience, time-frequency analysis is widely used to investigate brain rhythms in brain recordings. In event-related protocols, it is applied to quantify how the brain responds to a stimulation repeated over many trials. We here focus on two common measures: the power of the transform for each single trial averaged across trials, avgPOW; and the power of the transform of the average evoked potential, POWavg. We investigate the influence of additive noise on these two measures. We quantify the expected effect using theoretical calculations, simulated data and experimental brain recordings. We also consider the case of color noise. We extract the main factors influencing the effect of noise on POWavg and avgPOW, such as the noise variance, the number of trials, the sampling rate, the type of noise, the type of time-frequency transform and the frequency of interest. When dealing with time-frequency analysis, the impact of noise on the neuroscientist's work can drastically vary depending on these factors. The present results should help researchers improve their understanding and interpretation of time-frequency diagrams, as well as optimize their experimental designs and analyses based on their neuroscientific question.