Phase estimation with autoregressive padding (PEAP): addressing inaccuracies and biases in EEG analysis

TL;DR

This paper introduces PEAP, a new EEG phase estimation method that reduces biases and improves accuracy over existing methods, enhancing reliability in clinical and research applications.

Contribution

PEAP is a novel phase estimation technique that prevents artifacts and biases inherent in established methods, offering more accurate EEG phase analysis.

Findings

PEAP improves phase estimation accuracy by 3.2 to 9.2%.

Established methods show systematic biases and phase shifts.

Differences between methods are consistent across populations.

Abstract

Accurate phase estimation at the edge of data segments is crucial for EEG applications such as EEG-TMS in offline and real-time data analysis. Our research evaluates the phase estimation performance of four commonly used methods (Phastimate, SSPE, ETP, and PhastPadding) for accuracy and systemic biases, using data from young and elderly healthy controls and chronic stroke participants. To address the identified limitations of the established methods, we introduce Phase Estimation with Autoregressive Padding (PEAP), a method that prevents strong bandpass filtering-induced artifacts. Contrary to the established methods, PEAP does not show significant biases and improves accuracy by 3.2 to 9.2% for the continuous phase estimation. Our offline analysis demonstrates how established methods are systematically biased towards some estimates and how they induce phase shifts. We also show that differences between methods do not vary between clinical and control populations, supporting their translatability. This work indicates that systematic biases in established phase estimation methods may compromise the validity and comparability of phase-dependent findings. PEAP addresses these limitations and thus offers a more reliable and more accurate alternative method.