# Phase Locking Value revisited: teaching new tricks to an old dog

arXiv: https://arxiv.org/abs/1710.08037 · HTML: https://mlsanity.com/paper/1710.08037/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters](https://mlsanity.com/paper/2110.10009/) (2021)
- [Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment](https://mlsanity.com/paper/2310.02398/) (2023)
- [Graph Attention Networks for Detecting Epilepsy from EEG Signals Using Accessible Hardware in Low-Resource Settings](https://mlsanity.com/paper/2507.15118/) (2025)
- [Bridging Accuracy and Explainability in EEG-based Graph Attention Network for Depression Detection](https://mlsanity.com/paper/2511.05537/) (2025)

## Abstract (arXiv)

Despite the increase in calculation power in the last decades, the estimation of brain connectivity is still a tedious task. The high computational cost of the algorithms escalates with the square of the number of signals evaluated, usually in the range of thousands. In this work we propose a re-formulation of a widely used algorithm that allows the estimation of whole brain connectivity in much smaller times. We start from the original implementation of Phase Locking Value (PLV) and re-formulated it in a highly computational efficient way. Besides, this formulation stresses its strong similarity with coherence, which we used to introduce two new metrics insensitive to zero lag synchronization, the imaginary part of PLV (iPLV) and its corrected counterpart (ciPLV). The new implementation of PLV avoids some highly CPU-expensive operations, and achieved a 100-fold speedup over the original algorithm. The new derived metrics were highly robust in the presence of volume conduction. ciPLV, in particular, proved capable of ignoring zero-lag connectivity, while correctly estimating nonzero-lag connectivity. Our implementation of PLV makes it possible to calculate whole-brain connectivity in much shorter times. The results of the simulations using ciPLV suggest that this metric is ideal to measure synchronization in the presence of volume conduction or source leakage effects.