Dastin-van Rijn, E. | Search bioRxiv

bioRxiv2022

PELP: accounting for missing data in neural time series by Periodic Estimation of Lost Packets

ObjectiveImplanted electrical stimulators with sensing capabilities have enabled the development of closed-loop neuromodulation therapies capable of responding to patient needs in real-time. Through a combination of rechargeable technologies and wireless data transmission, it is now possible for researchers to acquire extensive neural recordings from human participants in naturalistic settings using these bidirectional devices. However, data losses during wireless transmission hamper processing and the identification of neural signals of interest, driving the need for methodologies to properly estimate the impact of data loss. ApproachTo accurately reconstruct the timing of data containing losses, we have developed a method called Periodic Estimation of Lost Packets (PELP) to precisely determine the number of samples lost from implanted recordings during active stimulation. PELP leverages a data-driven procedure for determining the period of stimulation and the knowledge that stimulation continues identically during periods where data are missing to accurately account for the number of samples lost. Main resultsUsing simulated stimulation added to collected human EEG data, we show that PELP is robust to a range of stimulation waveforms and noise characteristics. Lastly, we successfully applied PELP to local field potential (LFP) recordings from an implanted, bidirectional device using data recorded in the clinic and the patients own home. SignificanceBy effectively accounting for the timing of missing data, PELP enables the analysis of complex, naturalistic neural time series data from bidirectional implanted devices aiding in the development of novel therapeutic approaches. NCT04806516 (ClinicalTrials.gov).

neuroscience↗

bioRxiv2021

How do packet losses affect measures of averaged neural signals?

Recent advances in implanted device development have enabled chronic streaming of neural data to external devices allowing for long timescale, naturalistic recordings. However, characteristic data losses occur during wireless transmission. Estimates for the duration of these losses are typically uncertain reducing signal quality and impeding analyses. To characterize the effect of these losses on recovery of averaged neural signals, we simulated neural time series data for a typical event-related potential (ERP) experiment. We investigated how the signal duration and the degree of timing uncertainty affected the offset of the ERP, its duration in time, its amplitude, and the ability to resolve small differences corresponding to different task conditions. Simulations showed that long timescale signals were generally robust to the effects of packet losses apart from timing offsets while short timescale signals were significantly delocalized and attenuated. These results provide clarity on the types of signals that can be resolved using these datasets and provide clarity on the restrictions imposed by data losses on typical analyses.

bioengineering↗