PELP: Accounting for Missing Data in Neural Time Series by Periodic Estimation of Lost Packets - Dastin-van Rijn, Evan M.; Provenza, Nicole R.; Vogt, Gregory S.; Avendano-Ortega, Michelle; Sheth, Sameer A.; Goodman, Wayne K.; Harrison, Matthew T.; Borton, David A. (Frontiers in Human Neuroscience, 2022)
Recent advances in wireless data transmission technology have the potential to revolutionize clinical neuroscience. Today sensing-capable electrical stimulators, known as “bidirectional devices”, are used to acquire chronic brain activity from humans in natural environments. However, with wireless transmission come potential failures in data transmission, and not all available devices correctly account for missing data or provide precise timing for when data losses occur. Our inability to precisely reconstruct time-domain neural signals makes it difficult to apply subsequent neural signal processing techniques and analyses. Here, our goal was to accurately reconstruct time-domain neural signals impacted by data loss during wireless transmission. Towards this end, we developed a method termed Periodic Estimation of Lost Packets (PELP). PELP leverages the highly periodic nature of stimulation artifacts to precisely determine when data losses occur. Using simulated stimulation waveforms added to human EEG data, we show that PELP is robust to a range of stimulation waveforms and noise characteristics. Then, we applied PELP to local field potential (LFP) recordings collected using an implantable, bidirectional DBS platform operating at various telemetry bandwidths. By effectively accounting for the timing of missing data, PELP enables the analysis of neural time series data collected via wireless transmission– a prerequisite for better understanding the brain-behavior relationships underlying neurological and psychiatric disorders.