Comparison of eye movement metrics recorded at different sampling rates

Previous work has shown significant differences in eye movement metrics recorded by devices differing in sampling rates. Two schools of thought have emerged on how to effectively compare such apparently disparate data. The first, termed here as upsampling, strives to process eye movement data recorded at a low sampling rate to allow comparison with data recorded at a high sampling rate, e. g., by fitting a cubic spline to the signal derivative (i.e., velocity). Instead, we suggest downsampling based on a two-pass solution in which data is first downsampled and smoothed prior to its velocity-based classification. Results indicate that given a similar experimental task, this approach gives more equitable results than other single-pass classification methods as they typically do not explicitly consider sampling rates.