A new and general approach to signal denoising and eye movement classification based on segmented linear regression.

ABSTRACT: We introduce a conceptually novel method for eye-movement signal analysis. The method is general in that it does not place severe restrictions on sampling frequency, measurement noise or subject behavior. Event identification is based on segmentation that simultaneously denoises the signal and determines event boundaries. The full gaze position time-series is segmented into an approximately optimal piecewise linear function in O(n) time. Gaze feature parameters for classification into fixations, saccades, smooth pursuits and post-saccadic oscillations are derived from human labeling in a data-driven manner. The range of oculomotor events identified and the powerful denoising performance make the method useable for both low-noise controlled laboratory settings and high-noise complex field e

SUBMITTER: Pekkanen J 

PROVIDER: S-EPMC5735175 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature