Prior studies aiming to parametrize the sequences obtained from the Smooth Pursuit Eye Movements (SPEM) of patients with Parkinson's disease are based on the manual extraction of cues of interest. This is because methods to automatically extract the relevant information are complex to implement and are constrained, in part, by the appearance of a baseline wander (BW). Thus, new methods are required for preprocessing the SPEM sequences to make the potential parameterisation procedures much more robust, removing the aforementioned BW. In this respect, the present study compares different BW removal methods applied to SPEM sequences based on several objective evaluation metrics. At the same time, it proposes a set of guidelines to estimate the ground truth that is required for comparison purposes. Data were collected using a high-speed video-based eye-tracking device. 52 patients and 60 controls and 12 young participants were enrolled in the study. The ground truth required to compare the different BW removal techniques was manually delineated according to a predefined protocol. Seven methods were developed to remove the BW, and four objective metrics were used to evaluate the results. According to the results, a method based on the Empirical Wavelet Transform provided the best performance removing the BW 1 . Furthermore, the objective and subjective results show that potential asymmetries between left and right eye movements are solved by removing the BW. Regardless of the techniques used, BW removal is revealed to be a crucial step for any autonomous SPEM processing tool.INDEX TERMS Eye movements, smooth pursuit, baseline wander removal, Parkinson's disease.