Sampath Jayarathna

TL;DR: This paper evaluates the performance of five eye-movement classification algorithms in terms of their assessment of oculomotor fixation and saccadic behavior and proposes techniques to enable efficient and objective clinical applications providing means to assure meaningful automated eye- Movement classification.

TL;DR: A new biometric approach that involves an estimation of the unique oculomotor plant or eye globe muscle parameters from an eye movement trace provides a number of advantages for biometric identification: it includes both behavioral and physiological human attributes, is difficult to counterfeit, non-intrusive, and could easily be incorporated into existing biometric systems to provide an extra layer of security.

TL;DR: The paper presents an evaluation of the classification performance of each algorithm in the case when values of the input parameters are varied and Discussion on what is the "best" classification algorithm is provided for several applications.

TL;DR: A survey of major EEG-based ASD classification approaches from 2010 to 2018, which adopt machine learning is presented, exploring different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model.

TL;DR: A rulebased approach is used to analyse the accuracies of decision tree classifiers in identifying ADHD subjects and both algorithms have shown high accuracy with the rule-based component.