Eye tracking analytics for mental states assessment – a review

Abstract

Objectively measuring and monitoring human mental states in a non-intrusive way is important in improving the context-awareness of smart objects. One of the suitable bio-signals in measuring human mental states is aye-tracking data, as visual is the first channel of information collection. In addition, eye-tracking data shows the process of human-system interactions. Traditionally, many studies have been conducted to investigate the correlations between eye-tracking data and human mental states. Recently, with advanced artificial intelligence algorithms, the spatial and temporal patterns of eye-tracking data can be deeply analyzed for detecting human mental states. This study aims to explore and review eye-tracking parameters and state-of-art methods for mental states assessments. The study reveals that both statistical methods and novel methods, such as machine learning and deep learning have been applied to process eye-tracking data. Besides, novel features extracted from eye-tracking data, such as gaze-bin and entropy have been used in assessing human mental states. This review is expected to provide references for eye-tracking data analysis.