Assessment of reading material using sensor data. (13th June 2022)

• Record Type: Journal Article
• Authors: Sinha, Aniruddha; Saha, Sanjoy Kumar; Basu, Anupam
• Is Part Of: International journal of automation and control. Volume 16:Number 5(2022)
• Page Start: 519
• Page End: 546
• Publication Date: 2022-06-13
• Subjects: textual content -- eye-gaze -- electroencephalogram -- EEG -- cognitive load -- sigmoid; Automation -- Periodicals; Control theory -- Periodicals; Automatic control -- Periodicals; Robots -- Periodicals; Bionics -- Periodicals; Intelligent control systems -- Periodicals
• Languages: English
• ISSNs: 1740-7516

Abstract

Reading is characterised by a sequence of complex processing in the brain. The experienced cognitive load and engagement level play a major role in the assimilation of content. In this paper, for the evaluation of a textual content, we use electroencephalogram (EEG) for brain signals and eyetracker for the eyegaze data. Experiment is done with two types (easy and difficult) of textual contents which are benchmarked using standard parameters of natural language processing. Features on cognitive load and engagement index extracted from alpha and beta frequency bands of EEG are found to be discriminative from left-temporal and right-prefrontal lobes respectively. Statistical features, derived from the shift in eyegaze fixations from the current line to the adjacent lines, are found to be discriminative. A difficulty score is computed using a novel mapping function derived from the mixture of two partial sigmoid. This enables more objective comparison of two contents and helps in finding differences in individual reading skills.