Analyzing the Impact of Cognitive Load in Evaluating Gaze-based Typing

TL;DR

This study investigates how cognitive load affects gaze-based typing by analyzing EEG signals, revealing mental workload differences not captured by traditional performance metrics, and suggesting improvements for usability.

Contribution

The paper introduces EEG-based cognitive load measurement as a novel evaluation method for gaze-based virtual keyboards, highlighting its importance alongside conventional metrics.

Findings

EEG analysis shows variations in mental workload across different keyboard designs.

Traditional metrics do not capture differences in cognitive effort between designs.

Cognitive load insights can inform better gaze-based keyboard design improvements.

Abstract

Gaze-based virtual keyboards provide an effective interface for text entry by eye movements. The efficiency and usability of these keyboards have traditionally been evaluated with conventional text entry performance measures such as words per minute, keystrokes per character, backspace usage, etc. However, in comparison to the traditional text entry approaches, gaze-based typing involves natural eye movements that are highly correlated with human brain cognition. Employing eye gaze as an input could lead to excessive mental demand, and in this work we argue the need to include cognitive load as an eye typing evaluation measure. We evaluate three variations of gaze-based virtual keyboards, which implement variable designs in terms of word suggestion positioning. The conventional text entry metrics indicate no significant difference in the performance of the different keyboard designs. However, STFT (Short-time Fourier Transform) based analysis of EEG signals indicate variances in the mental workload of participants while interacting with these designs. Moreover, the EEG analysis provides insights into the user's cognition variation for different typing phases and intervals, which should be considered in order to improve eye typing usability.

Full text

See pages 1-last of paper.pdf