An error-aware gaze-based keyboard by means of a hybrid BCI system

• Published in: Scientific reports 2018-09, Vol.8 (1), p.13176-11, Article 13176
• Main Authors: Kalaganis, Fotis P., Chatzilari, Elisavet, Nikolopoulos, Spiros, Kompatsiaris, Ioannis, Laskaris, Nikos A.
• Format: Article
• Language: English
• Subjects: 631/114; 631/378/116; Brain; Computer applications; Contamination; EEG; Error correction & detection; Humanities and Social Sciences; Implants; Interfaces; Keyboards; multidisciplinary; Science; Science (multidisciplinary); Tracking equipment
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-31425-2

Gaze-based keyboards offer a flexible way for human-computer interaction in both disabled and able-bodied people. Besides their convenience, they still lead to error-prone human-computer interaction. Eye tracking devices may misinterpret user’s gaze resulting in typesetting errors, especially when operated in fast mode. As a potential remedy, we present a novel error detection system that aggregates the decision from two distinct subsystems, each one dealing with disparate data streams. The first subsystem operates on gaze-related measurements and exploits the eye-transition pattern to flag a typo. The second, is a brain-computer interface that utilizes a neural response, known as Error-Related Potentials (ErrPs), which is inherently generated whenever the subject observes an erroneous action. Based on the experimental data gathered from 10 participants under a spontaneous typesetting scenario, we first demonstrate that ErrP-based Brain Computer Interfaces can be indeed useful in the context of gaze-based typesetting, despite the putative contamination of EEG activity from the eye-movement artefact. Then, we show that the performance of this subsystem can be further improved by considering also the error detection from the gaze-related subsystem. Finally, the proposed bimodal error detection system is shown to significantly reduce the typesetting time in a gaze-based keyboard.