Biomedical Signal Processing and Control 47 (2019) 159–167 Contents lists available at ScienceDirect Biomedical Signal Processing and Control journal homepage: www.elsevier.com/locate/bspc EOG-based eye movement detection and gaze estimation for an asynchronous virtual keyboard Nathaniel Barbara ∗ , Tracey A. Camilleri, Kenneth P. Camilleri Department of Systems and Control Engineering, Faculty of Engineering, University of Malta, Msida MSD2080, Malta a r t i c l e i n f o Article history: Received 28 March 2018 Received in revised form 13 June 2018 Accepted 10 July 2018 Keywords: Electrooculography Gaze estimation Eye movement detection Saccades Blinks Virtual keyboard a b s t r a c t This work aims to develop a novel electrooculography (EOG)-based virtual keyboard with a standard QWERTY layout which, unlike similar state-of-the-art systems, allows users to reach any icon from any location directly and asynchronously. The saccadic EOG potential displacement is mapped to angular gaze displacement using a novel two-channel input linear regression model, which considers features extracted from both the horizontal and vertical EOG signal components jointly. Using this technique, a gaze displacement estimation error of 1.32 ± 0.26 ◦ and 1.67 ± 0.26 ◦ in the horizontal and vertical directions respectively was achieved, a performance which was also found to be generally statistically significantly better than the performance obtained using one model for each EOG component to model the relationship in the horizontal and vertical directions separately, as typically used in the literature. Furthermore, this work also proposes a threshold-based method to detect eye movements from EOG signals in real-time, which are then classified as saccades or blinks using a novel cascade of a parametric and a signal-morphological classifier based on the EOG peak and gradient features. This resulted in an average saccade and blink labelling accuracy of 99.92% and 100.00% respectively, demonstrating that these two eye movements could be reliably detected and discriminated in real-time using the proposed algorithms. When these techniques were used to interface with the proposed asynchronous EOG-based virtual keyboard, an average writing speed across subjects of 11.89 ± 4.42 characters per minute was achieved, a performance which has been shown to improve substantially with user experience. © 2018 Elsevier Ltd. All rights reserved. 1. Introduction Computers are nowadays regarded as being ubiquitous, gener- ally requiring very little effort to use. However, individuals with mobility impairments, such as those diagnosed with Amyotrophic Lateral Sclerosis (ALS) or paralysed stroke patients, may be seri- ously challenged in their autonomy and control of such devices. Despite the limitations imposed by the different conditions, the eyes are typically the last organs to be affected and hence, an eye movement-based human–computer interface (HCI) system could provide an alternative communication channel to such intelligent systems, giving the individuals suffering from these conditions more independence and an enhanced quality of life [1]. In recent years, such eye-based HCIs have been widely devel- oped using videooculography (VOG)-based techniques, which use cameras and image processing algorithms to track the user’s ocu- lar pose. Although VOG-based techniques yield a better resolution ∗ Corresponding author. E-mail address: nathaniel.barbara@um.edu.mt (N. Barbara). than electrooculography (EOG)-based techniques, they are known to be computationally demanding, susceptible to the lighting con- ditions, sensitive to the user’s movements and also normally require an external illumination source. Alternative eye move- ment recording techniques include infrared reflection oculography, which is generally restricted to the recording of horizontal eye movements only; or the scleral search coil technique, which is semi-invasive as it requires the user to wear contact lenses with embedded coils [2]. EOG, on the other hand, can offer a good alternative solution to these techniques by capturing the electrical activity generated by the human eye, which could be regarded to behave like an elec- tric dipole, having the positive and negative poles at the cornea and retina respectively. In fact, this is known to create a poten- tial difference varying in the range of 0.4–1.0 mV, referred to as the corneo-retinal potential (CRP), which creates an electrical field. Specifically, EOG captures the electrical activity generated by the CRP non-invasively, using a set of gel-based electrodes, attached to the face in peri-orbital positions around the eyes [2,3]. This work is concerned with the use of EOG signals to inter- act with a virtual keyboard application. State-of-the-art EOG-based https://doi.org/10.1016/j.bspc.2018.07.005 1746-8094/© 2018 Elsevier Ltd. All rights reserved.