A high performance asynchronous EOG speller system. (May 2020)

• Record Type: Journal Article
• Authors: D, Saravanakumar; M, Ramasubba Reddy
• Is Part Of: Biomedical signal processing and control. Volume 59(2020)
• Publication Date: 2020-05
• Subjects: Electrooculogram (EOG) -- Keyboard/speller -- Human computer interface (HCI); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.101898
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The proposed electrooculogram (EOG) speller works in asynchronous mode with high performance. In this proposed system, continuous eye movements are avoided while selecting the targets by the introduction of oddball paradigm. The oddball paradigm makes the proposed speller layout to be more user friendly. Only two eye movements are used for target selection (Blink/Wink). Abstract: In this study, an asynchronous region based electrooculogram (EOG) speller system has been proposed with fourty two targets consisting of alphabets, numbers and special characters. The total set of targets were arranged into six groups and each group has seven targets. The target groups were displayed in the form of a hexagon, three target groups on each side (right and left) of the display. The proposed system uses single blink or wink eye movements for selecting the desired target. It adopts two step recognition approach, 1) group selection and 2) target identification from the selected group. A pair of opposite target groups were highlighted at the same time in a random order, like an oddball paradigm. The paradigm randomly highlights all target groups once in a single trial. The subject should blink or wink for target group selection in synchrony with the highlights. In a similar manner the desired target is identified from the selected target group. A multi thresholding algorithm is used for eye movement detection. The online and offline experiments were conducted on ten healthy subjects for validating the proposed speller system. An average online classification accuracy of 98.482% is obtained with the information transfer rate (ITR) of 126.656 bits/min.