AI-driven pupillary-computer interface via binary-coded flickering stimuli

Abstract

Pupillary-computer interface (PCI) refers to a novel interaction modality that leverages pupil size variations elicited by changes in visual stimulus brightness. The PCI based on the pupillary light reflex (PLR) induced by binary-coded visual stimuli was proposed. A novel PCI interface was devised to overcome the limitations of conventional electroencephalogram hardware, using artificial intelligence to model subtle pupil signal patterns induced by visual stimuli. The proposed PCI system exhibited high performance in terms of the number of commands, classification accuracy, and information transfer rate (ITR) using a simple binary coding scheme and convolutional neural network-based deep learning. Twelve healthy subjects (six men and six women, aged 28.6 ± 3.4 year) participated in three experimental conditions, each using 4-, 10-, and 20-class binary-coded visual stimuli. Each visual stimulus was constructed by dividing the 3-s period into ten phases of 0.3 s each, with a single brightness change (e.g., from dark to bright) occurring within this interval. The proposed system achieved a high classification accuracy (91.84 %, 93.84 %, and 98.61 %) and ITR (59.74, 62.04, and 69.36 bits/min) for 20-, 10-, and 4-class stimuli in the test dataset, considerably outperforming previous PLR-based interface studies. The findings indicate that the proposed PCI system provides a simple, cost-effective, and low-training-requirement interface solution that does not require user training and maintains long-term stability.

Keywords: Brain–Computer Interface (BCI); Convolutional Neural Network (CNN); Human–Computer Interface (HCI); Keyboard speller; Pupillary Light Reflex (PLR); Pupillary–Computer Interface (PCI).