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

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 t...

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Vydané v:	Computers in biology and medicine Ročník 197; číslo Pt B; s. 111057
Hlavní autori:	Park, Sangin, Mun, Sungchul
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 01.10.2025
Predmet:	Adult Artificial Intelligence Brain–Computer Interface (BCI) Convolutional Neural Network (CNN) Female Humans Human–Computer Interface (HCI) Internal Medicine Keyboard speller Male Neural Networks, Computer Other Photic Stimulation Pupil - physiology Pupillary Light Reflex (PLR) Pupillary–Computer Interface (PCI) Reflex, Pupillary - physiology Signal Processing, Computer-Assisted User-Computer Interface Pupillary Light Reflex (PLR) Brain–Computer Interface (BCI) Pupillary–Computer Interface (PCI) Human–Computer Interface (HCI) Keyboard speller Convolutional Neural Network (CNN)
ISSN:	0010-4825, 1879-0534, 1879-0534
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Popis
Shrnutí:	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. •Novel pupillary–computer interface using binary-coded visual stimuli.•High accuracy (91.84 %) and ITR (59.74 bits/min) for 20-class stimuli.•Outperforms previous PLR-based interfaces in commands, accuracy, and ITR.•Affordable, easy to operate, with adaptively pre-trained PCI.•Potential alternative to BCI in XR environments with further improvements.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0010-4825 1879-0534 1879-0534
DOI:	10.1016/j.compbiomed.2025.111057