Article: Multimodal simulation for diagnosing microstrabismus integrating electrooculogram and electroencephalographic signals Journal: International Journal of Simulation and Process Modelling (IJSPM) 2026 Vol.23 No.3 pp.169 - 183 Abstract: Microstrabismus is difficult to diagnose using conventional methods due to its subtle presentation and high risk of misdiagnosis. To overcome this limitation, we propose a multimodal diagnostic framework that integrates electrooculogram (EOG) and electroencephalogram (EEG) signals. The framework first preprocesses both signal types and applies principal component analysis to remove redundancy while preserving essential information. A dual-branch architecture then extracts spatio-temporal features from each modality, which are subsequently fused through a multi-layer interaction mechanism designed to capture cross-modal complementarity. To handle inter-sample variability, a gating-based module dynamically adjusts the fusion ratio between modalities according to individual sample characteristics. Experimental results indicate that the proposed model improves diagnostic accuracy by at least 6.35% over baseline methods, demonstrating strong potential for aiding the precise diagnosis of micro-degree strabismus. Inderscience Publishers - linking academia, business and industry through research

Title: Multimodal simulation for diagnosing microstrabismus integrating electrooculogram and electroencephalographic signals

Authors: Tong Yang; Shaoyun Bai

Addresses: College of Computer Science and Technology, Shanxi University of Electronic Science and Technology, Linfen, 041000, China ' College of Computer Science and Technology, Shanxi University of Electronic Science and Technology, Linfen, 041000, China

Abstract: Microstrabismus is difficult to diagnose using conventional methods due to its subtle presentation and high risk of misdiagnosis. To overcome this limitation, we propose a multimodal diagnostic framework that integrates electrooculogram (EOG) and electroencephalogram (EEG) signals. The framework first preprocesses both signal types and applies principal component analysis to remove redundancy while preserving essential information. A dual-branch architecture then extracts spatio-temporal features from each modality, which are subsequently fused through a multi-layer interaction mechanism designed to capture cross-modal complementarity. To handle inter-sample variability, a gating-based module dynamically adjusts the fusion ratio between modalities according to individual sample characteristics. Experimental results indicate that the proposed model improves diagnostic accuracy by at least 6.35% over baseline methods, demonstrating strong potential for aiding the precise diagnosis of micro-degree strabismus.

Keywords: strabismus diagnosis; multimodal feature fusion; EOG signal; EEG signal; attention mechanism.

DOI: 10.1504/IJSPM.2026.156729

International Journal of Simulation and Process Modelling, 2026 Vol.23 No.3, pp.169 - 183

Received: 08 Dec 2025
Accepted: 17 May 2026
Published online: 01 Oct 2026 *

Title: Multimodal simulation for diagnosing microstrabismus integrating electrooculogram and electroencephalographic signals

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