Decoding visual disorders: quantifying SSVEP responses to differentiate simulated visual field defects - Paing Min Htet (Nanyang Technological University, 2024)
Background: Early detection of visual field anomalies is vital in the prevention of irreversible vision losses such as glaucoma. Steady State Visual Evoked Potential (SSVEP) based Brain Computer Interface (BCI) paradigm identifies such abnormal vision conditions at an early stage. This study investigates how novel SSVEP stimuli design is effective in detecting visual field anomalies. Methods: We designed and developed an SSVEP based visual field assessment in Python using Psychopy library to evaluate three conditions: Full Vision, Bitemporal Hemianopsia and Bitemporal Hemianopsia with Blind Spot. To mimic these visual field defects, I used custom-designed goggles. I collected data from 14 healthy subjects using the 64-channel EEG system. Analysis & Findings: We analysed EEG data using different signal processing and data-driven analysis techniques and this approach allowed for the examination of the SSVEP responses’ characteristics and their correlation with the simulated visual field conditions. Although we only managed to complete the initial data analysis, preliminary insights suggest the SSVEP multifocal design with a multi-group presentation paradigm can classify these visual field defects. Further analysis outcomes are anticipated to offer foundational evidence on the system’s effectiveness in early detection of visual field anomalies. Conclusion: The proposed SSVEP-based BCI system shows promising results for the objective detection of stimulated visual field defects. Pending a comprehensive analysis, further experiments are still required to validate the efficacy. Nevertheless, our findings could pave the way for objective detection of visual impairments enabling reliable early detection of peripheral visual field losses.