SSVEP Speller Implementations Across Desktop, Web, and Mobile Platforms
Rohit Kumar Mishra, Saumya Kushwaha, Aakash Deep, Harsh Koria, Priyanka Jain, Naveen Jain · 2024
This study explores the implementation of steady-state visual evoked potential (SSVEP) stimuli generation across various platforms to advance brain-computer interface (BCI) accessibility and usability. BCls provide assistive capabilities for people with diseases like spinal cord injury (SCI), Stroke, and Amyotrophic lateral sclerosis (ALS) by facilitating direct communication paths between the brain and external equipment Creating visual stimuli at specific frequencies to produce reliable neural responses is a basic challenge for BCls based on SSVEP. We ensured outstanding speed and device and browser synchronization by effectively rendering stimuli on web-based systems using WebGL. To create a seamless user experience, we combined WebGL with Web View for mobile devices, improving rendering speed and compatibility. Our work discusses the implementation of Psychopy and WebGL in providing reliable SSVEP stimuli through this multi-platform method. Comparative findings between desktop, online, and mobile implementations show how flexible and scalable our approach.