Reducing noise is paramount in EEG research, especially when the signal-to-noise ratio is low, as high noise levels can obscure significant findings and hinder publication. While traditional noise reduction methods are often cumbersome, Steady-State Visually Evoked Potentials (SSVEPs) offer a powerful alternative to enhance signal visibility.
Steady-State Visually Evoked Potentials (SSVEPs) are brain signals that synchronize with flickering visual stimuli, making them a popular and reliable tool for Brain-Computer Interfaces (BCIs). By detecting which flickering frequency matches a user's neural activity, SSVEP-based BCIs can enable control over external systems with high accuracy and minimal training. This post delves into SSVEPs, illustrating their application with examples of online and offline classifiers developed using the Mentalab Explore system, freely available for researchers to build upon.