We developed a novel EEG system with a dual-electrode net structure for noise reduction and precise biosignal capture. Incorporating advanced software for signal processing, this invention enhances EEG accuracy, reduces setup complexity, and broadens EEG applications, including brain-computer interfaces, through real-time noise separation and immersive noise layering techniques.

In exploring EEG re-referencing techniques, it’s emphasized that re-referencing may unintentionally remove common mode biological signals, crucial for accurate data interpretation. This document from the BRaIN Lab at the University of Central Florida discusses the implications of such data loss and proposes methods to mitigate these effects, ensuring reliable EEG analysis.

This project involved adapting a Desk Cycle to incorporate a controllable resistance mechanism and a tachometer, both interfaced with a Raspberry Pi Zero W using I2C and PWM protocols. The customized device enables wireless communication of exercise data to Simulink for analysis. The integration of mechanical design, control systems, and electronic communication demonstrates innovative solutions to enhance interactive locomotion exercises, aiming to improve neuromuscular engagement and potentially benefit balance and fall-risk assessment in elderly populations.