A brain-machine interface using dry-contact, low-noise EEG sensors

TLDR

Electroencephalograph (EEG) recording systems provide a versatile, non‑invasive window into the brain’s spatiotemporal activity for neuroscience and clinical use. This work seeks to enhance EEG convenience and mobility by eliminating conductive gel and developing dry‑contact sensors that integrate into a scalable array. The authors fabricate MEMS‑based dry‑contact electrodes and mount each channel on a custom dime‑sized board with amplifier, filters, and ADC, then link boards in a daisy‑chain to reduce wiring. A seven‑sensor system was validated in a real‑world setting, consuming only 3 mW, achieving 0.28 µVrms input‑referred noise over 1–100 Hz, and successfully detecting alpha rhythms and eye blinks while characterizing noise across the daisy‑chain.

Abstract

Electroencephalograph (EEG) recording systems offer a versatile, non-invasive window on the brain's spatiotemporal activity for many neuroscience and clinical applications. Our research aims to improve the convenience and mobility of EEG recording by eliminating the need for conductive gel and creating sensors that fit into a scalable array architecture. The EEG drycontact electrodes are created with micro-electrical-mechanical system (MEMS) technology. Each channel of our analog signal processing front-end comes on a custom-built, dime-sized circuit board which contains an amplifier, filters, and analog-to-digital conversion. A daisy-chain configuration between boards with bitserial output reduces the wiring needed. A system consisting of seven sensors is demonstrated in a real-world setting. Consuming just 3 mW, it is suitable for mobile applications. The system achieves an input-referred noise of 0.28 μVrms in the signal band of 1 to 100 Hz, comparable to the best medical-grade systems in use. Noise behavior across the daisychain is characterized, alpha-band rhythms are detected, and an eye-blink study is demonstrated.