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

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 dry-contact 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, Alters, and analog-to-digital conversion. A daisy-chain configuration between boards with bit-serial 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 muVrms in the signal band of 1 to 100 Hz, comparable to the best medical-grade systems in use. Noise behavior across the daisy-chain is characterized, alpha-band rhythms are detected, and an eye-blink study is demonstrated.