A Brain-Machine Interface using Dry-Contact, Low-Noise EEG Sensors

Abstract

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 realworld

setting. Consuming just 3 mW, it is suitable for mobile

applications. The system achieves an input-referred noise of 0.28

—Electroencephalograph (EEG) recording systems offer

μ

medical-grade systems in use. Noise behavior across the daisychain

is characterized, alpha-band rhythms are detected, and an

eye-blink study is demonstrated.

Thomas J. Sullivan, Stephen R. Deiss

Division of Biological Sciences

UC San Diego

La Jolla, CA

Email: tom@sullivan.to, sdeiss@ucsd.edu

Tzyy-Ping Jung

Inst. for Neural Computation, UCSD

and National Chiao Tung University

Hsinchu, Taiwan, ROC

Email: jung@sccn.ucsd.edu

Gert Cauwenberghs

Division of Biological Sciences

UC San Diego

La Jolla, CA

Email: gert@ucsd.eduVrms in the signal band of 1 to 100 Hz, comparable to the best