A Low-Noise, Low-Power EEG Acquisition Node for Scalable Brain-Machine Interfaces Thomas J. Sullivan a and Stephen R. Deiss a and Gert Cauwenberghs a and Tzyy-Ping Jung b,c a Department of Biology b Institute for Neural Computation University of California, San Diego, 9500 Gilman Dr., La Jolla, California, USA c National Chiao Tung University 1001 Ta Hsueh Road, Hsinchu, Taiwan ABSTRACT Electroencephalograph (EEG) recording systems offer a versatile, non-invasive window on the brain’s spatiotem- poral activity for many neuroscience and clinical applications. Our research aims at improving the spatial resolution and mobility of EEG recording by reducing the form factor, power drain and signal fanout of the EEG acquisition node in a scalable sensor array architecture. We present such a node integrated onto a dime- sized circuit board that contains a sensor’s complete signal processing front-end, including amplifier, filters, and analog-to-digital conversion. A daisy-chain configuration between boards with bit-serial output reduces the wiring needed. The circuit’s low power consumption of 423 μW supports EEG systems with hundreds of electrodes to operate from small batteries for many hours. Coupling between the bit-serial output and the highly sensitive analog input due to dense integration of analog and digital functions on the circuit board results in a deterministic noise component in the output, larger than the intrinsic sensor and circuit noise. With software correction of this noise contribution, the system achieves an input-referred noise of 0.277 μVrms in the signal band of 1 to 100 Hz, comparable to the best medical-grade systems in use. A chain of seven nodes using EEG dry electrodes created in micro-electrical-mechanical system (MEMS) technology is demonstrated in a real-world setting. Keywords: EEG, brain-computer interface, brain-machine interface, biosensor 1. INTRODUCTION Electroencephalograph (EEG) systems designed for use on humans record electrical potentials from various locations on the scalp. These potentials are generated by neural activity within the brain and may ultimately shed light on the inner workings of the brain. The EEG sensors are typically made of metal and make contact with the skin through an electrically-conductive gel. The sensors (also called electrodes) are mounted on a cap that the subject wears on the head. These systems have many research and clinical applications. 4, 5 Two issues limit the use of these systems. The first is that electrically-conductive gel is required for a good connection between the sensors and the scalp. This gel takes a lot of time to apply, it limits the realizable density of sensors on the scalp, and it tends to dry out, which limits the recording time. Efforts are currently underway to alleviate these issues by creating MEMS sensors that do not require gel. 2 The second limiting issue with typical EEG systems is that they are not portable. The EEG cap is not portable due to the mass of wires connecting the cap to the data-collecting computer, as well as high power consumption. This is the problem we address here. The circuit described is intended to provide all the analog signal processing for one electrode. The number of wires needed to interface with a large number of electrodes will be reduced since the power, clocks, and measured signals are daisy-chained from one board to another. If all the data are concatenated at each measurement time, then only a couple wires, or a wireless interface, will be sufficient to send the data to a computer for recording. Also, with 256 of these signal processing boards, less than 40 mA would be consumed with a 3V supply. Three rechargeable AAA NiMH batteries with a 900 mAh capacity would be able to power the entire EEG recording system for several hours. Send correspondence to T.J.S. by E-mail: tom@sullivan.to