A low-cost, multiplexed electrophysiology system for chronic μECoG recordings in rodents

Micro-Electrocorticography (μECoG) offers a minimally invasive, high resolution interface with large areas of cortex. However, large arrays of electrodes with many contacts that are individually wired to external recording systems are cumbersome and make chronic recording in freely behaving small animals challenging. Multiplexed headstages overcome this limitation by combining the signals from many electrodes to a smaller number of connections directly on the animal's head. Commercially available multiplexed headstages provide high performance integrated amplification, multiplexing and analog to digital conversion[1], [2]. However, the cost of these systems can be prohibitive for small labs or for experiments that require a large number of animals to be continuously recorded at the same time. Here we have developed a multiplexed 60-channel headstage amplifier optimized to chronically record electrophysiological signals from high-density μECoG electrode arrays. A single, ultraflexible (2mm thickness) microHDMI cable provided the data interface. Using low cost components, we have reduced the cost of the multiplexed headstage to ~$125. Paired with a custom interface printed circuit board (PCB) and a general purpose data acquisition system (M-series DAQ, National Instruments), an inexpensive and customizable electrophysiology system is assembled. Open source LabVIEW software that we have previously released [3] controlled the system. It can also be used with other open source neural data acquisition packages [4][5]. Combined, we have presented a scalable, low-cost platform for high-channel count electrophysiology.

[1]  D. Moses,et al.  Sub-mm functional decoupling of electrocortical signals through closed-loop BMI learning , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  S. A. Shamma,et al.  MANTA—an open-source, high density electrophysiology recording suite for MATLAB , 2013, Front. Neural Circuits.

[3]  Joost B. Wagenaar,et al.  Data acquisition system for high resolution, multiplexed electrode arrays , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).