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Electrocorticography (ECoG) is an important neuroscientific tool for acquiring information about brain states and mesoscopic neural activity. Additionally, the use of ECoG and the higher resolution technique micro-ECoG (μECoG) show promise in Brain-Machine Interface (BMI) applications for motor and speech prosthetics. Commercially available μECoG arrays made through photolithographic and vapor-deposition processes allow neuroscientists to incorporate μECoG into their studies, however, these electrode arrays can be expensive and do not lend themselves to easy reconfigurability for experiment-specific electrode layouts. Here we show a process for patterning μECoG electrode arrays using inkjet printing on a 50 μm thick PET substrate. With inkjet printing, we achieve electrode active areas with 300 μm diameters and interconnects with a 500 μm pitch. These electrode arrays demonstrate an average impedance of 2.5 kΩ at 100 Hz and were used to record local field potentials from the mouse somatosensory cortex with signal-to-noise ratios between 30-45 dB. Our results demonstrate the feasibility of using inkjet-patterned μECoG electrode arrays in future neuroscientific studies. Furthermore, we expect printed μECoG arrays to be compatible with roll-to-roll processing for high-throughput and low-cost manufacturing, decreasing the cost-barrier for neuroscientists seeking to incorporate customizable μECoG electrode arrays into their experimental design.
|Original language||English (US)|
|Title of host publication||9th International IEEE EMBS Conference on Neural Engineering, NER 2019|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|State||Published - May 16 2019|
|Event||9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States|
Duration: Mar 20 2019 → Mar 23 2019
|Name||International IEEE/EMBS Conference on Neural Engineering, NER|
|Conference||9th International IEEE EMBS Conference on Neural Engineering, NER 2019|
|Period||3/20/19 → 3/23/19|
Bibliographical noteFunding Information:
* This work was supported by the Office of the Vice President for Research and the MnDRIVE RSAM Initiative, University of Minnesota. A portion of this work was carried out in the Minnesota Nano Center which receives partial support from the National Science Foundation through the NNCI program. Part of this work was carried out in the College of Science and Engineering Characterization Facility, University of Minnesota, which has received capital equipment funding from the NSF through the UMN MRSEC program under Award number DMR-1420013. This research was supported in part by NSF IGERT grant DGE-1069104.
© 2019 IEEE.
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