Publication: Printable and transparent micro-electrocorticography (μECoG) for optogenetic applications
Issued Date
2014-01-01
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2-s2.0-84929484064
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Mahidol University
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SCOPUS
Bibliographic Citation
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. (2014), 482-485
Suggested Citation
Thaninamon Kimtan, Jiyaporn Thupmongkol, Justin C. Williams, Sanitta Thongpang Printable and transparent micro-electrocorticography (μECoG) for optogenetic applications. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. (2014), 482-485. doi:10.1109/EMBC.2014.6943633 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/33749
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Title
Printable and transparent micro-electrocorticography (μECoG) for optogenetic applications
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Abstract
© 2014 IEEE. Micro-electrocorticography (μECoG) displays advantages over traditional invasive methods. The μECoG electrode can record neural activity with high spatial-temporal resolution and it can reduce implantation side effects (e.g. vascular and local-neuronal damage, tissue encapsulation, infection). In this study, we propose a printable transparent μECoG electrode for optogenetic applications by using ultrasonic microfluid printing technique. The device is based on poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT: PSS) as a conductive polymer, polydimethylsiloxane (PDMS) as an insulating polymer and poly(chloro-para-xylylene) (Parylene-C) as the device substrate. We focus on ultrasonic microfluid printing due to its low production cost, excellent material handling capability, and its customizable film thickness (down to 5-20 microns). The ultrasonic fluid-printed μECoG displays high spatial resolution and records simulated signal (0-200 Hz sine wave) effectively with low electrode impedance (50-200 kOhms@1kHz). The μECoG also shows good biocompatibility suitable for customizable chronic implants. This new neural interfacing device could be combined with optogenetics and Brain-Computer Interface (BCI) applications for a possible future use in neurological disease diagnosis and rehabilitations.