Multiplexed neural sensor array of graphene solution-gated field-effect transistors

Schäfer, Nathan; Garcia‐Cortadella, Ramon; Martinez‐Aguilar, Javier; Schwesig, Gerrit; Illa, Xavi; Moya Lara, Ana; Santiago, Sara; Hébert, Clement; Guirado, Gonzalo; Villa, Rosa; Sirota, Anton ; Guimerà-Brunet, Anton und Garrido, Jose A. (2020): Multiplexed neural sensor array of graphene solution-gated field-effect transistors. In: 2D Materials, Bd. 7, Nr. 2, 025046

Abstract

Electrocorticography (ECoG) is a well-established technique to monitor electrophysiological activity from the surface of the brain and has proved crucial for the current generation of neural prostheses and brain–computer interfaces. However, existing ECoG technologies still fail to provide the resolution necessary to accurately map highly localized activity across large brain areas, due to the rapidly increasing size of connector footprint with sensor count. This work demonstrates the use of a flexible array of graphene solution-gated field-effect transistors (gSGFET), exploring the concept of multiplexed readout using an external switching matrix. This approach does not only allow for an increased sensor count, but due to the use of active sensing devices (i.e. transistors) over microelectrodes it makes additional buffer transistors redundant, which drastically eases the complexity of device fabrication on flexible substrates. The presented results pave the way for upscaling the gSGFET technology towards large-scale, high-density μECoG–arrays, eventually capable of resolving neural activity down to a single neuron level, while simultaneously mapping large brain regions.

• Dokumententyp: Zeitschriftenartikel
• EU Funded Grant Agreement Number: info:eu-repo/grantAgreement/EC/H2020/732032
• EU-Projekte: Horizon 2020 > Future & Emerging Technologies Program > 732032: BrainCom - High-density cortical implants for cognitive neuroscience and rehabilitation of speech using brain-computer interfaces
• Fakultät: Medizin > Lehrstuhl für Kognition und Neuronale Plastizität
• Themengebiete: 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften; Biologie ; 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 2053-1583
• Sprache: Englisch