Publication

Flexible Cylindrical Neural Probe With Graphene Enhanced Conductive Polymer For Multi-Mode Bci Applications

Yi Zhang
2017
Conference Papers

Abstract

This paper reports a novel flexible neural probe fabricated by cylindrical substrates lithography for Brain-Computer-Interface (BCI) applications. The electrode sites were patterned on the cylindrical surface to acquire high space selectivity and the micro channel was integrated to deliver drags or optical stimulation. The electrode sites of the probe were further modified with graphene enhanced conductive polymer composite to improve the interface performance for neural electrical recording and stimulation. The results show the composite coated probe has extreme high charge storage capacity (CSC), low impedance and good stability, which will become a promising BCI device.

Official source

https://infoscience.epfl.ch/entities/publication/2019ff33-32f9-4d21-9830-8bdf98e40d4d

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Yi Zhang
2017
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/2019ff33-32f9-4d21-9830-8bdf98e40d4d

About this result

Related concepts (32)

Brain–computer interface

A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI) or smartbrain, is a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary component of the physical movement of body parts, although they also raise the possibility of the erasure of the discreteness of brain and machine.

User interface

In the industrial design field of human–computer interaction, a user interface (UI) is the space where interactions between humans and machines occur. The goal of this interaction is to allow effective operation and control of the machine from the human end, while the machine simultaneously feeds back information that aids the operators' decision-making process. Examples of this broad concept of user interfaces include the interactive aspects of computer operating systems, hand tools, heavy machinery operator controls and process controls.

Graphene

Graphene (ˈgræfiːn) is an allotrope of carbon consisting of a single layer of atoms arranged in a hexagonal lattice nanostructure. The name is derived from "graphite" and the suffix -ene, reflecting the fact that the graphite allotrope of carbon contains numerous double bonds. Each atom in a graphene sheet is connected to its three nearest neighbors by σ-bonds and a delocalised π-bond, which contributes to a valence band that extends over the whole sheet.

Related publications (32)

Flexible Cylindrical Neural Probe With Graphene Enhanced Conductive Polymer For Multi-Mode Bci Applications

Chat with Graph Search

Modeling, fabrication and validation of 3D neural interfaces for peripheral nerves and brain organoids

Customizing the human-avatar mapping based on EEG error related potentials during avatar-based interaction

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes