Brain‐Computer Interfaces Using Flexible Electronics: An a‐IGZO Front‐End for Active ECoG Electrodes

Brain-computer interfaces (BCIs) are evolving toward higher electrode count and fully implantable solutions, which require extremely low power densities (<15mW cm<sup>-2</sup>). To achieve this target, and allow for a large and scalable number of channels, flexible electronics can be used as a multiplexing interface. This work introduces an active analog front-end fabricated with amorphous Indium-Gallium-Zinx-Oxide (a-IGZO) Thin-Film Transistors (TFTs) on foil capable of active matrix multiplexing. The circuit achieves only 70nV per sqrt(Hz) input referred noise, consuming 46µW, or 3.5mW cm<sup>-2</sup>. It demonstrates for the first time in literature a flexible front-end with a noise efficiency factor comparable with Silicon solutions (NEF = 9.8), which is more than 10X lower compared to previously reported flexible front-ends. These results have been achieved using a modified bootstrap-load amplifier. The front end is tested by playing through it recordings obtained from a conventional BCI system. A gesture classification based on the flexible front-end outputs achieves 94% accuracy. Using a flexible active front end can improve the state-of-the-art in high channel count BCI systems by lowering the multiplexer noise and enabling larger areas of the brain to be monitored while reducing power density. Therefore, this work enables a new generation of high channel-count active BCI electrode grids.

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Publication Details

Journal
Advanced Science
Published
2024-12-18
DOI
https://doi.org/10.1002/advs.202408576
Citations
3
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.03

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article

Brain‐Computer Interfaces Using Flexible Electronics: An a‐IGZO Front‐End for Active ECoG Electrodes

Mariana P. Branco, Erik J. Aarnoutse, Martijn J.T.N. Timmermans, Kyle van Oosterhout et al.
3 citations
Advanced Science
EEG and Brain-Computer Interfaces
1.03
article

Brain‐Computer Interfaces Using Flexible Electronics: An a‐IGZO Front‐End for Active ECoG Electrodes

Mariana P. Branco, Erik J. Aarnoutse, Martijn J.T.N. Timmermans, Kyle van Oosterhout, Eugenio Cantatore, Nick F. Ramsey, Marco Fattori, Ashley Chilundo
article en
3 citations

Abstract

Brain-computer interfaces (BCIs) are evolving toward higher electrode count and fully implantable solutions, which require extremely low power densities (<15mW cm<sup>-2</sup>). To achieve this target, and allow for a large and scalable number of channels, flexible electronics can be used as a multiplexing interface. This work introduces an active analog front-end fabricated with amorphous Indium-Gallium-Zinx-Oxide (a-IGZO) Thin-Film Transistors (TFTs) on foil capable of active matrix multiplexing. The circuit achieves only 70nV per sqrt(Hz) input referred noise, consuming 46µW, or 3.5mW cm<sup>-2</sup>. It demonstrates for the first time in literature a flexible front-end with a noise efficiency factor comparable with Silicon solutions (NEF = 9.8), which is more than 10X lower compared to previously reported flexible front-ends. These results have been achieved using a modified bootstrap-load amplifier. The front end is tested by playing through it recordings obtained from a conventional BCI system. A gesture classification based on the flexible front-end outputs achieves 94% accuracy. Using a flexible active front end can improve the state-of-the-art in high channel count BCI systems by lowering the multiplexer noise and enabling larger areas of the brain to be monitored while reducing power density. Therefore, this work enables a new generation of high channel-count active BCI electrode grids.

Advanced ScienceVol. 12(6)
University Medical Center Utrecht (NL), Eindhoven University of Technology (NL)
Technische Universiteit Eindhoven, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Universitair Medisch Centrum Utrecht, Stichting voor de Technische Wetenschappen
Affordable and clean energy
Openalex Percentile: Top 23%
EEG and Brain-Computer Interfaces
1.03
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