Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems

This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG) recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode...

Full description

Bibliographic Details
Main Authors: Sun-Il Chang, Sung-Yun Park, Euisik Yoon
Format: Article
Language:English
Published: MDPI AG 2018-01-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/18/1/263
id doaj-1c6077c466a9466f8fcee6b7af69075c
record_format Article
spelling doaj-1c6077c466a9466f8fcee6b7af69075c2020-11-25T00:05:01ZengMDPI AGSensors1424-82202018-01-0118126310.3390/s18010263s18010263Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording SystemsSun-Il Chang0Sung-Yun Park1Euisik Yoon2Apple Incorporated, Cupertino, CA 95014, USADepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USADepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USAThis paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG) recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM) module. The core integrated circuit (IC) consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC) with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm2 and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µVrms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.http://www.mdpi.com/1424-8220/18/1/263Electrocorticogram (ECoG)low-powerlow-noiseneural recordingpush-pull double-gated amplifierintra-skin communication (ISCOM)neural interface
collection DOAJ
language English
format Article
sources DOAJ
author Sun-Il Chang
Sung-Yun Park
Euisik Yoon
spellingShingle Sun-Il Chang
Sung-Yun Park
Euisik Yoon
Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
Sensors
Electrocorticogram (ECoG)
low-power
low-noise
neural recording
push-pull double-gated amplifier
intra-skin communication (ISCOM)
neural interface
author_facet Sun-Il Chang
Sung-Yun Park
Euisik Yoon
author_sort Sun-Il Chang
title Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
title_short Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
title_full Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
title_fullStr Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
title_full_unstemmed Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
title_sort minimally-invasive neural interface for distributed wireless electrocorticogram recording systems
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2018-01-01
description This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG) recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM) module. The core integrated circuit (IC) consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC) with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm2 and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µVrms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.
topic Electrocorticogram (ECoG)
low-power
low-noise
neural recording
push-pull double-gated amplifier
intra-skin communication (ISCOM)
neural interface
url http://www.mdpi.com/1424-8220/18/1/263
work_keys_str_mv AT sunilchang minimallyinvasiveneuralinterfacefordistributedwirelesselectrocorticogramrecordingsystems
AT sungyunpark minimallyinvasiveneuralinterfacefordistributedwirelesselectrocorticogramrecordingsystems
AT euisikyoon minimallyinvasiveneuralinterfacefordistributedwirelesselectrocorticogramrecordingsystems
_version_ 1725426718255087616