Literature DB >> 17873433

A single-chip signal processing and telemetry engine for an implantable 96-channel neural data acquisition system.

Michael Rizk1, Iyad Obeid, Stephen H Callender, Patrick D Wolf.   

Abstract

A fully implantable neural data acquisition system is a key component of a clinically viable cortical brain-machine interface. We present the design and implementation of a single-chip device that serves the processing needs of such a system. Our device processes 96 channels of multi-unit neural data and performs all digital processing necessary for bidirectional wireless communication. The implementation utilizes a single programmable logic device that is responsible for performing data reduction on the 96 channels of neural data, providing a bidirectional telemetry interface to a transceiver and performing command interpretation and system supervision. The device takes as input neural data sampled at 31.25 kHz and outputs a line-encoded serial bitstream containing the information to be transmitted by the transceiver. Data can be output in one of the following four modes: (1) streaming uncompressed data from a single channel, (2) extracted spike waveforms from any subset of the 96 channels, (3) 1 ms bincounts for each channel or (4) streaming data along with extracted spikes from a single channel. The device can output up to 2000 extracted spikes per second with latencies suitable for a brain-machine interface application. This device provides all of the digital processing components required by a fully implantable system.

Mesh:

Year:  2007        PMID: 17873433     DOI: 10.1088/1741-2560/4/3/016

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  17 in total

1.  Minimum requirements for accurate and efficient real-time on-chip spike sorting.

Authors:  Joaquin Navajas; Deren Y Barsakcioglu; Amir Eftekhar; Andrew Jackson; Timothy G Constandinou; Rodrigo Quian Quiroga
Journal:  J Neurosci Methods       Date:  2014-04-24       Impact factor: 2.390

2.  A Fully Implantable, Programmable and Multimodal Neuroprocessor for Wireless, Cortically Controlled Brain-Machine Interface Applications.

Authors:  Fei Zhang; Mehdi Aghagolzadeh; Karim Oweiss
Journal:  J Signal Process Syst       Date:  2011-06-15

3.  Listening to Brain Microcircuits for Interfacing With External World-Progress in Wireless Implantable Microelectronic Neuroengineering Devices: Experimental systems are described for electrical recording in the brain using multiple microelectrodes and short range implantable or wearable broadcasting units.

Authors:  Arto V Nurmikko; John P Donoghue; Leigh R Hochberg; William R Patterson; Yoon-Kyu Song; Christopher W Bull; David A Borton; Farah Laiwalla; Sunmee Park; Yin Ming; Juan Aceros
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2010       Impact factor: 10.961

4.  A 100-channel hermetically sealed implantable device for chronic wireless neurosensing applications.

Authors:  Ming Yin; David A Borton; Juan Aceros; William R Patterson; Arto V Nurmikko
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2013-04       Impact factor: 3.833

5.  A wideband dual-antenna receiver for wireless recording from animals behaving in large arenas.

Authors:  Seung Bae Lee; Ming Yin; Joseph R Manns; Maysam Ghovanloo
Journal:  IEEE Trans Biomed Eng       Date:  2013-02-15       Impact factor: 4.538

6.  An implantable wireless neural interface for recording cortical circuit dynamics in moving primates.

Authors:  David A Borton; Ming Yin; Juan Aceros; Arto Nurmikko
Journal:  J Neural Eng       Date:  2013-02-21       Impact factor: 5.379

7.  Wireless, high-bandwidth recordings from non-human primate motor cortex using a scalable 16-Ch implantable microsystem.

Authors:  David A Borton; Yoon-Kyu Song; William R Patterson; Christopher W Bull; Sunmee Park; Farah Laiwalla; John P Donoghue; Arto V Nurmikko
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

Review 8.  The science of neural interface systems.

Authors:  Nicholas G Hatsopoulos; John P Donoghue
Journal:  Annu Rev Neurosci       Date:  2009       Impact factor: 12.449

9.  Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level.

Authors:  Michael Rizk; Patrick D Wolf
Journal:  Med Biol Eng Comput       Date:  2009-02-10       Impact factor: 2.602

10.  Neuroprosthetic devices: how far are we from recovering movement in paralyzed patients?

Authors:  Joseph J Pancrazio; P Hunter Peckham
Journal:  Expert Rev Neurother       Date:  2009-04       Impact factor: 4.618

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