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A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface

Wang, Po T. and Camacho, Everardo and Wang, Ming and Li, Yongcheng and Shaw, Susan J. and Armacost, Michelle and Gong, Hui and Kramer, Daniel and Lee, Brian and Andersen, Richard A. and Liu, Charles Y. and Heydari, Payam and Nenadic, Zoran and Do, An H. (2019) A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface. Journal of Neural Engineering, 16 (6). Art. No. 066043. ISSN 1741-2560. PMCID PMC7271898. doi:10.1088/1741-2552/ab4b0c.

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Objective: State-of-the-art invasive brain-machine interfaces (BMIs) have shown significant promise, but rely on external electronics and wired connections between the brain and these external components. This configuration presents health risks and limits practical use. These limitations can be addressed by designing a fully implantable BMI similar to existing FDA-approved implantable devices. Here, a prototype BMI system whose size and power consumption are comparable to those of fully implantable medical devices was designed and implemented, and its performance was tested at the benchtop and bedside. Approach: A prototype of a fully implantable BMI system was designed and implemented as a miniaturized embedded system. This benchtop analogue was tested in its ability to acquire signals, train a decoder, perform online decoding, wirelessly control external devices, and operate independently on battery. Furthermore, performance metrics such as power consumption were benchmarked. Main results: An analogue of a fully implantable BMI was fabricated with a miniaturized form factor. A patient undergoing epilepsy surgery evaluation with an electrocorticogram (ECoG) grid implanted over the primary motor cortex was recruited to operate the system. Seven online runs were performed with an average binary state decoding accuracy of 87.0% (lag optimized, or 85.0% at fixed latency). The system was powered by a wirelessly rechargeable battery, consumed ~150 mW, and operated for >60 hours on a single battery cycle. Significance: The BMI analogue achieved immediate and accurate decoding of ECoG signals underlying hand movements. A wirelessly rechargeable battery and other supporting functions allowed the system to function independently. In addition to the small footprint and acceptable power and heat dissipation, these results suggest that fully implantable BMI systems are feasible.

Item Type:Article
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URLURL TypeDescription CentralArticle
Wang, Po T.0000-0002-6611-3863
Kramer, Daniel0000-0003-4551-2977
Andersen, Richard A.0000-0002-7947-0472
Additional Information:© 2019 IOP Publishing Ltd. Received 19 June 2019; Revised 23 September 2019; Accepted 4 October 2019; Accepted Manuscript online 4 October 2019. The authors would like to thank Angelica Nguyen for her assistance in setting up the experiments. The authors declare no conflict of interest. This work was supported by the National Science Foundation awards 1446908 and 1646275.
Funding AgencyGrant Number
Issue or Number:6
PubMed Central ID:PMC7271898
Record Number:CaltechAUTHORS:20191014-144810554
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Official Citation:Po T Wang et al 2019 J. Neural Eng. 16 066043
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99262
Deposited By: Tony Diaz
Deposited On:14 Oct 2019 21:58
Last Modified:16 Feb 2022 18:59

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