A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface. (12th November 2019)

• Record Type: Journal Article
• Authors: Wang, Po T; Camacho, Everardo; Wang, Ming; Li, Yongcheng; Shaw, Susan J; Armacost, Michelle; Gong, Hui; Kramer, Daniel; Lee, Brian; Andersen, Richard A; Liu, Charles Y; Heydari, Payam; Nenadic, Zoran; Do, An H
• Is Part Of: Journal of neural engineering. Volume 16:Number 6(2019:Dec.)
• Publication Date: 2019-11-12
• Subjects: brain-machine interface -- implantable -- invasive -- electrocorticogram; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ab4b0c
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: 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 h 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.