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.
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