Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia

John D Simeral, Thomas Hosman, Jad Saab, Sharlene N Flesher, Marco Vilela, Brian Franco, Jessica N Kelemen, David M Brandman, John G Ciancibello, Paymon G Rezaii, Emad N Eskandar, David M Rosler, Krishna V Shenoy, Jaimie M Henderson, Arto V Nurmikko, Leigh R Hochberg, John D Simeral, Thomas Hosman, Jad Saab, Sharlene N Flesher, Marco Vilela, Brian Franco, Jessica N Kelemen, David M Brandman, John G Ciancibello, Paymon G Rezaii, Emad N Eskandar, David M Rosler, Krishna V Shenoy, Jaimie M Henderson, Arto V Nurmikko, Leigh R Hochberg

Abstract

Objective: Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-and-click control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI.

Methods: Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial completed on-screen item selection tasks to assess iBCI-enabled cursor control.

Results: Communication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home.

Significance: Wireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to high-performance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment.

Conflict of interest statement

Conflict of Interest: BF is currently with NeuroPace, Inc., and holds stock options in the company. The MGH Translational Research Center has a clinical research support agreement with Neuralink, Paradromics, and Synchron, for which LRH provides consultative input. KVS is a consultant to Neuralink Corp. and CTRL-Labs Inc. (now part of Facebook Reality Labs) and is on the scientific advisory boards of Inscopix Inc., Heal Inc. and Mind-X. JMH is a consultant for Neuralink Corp. and serves on the Medical Advisory Board of Enspire DBS.

Figures

Fig. 1.
Fig. 1.
Components of the cabled and wireless systems for dual-array recording. Pathways for neural signal acquisition differed as shown, but NSPs and all downstream file recording, signal processing and decoding hardware and software were the same for both systems. Ant.: antenna; f.o.: fiber optic.
Fig. 2.
Fig. 2.
Some components of the wireless system, (a) BWD transmitter (52 mm x 44 mm) showing battery compartment. Turn-screw disc is used to attach the device onto a percutaneous pedestal, (b) The BWD connected to T10’s posterior pedestal (here, the anterior pedestal is covered by a protective cap), (c) A two-frequency wireless receiver system in a four-antenna configuration as deployed for T10. The output optical fibers (orange) connect to downstream NSPs. (d) T5 in his home with two transmitters. The antenna in the background was one of four mounted around the room. Photos used with permission.
Fig. 3.
Fig. 3.
Metrics comparing closed-loop cursor control in wired (light blue) and wireless (dark blue) configurations. (a) Median target acquisition rates in wired and wireless conditions. Circles indicate the measure for each iteration of the Grid Task across two sessions for each participant (white and black used for contrast). (b) Bitrates in wired and wireless conditions (one measure for each Grid Task across two sessions for each participant). (c) Three metrics of cursor control over two days for each participant. Each point shows the metric computed for an individual trial (one target acquisition). Points are spread on each X-axis to reveal individual trials. Histograms on the right of each plot summarize wired and wireless performance. ‘x’ indicates an angle error exceeding 90 degrees. ‘*’ indicates significant difference (P<0.05).
Fig. 4.
Fig. 4.
Spectral content of T10 neural data recorded continuously over 24 hours with the wireless system (posterior pedestal). X-axis indicates wall-clock time. Dark vertical bars reflect periods where data was not recorded (e.g., transmitters removed) or was severely disrupted (high frame loss). Peaks in the spectral power are noted on the right at 10.6 Hz and 19.6 Hz.
Fig. 5.
Fig. 5.
Comparison of wired and wireless recordings of simulated neural signals, (a) Waveforms of three different spiking neurons aligned from bandpass-filtered wired and wireless recordings, (b) Distribution of baseline noise in the spike-filtered data (250 Hz – 7.5 kHz) across all 96 channels. Noise was measured as RMS power of the residual signal after all spike events were removed. Triangles indicate median noise values, (c) Distribution of noise values in the field potential range (5 Hz – 250 Hz) computed for each channel after removing the 96-channel mean signal recorded in the wired condition.
Fig. 6.
Fig. 6.
Human intracortical signals recorded in wired and wireless configurations in the home, (a) Comparison of recorded neural activity on one electrode (t10 trial day 361, e24 blocks 6, 7). Top: the “raw” unfiltered neural signal. Middle: low-pass filtered (100 Hz cutoff). Bottom: band pass filtered for spike extraction (250 Hz — 7.5 kHz). (b) Distribution of residual RMS amplitudes from all electrodes on one array after band-pass filtering and removing thresholded spikes for wired (light blue) and wireless (dark blue) recordings, (c) Sample waveforms from two units sorted from the same electrode shown in (a) and (b). Light blue (wired) and dark blue (wireless) waveforms show substantial similarity.

References

    1. Hochberg LR et al., “Neuronal ensemble control of prosthetic devices by a human with tetraplegia”, Nature, vol. 442, no. 7099, pp. 164–171, 2006.
    1. Hochberg LR et al., “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm”, Nature, vol. 485, no. 7398, pp. 372–5, 2012.
    1. Collinger JL et al., “High-performance neuroprosthetic control by an individual with tetraplegia”, Lancet, vol. 381, no. 9866, pp. 557–64, 2013.
    1. Jarosiewicz B et al., “Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface”, Sci. Transl. Med, vol. 7, no. 313, p. 313ra179, 2015.
    1. Aflalo T et al., “Decoding motor imagery from the posterior parietal cortex of a tetraplegic human”, Science, vol. 348, no. 6237, pp. 906–910, 2015.
    1. Bouton CE et al., “Restoring cortical control of functional movement in a human with quadriplegia”, Nature, vol. 533, no. 7602, pp. 247–250, 2016.
    1. Pandarinath C et al., “High performance communication by people with paralysis using an intracortical brain-computer interface”, Elife, vol. 6, p. e18554, 2017.
    1. Nuyujukian P et al., “Cortical control of a tablet computer by people with paralysis”, PLoS One, vol. 13, no. 11, p. e0204566, 2018.
    1. Stavisky SD et al., “Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis”, Elife, vol. 8, 2019.
    1. Stavisky SD et al., “Speech-related dorsal motor cortex activity does not interfere with iBCI cursor control”, J. Neural Eng, vol. 17, no. 1, p. 016049, 2020.
    1. Wodlinger B et al., “Ten-dimensional anthropomorphic arm control in a human brain–machine interface: difficulties, solutions, and limitations”, J. Neural Eng, vol. 12, no. 1, p. 016011, 2015.
    1. Ajiboye AB et al., “Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration”, Lancet, vol. 389, no. 10081, pp. 1821–1830, 2017.
    1. Bacher D et al., “Neural point-and-click communication by a person with incomplete locked-in syndrome”, Neurorehabil. Neural Repair, vol. 29, no. 5, pp. 462–71, 2015.
    1. Kim S-PP et al., “Point-and-click cursor control with an intracortical neural interface system by humans with tetraplegia”, IEEE Trans Neural Syst Rehabil Eng, vol. 19, no. 2, pp. 193–203, 2011.
    1. Simeral JD et al., “Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array,” J Neural Eng, vol. 8, no. 2, p. 025027, 2011.
    1. Gilja V et al., “Clinical translation of a high-performance neural prosthesis”, Nat. Med, vol. 21, no. 10, pp. 1142–1145, 2015.
    1. Perge JA et al., “Intra-day signal instabilities affect decoding performance in an intracortical neural interface system”, J. Neural Eng, vol. 10, no. 3, p. 036004, 2013.
    1. Perge JA et al., “Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex”, J. Neural Eng, vol. 11, no. 4, p. 046007, 2014.
    1. Homer ML et al., “Adaptive offset correction for intracortical brain-computer interfaces”, IEEE Trans. Neural Syst. Rehabil. Eng, vol. 22, no. 2, pp. 239–48, 2014.
    1. Milekovic T et al., “Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals”, J. Neurophysiol, vol. 120, no. 1, pp. 343–360, 2018.
    1. Even-Chen N et al., “Power-saving design opportunities for wireless intracortical brain–computer interfaces”, Nat. Biomed. Eng, vol. 4, no. 10, pp. 984–996, 2020.
    1. Nason SR et al., “A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces”, Nat. Biomed. Eng, pp. 1–11, 2020.
    1. Slutzky MW, “Increasing power efficiency”, Nat. Biomed Eng, vol. 4, pp. 937–938, 2020.
    1. Yin M et al., “An externally head-mounted wireless neural recording device for laboratory animal research and possible human clinical use”, in, Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, 3109–14.
    1. Yin M et al., “Wireless neurosensor for lull-spectrum electrophysiology recordings during free behavior”, Neuron, vol. 84, no. 6, pp. 1170–1182, 2014.
    1. Borton DA et al., “An implantable wireless neural interface for recording cortical circuit dynamics in moving primates”, J. Neural Eng, vol. 10, p.26010, 2013.
    1. Yin M et al., “A 100-channel hermetically sealed implantable device for chronic wireless neurosensing applications”, IEEE Trans. Biomed Circuits Syst, vol. 7, pp. 115–128, 2013.
    1. Brandman DM et al., “Rapid calibration of an intracortical brain–computer interface for people with tetraplegia”, J. Neural Eng, vol. 15, no. 2, p. 026007, 2018.
    1. Nuyujukian P et al., “A high-performance keyboard neural prosthesis enabled by task optimization”, IEEE Trans. Biomed Eng, pp. 1–9, 2015.
    1. Vargas-Irwin C and Donoghue JP, “Automated spike sorting using density grid contour clustering and subtractive waveform decomposition”,J. Neurosci. Methods, vol. 164, no. 1, pp. 1–18, 2007.
    1. Perel S et al., “Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics”, J. Neurophysiol, vol. 114, no. 3, pp. 1500–1512, 2015.
    1. Todorova S et al., “To sort or not to sort: the impact of spike-sorting on neural decoding performance.”, J Neural Eng, vol. 11, no. 5, p. 056005, 2014.
    1. Christie BP et al., “Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance”,J Neural Eng, vol. 12, no. 1, p. 016009, 2015.
    1. Dai J et al., “Reliability of motor and sensory neural decoding by threshold crossings for intracortical brain–machine interface”, J Neural Eng, vol. 16, no. 3, p. 036011, 2019.
    1. Fraser GW et al., “Control of a brain-computer interface without spike sorting”, J Neural Eng, vol. 6, no. 5, p. 055004, 2009.
    1. Chestek CA et al., “Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex”, J. Neural Eng, vol. 8, no. 4, p. 045005, 2011.
    1. Trautmann EM et al., “Accurate estimation of neural population dynamics without spike sorting”, Neuron, vol. 103, no. 2, pp. 292–308.e4, 2019.
    1. Steinmetz NA et al., “Challenges and opportunities for large-scale electrophysiology with Neuropixels probes”, Curr. Opin. Neurobiol, vol. 50, pp. 92–100, 2018.
    1. Maharbiz MM et al., “Reliable next-generation cortical interfaces for chronic brain—machine interfaces and neuroscience”, Proc. IEEE, vol. 105, no. 1, pp. 73–82, 2017.
    1. Obaid A et al., “Massively parallel microwire arrays integrated with CMOS chips for neural recording”, bioRxiv, p. 573295, 2019.
    1. Musk E and Neuralink, “An integrated brain-machine interface platform with thousands of channels”, J. Med. Internet Res, vol. 21, no. 10, p. e16194, 2019.
    1. Neely RM et al., “Recent advances in neural dust: towards a neural interface platform.”, Curr. Opin. Neurobiol, vol. 50, pp. 64–71, 2018.
    1. Laiwalla F et al., “A distributed wireless network of implantable sub-mm cortical microstimulators for brain-computer interfaces”, in, 2019 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., IEEE, 6876–6879.
    1. Fernandez-Leon JA et al., “A wireless transmission neural interface system for unconstrained non-human primates” ,J. Neural Eng, vol. 12, no. 5, p. 056005,2015.
    1. Chestek CA et al., “HermesC: Low-power wireless neural recording system for freely moving primates”, IEEE Trans. Neural Syst. Rehabil. Eng, vol. 17, pp. 330–338, 2009.
    1. Schwarz DA et al., “Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys”, Nat. Methods, vol. 11, no. 6, pp. 670–6, 2014.
    1. Gao H et al., “HermesE: A 96-channel full data rate direct neural interface in 0.13 μm CMOS”, IEEE J. Solid-State Circuits, vol. 47, no. 4, pp. 1043–1055, 2012.
    1. Rajangam S et al., “Wireless cortical brain-machine interface for whole-body navigation in primates”, Sci. Rep, vol. 6, p. 22170, 2016.
    1. Foster JD et al., “Combining wireless neural recording and video capture for the analysis of natural gait”, in, 2011 5th Int. IEEE/EMBS Conf. Neural Eng., IEEE, 613–616.
    1. Foster JD et al., “A freely-moving monkey treadmill model”, J. Neural Eng, vol. 11, no. 4, p. 046020, 2014.
    1. Kennedy PR and Bakay RA, “Restoration of neural output from a paralyzed patient by a direct brain connection”, Neuroreport, vol. 9, no. 8, pp. 1707–1711, 1998.
    1. Wang W et al., “An electrocorticographic brain interface in an individual with tetraplegia”,PLoS One, vol. 8, no. 2, p. e55344, 2013.
    1. Silversmith DB et al., “Plug-and-play control of a brain–computer interface through neural map stabilization”, Nat. Biotechnol, pp. 1–10, 2020.
    1. Degenhart AD et al., “Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity”, Nat. Biomed. Eng, pp. 1–14, 2020.
    1. Oxley TJ et al., “Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity”, Nat. Biotechnol, vol. 34, no. 3, pp. 320–327, 2016.
    1. Oxley TJ et al., “Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: First in-human experience”, J Neurointerv. Surg, vol. 13, no. 2, pp. 102–108, 2021.
    1. Stanslaski S et al., “A chronically implantable neural coprocessor for investigating the treatment of neurological disorders”, IEEE Trans. Biomed Circuits Syst, vol. 12, no. 6, pp. 1230–1245, 2018.
    1. Vansteensel MJ et al., “Fully implanted brain–computer interface in a locked-in patient with ALS”, N. Engl. J. Med, vol. 375, no. 21, pp. 2060–2066, 2016.
    1. Swann NC et al., “Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson’s disease”, J Neurosurg, vol. 128, no. 2, pp. 605–616, 2018.
    1. Matsushita K et al., “A fully implantable wireless ECoG 128-channel recording device for human brain—machine interfaces: W-HERBS”, Front. Neurosci, vol. 12, p. 511, 2018.
    1. Wang PT et al., “A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface”, J. Neural Eng, vol. 16, no. 6, p. 066043, 2019.
    1. Benabid AL et al., “An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration”, Lancet Neurol, vol. 18,no. 12, pp. 1112–1122, 2019.
    1. Masse NY et al., “Non-causal spike filtering improves decoding of movement intention for intracortical BCIs.”, J. Neurosci. Methods, vol. 236, pp. 58–67, 2014.
    1. Heelan C et al., “A mobile embedded platform for high performance neural signal computation and communication”, in, IEEE Biomed. Circuits Syst. Conf. (BioCAS). Progr. 1316., Atlanta, GA, USA.
    1. Flesher SN et al., “Intracortical microstimulation of human somatosensory cortex”, Sci. Transl. Med, vol. 8, no. 361, pp. 361ra141–361ra141, 2016.
    1. Pais-Vieira M et al., “A closed loop brain-machine interface for epilepsy control using dorsal column electrical stimulation”, Sci. Rep, vol. 6, no. 1, p. 32814, 2016.
    1. Park YS et al., “Early detection of human epileptic seizures based on intracortical microelectrode array signals”, IEEE Trans. Biomed Eng, pp. 1–1, 2019.
    1. Greenwald E et al., “Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations”, Med. Biol. Eng. Comput, vol. 54, no. 1, pp. 1–17, 2016.

Source: PubMed

赞助者和合作者

医疗条件

药物干预

3
订阅