Implantable brain machine interfaces: first-in-human studies, technology challenges and trends

Rapeaux, Adrien; Constandinou, Timothy G.

doi:10.1016/j.copbio.2021.10.001

Cited by 77 publications

(56 citation statements)

References 64 publications

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“…With advances being made in size and biocompatibility for neurotechnology, the future for clinical grade optrode technology appears bright. 18 Trial sponsors spend considerable time considering the aims and objectives for a trial as well as the procedures and investigations necessary to meet them. But alongside trial protocols, long-term plans also need to be drawn up.…”

Section: Introductionmentioning

confidence: 99%

Post-Trial Considerations for an Early Phase Optogenetic Trial in the Human Brain

White¹,

Whittaker²

2022

OAJCT

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For many clinical trials, the issue of post-trial access to research treatments is straightforward. Sponsors offer a range of follow-on studies, compassionate use programs or expanded access programs to allow participants to continue accessing beneficial experimental treatments. But there are times when this is not always the case and participants are required to stop beneficial treatments and return to standard care. Guidance states that post-trial access should be made available for "those participants who still need an intervention identified as beneficial". This broad statement has allowed sponsors to make their own interpretation of when an intervention is still "needed" and when it is "beneficial". As a result, there have been a number of situations where participants of clinical trials have been left afterwards with feelings of abandonment. Participants involved in studies with long-term, invasive treatments can be seen as being particularly vulnerable. Optogenetic technology has the potential to offer hope to people with neurological conditions, especially people who may not respond to current approved treatments. Optogenetics typically involves two components: a gene therapy medicinal product (GTMP) that induces long-term expression of light-reactive proteins within cells, and an active implantable device to stimulate the light-sensitised cells. Neither works without the other, hence for long-term patient benefit, both must remain active and may therefore require maintenance or replacement. With the potential life-long consequences of both components and the difficulty of accessing the brain, there is a need to reconsider post-trial guidelines and whether they are suitable to support early phase optogenetic trial participants. This paper considers the ethical and regulatory requirements in place for post-trial access and care in relation to optogenetic treatments of neurological conditions. We propose that a new perspective with wider responsibilities for sponsors is required when it comes to these types of novel therapies.

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Section: Introductionmentioning

confidence: 99%

Post-Trial Considerations for an Early Phase Optogenetic Trial in the Human Brain

White¹,

Whittaker²

2022

OAJCT

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“…They can, for example, be used to treat paraplegia, quadriplegia, movement disorders, Locked-in syndrome and more [1]. Intracortical BMIs (iBMIs) are the most invasive form of BMI, where electrodes are placed into brain tissue [2]. They also provide the highest resolution of BMI data, capable of measuring the firing rates of individual neurons in the electrodes’ vicinity.…”

Section: Introductionmentioning

confidence: 99%

“…They also enable chronic powering without having to replace the implant, as battery life is limited. However, due to heating constraints in cortical tissue, power is strictly limited in Wireless Intracortical BMIs (WI-BMIs) to an approximately 1 C temperature increase or 1.6 mW/g of specific absorption rate (SAR) in tissue [2–4]. In the context of heating due to absorption of radio frequencies (RF), the IEEE standard C95.1-2019 gives limits for SAR heating depending on RF frequency [5].…”

Section: Introductionmentioning

confidence: 99%

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Hardware-Efficient Compression of Neural Multi-Unit Activity

Savolainen

Zhang

Feng

et al. 2022

Preprint

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Objective: Recent advances in intracortical brain machine interfaces (iBMIs) have demonstrated the feasibility of using our thoughts; by sensing and decoding neural activity, for communication and cursor control tasks. It is essential that any invasive device is completely wireless so as to remove percutaneous connections and the associated infection risks. However, wireless communication consumes significant power and there are strict heating limits in cortical tissue. Most iBMIs use Multi Unit Activity (MUA) processing, however the required bandwidth can be excessive for large channel counts in mm or sub-mm scale implants. As such, some form of data compression for MUA iBMIs is desirable. Approach: We used a Machine Learning approach to select static Huffman encoders that worked together, and investigated a broad range of resulting compression systems. They were implemented in reconfigurable hardware and their power consumption, resource utilization and compression performance measured. Main Results: Our design results identified a specific system that provided top performance. We tested it on data from 3 datasets, and found that, with less than 1% behavioural decoding performance reduction from peak, the communication bandwidth was reduced from 1 kb/s/channel to approximately 27 bits/s/channel, using only a Look-Up Table and a 50 ms temporal resolution for threshold crossings. Relative to raw broadband data, this is a compression ratio of 1700-15,000 × and is over an order of magnitude higher than has achieved before. Assuming 20 nJ per communicated bit, the total compression and communication power was between 1.37 and 1.52 μW/channel, occupying 246 logic cells and 4 kbit RAM supporting up to 512-channels. Significance: We show that MUA data can be significantly compressed in a hardware efficient manner, `out of the box' with no calibration necessary. This can significantly reduce on-implant power consumption and enable much larger channel counts in WI-BMIs. All results, code and hardware designs have been made publicly available.

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“…With the recent trend of designing wireless distributed BMIs with increased channel counts [4], the growing amount of data requires unfeasible bandwidth and wireless transmission power for the implants. Real-time on-implant feature extraction and compression are therefore essential to distill the informative features, reduce bandwidth and consequently reduce the transmitting power.…”

Section: Introductionmentioning

confidence: 99%

Hardware evaluation of spike detection algorithms towards wireless brain machine interfaces

Oprea

Zhang

Constandinou

2022

Preprint

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The current trend for implantable Brain Machine Interfaces (BMIs) is to increase the channel count, towards next generation devices that improve on information transfer rate. This however increases the raw data bandwidth for wired or wireless systems that ultimately impacts the power budget (and thermal dissipation). On-implant feature extraction and/or compression are therefore becoming essential to reduce the data rate, however the processing power is of concern. One common feature extraction technique for intracortical BMIs is spike detection. In this work, we have empirically compared the performance, resource utilization, and power consumption of three hardware efficient spike emphasizers, Non-linear Energy Operator (NEO), Amplitude Slope Operator (ASO) and Energy of Derivative (ED), and two common statistical thresholding mechanisms (using mean or median). We also propose a novel median approximation to address the issue of the median operator not being hardware-efficient to implement. These have all been implemented and evaluated on reconfigurable hardware (FPGA) to estimate their hardware efficiency in an ultimate ASIC design. Our results suggest that ED with average thresholding provides the most hardware efficient (low power/resource) choice, while using median has the advantage of improved detection accuracy and higher robustness on threshold multiplier settings. This work is significant because it is the first to implement and compare the hardware and algorithm trade-offs that have to be made before translating the algorithms into hardware instances to design wireless implantable BMIs.

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Implantable brain machine interfaces: first-in-human studies, technology challenges and trends

Cited by 77 publications

References 64 publications

Post-Trial Considerations for an Early Phase Optogenetic Trial in the Human Brain

Post-Trial Considerations for an Early Phase Optogenetic Trial in the Human Brain

Hardware-Efficient Compression of Neural Multi-Unit Activity

Hardware evaluation of spike detection algorithms towards wireless brain machine interfaces

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