We present a platform technology encompassing a family of innovations that together aim to tackle key challenges with existing implantable brain machine interfaces. The ENGINI (Empowering Next Generation Implantable Neural Interfaces) platform utilizes a 3-tier network (external processor, cranial transponder, intracortical probes) to inductively couple power to, and communicate data from, a distributed array of freely-floating mm-scale probes. Novel features integrated into each probe include: (1) an array of niobium microwires for observing local field potentials (LFPs) along the cortical column; (2) ultra-low power instrumentation for signal acquisition and data reduction; (3) an autonomous, self-calibrating wireless transceiver for receiving power and transmitting data; and (4) a hermetically-sealed micropackage suitable for chronic use. We are additionally engineering a surgical tool, to facilitate manual and robot-assisted insertion, within a streamlined neurosurgical workflow. Ongoing work is focused on system integration and preclinical testing. I. INTRODUCTION Brain Machine Interfaces (BMIs) have a genuine opportunity to effect a transformative impact on both medical [1], [2] and non-medical [3] applications. More specifically, clinical translation can lead to the restoration of movement and communication in patient populations with tetraplegia, amylotrophic lateral sclerosis, locked-in-syndrome, and speech disturbances. Current translational efforts utilize implantable medical devices (IMDs), e.g. Medtronic PC+S [1], experimental neuroscience tools, e.g. Blackrock Neuroport [2], or engineer new devices leveraged on IMDs [4], [5]. A. Key Challenges The major technical challenges with state-of-the-art BMI technology are chronic reliability (device longevity, recording stability, calibration/training) and scalability (extending number of recording and/or stimulation sites). In tackling these, wireless capability is crucial, but brings on its own set of challenges (wireless transfer efficiency, data throughput).

Objective Various on-workstation neural-spike-based brain machine interface(BMI) systems have reached the point of in-human trials, but on-node and on-implant BMI systems are still under exploration. Such systems are constrained by the area and battery. Researchers should consider the algorithm complexity, available resources, power budgets, CMOS technologies, and the choice of platforms when designing BMI systems. However, the effect of these factors is currently still unclear. Approaches. Here we have proposed a novel real-time 128 channel spike detection algorithm and optimised it on Microcontroller(MCU) and Field Programmable Gate Array(FPGA) platforms towards consuming minimal power and memory/resources. It is presented as a use case to explore the different considerations in system design. Main results. The proposed spike detection algorithm achieved over 97% sensitivity and a smaller than 3% false detection rate. The MCU implementation occupies less than 3KB RAM and consumes 31.5μW/ch. The FPGA platform only occupies 299 logic cells and 3KB RAM for 128 channels and consumes 0.04μW/ch. Significance. On the spike detection algorithm front, we have eliminated the processing bottleneck by reducing the dynamic power consumption to lower than the hardware static power, without sacrificing detection performance. More importantly, we have explored the considerations in algorithm and hardware design with respect to scalability, portability, and costs. These findings can facilitate and guide the future development of real-time on-implant neural signal processing platforms.

This paper investigates the relationship between Multi-Unit Activity (MUA) Binning Period (BP) and Brain-Computer Interface (BCI) decoding performance using Long-Short Term Memory decoders. The motivation is to determine whether lossy compression of MUA via increasing BP has any adverse consequences for BCI Behavioral Decoding Performance (BDP). The Neural data originates from intracortical recordings from Macaque Primary Motor cortex [1]. The BDP is measured by the Pearson correlation r between the observed and predicted velocity of the subject’s X-Y hand coordinates in reaching tasks [1]. The results suggest a statistically significant but slight linear relationship between increasing MUA BP and decreasing BDP. For example, when using a 100 ms moving average window, increasing the BP by 10 ms on average reduces the BDP r by approximately 0.85%. This relationship may be due to the reduced number of training examples, or due to the loss of Behavioral information because of reduced MUA temporal resolution.

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.

This paper investigates to what extent Long Short-Term Memory (LSTM) decoders can use Local Field Potentials (LFPs) to predict Single-Unit Activity (SUA) in Macaque Primary Motor cortex. The motivation is to determine to what degree the LFP signal can be used as a proxy for SUA, for both neuroscience and Brain-Computer Interface (BCI) applications. Firstly, the results suggest that the prediction quality varies significantly by implant location or animal. However, within each implant location / animal, the prediction quality seems to be correlated with the amount of power in certain LFP frequency bands (0-10, 10-20 and 40-50 Hz, standardised LFPs). Secondly, the results suggest that bipolar LFPs are more informative as to SUA than unipolar LFPs. This suggests common mode rejection aids in the elimination of non-local neural information. Thirdly, the best individual bipolar LFPs generally perform better than when using all available unipolar LFPs. This suggests that LFP channel selection may be a simple but effective means of lossy data compression in Wireless Intracortical LFP-based BCIs. Overall, LFPs were moderately predictive of SUA, and improvements can likely be made. I. BACKGROUND Brain-Computer Interfaces (BCI) are devices for connecting nervous systems to electronics. The next generation of intracortical BCIs is expected to be wireless. This is to remove any transcutaneous connection and its associated infection risks. An informative signal with low sampling and communication rate is desirable for Wireless Intracortical BCIs (WI-BCI) to reduce power consumption. This is to limit heat dissipation into the brain [1], and to generally reduce design constraints. Since the advent of intracortical BCIs, four major representations of intracortical signals have gained prominence: Broadband recordings, the Local Field Potential (LFP) signal, Single-Unit Activity (SUA), and Multi-Unit Activity (MUA) [2]. Of these four, two may be of particular interest: LFP and SUA.