Local field potential (LFP) has gained increasing interest as an alternative input signal for brain-machine interfaces (BMIs) due to its informative features, long-term stability, and low frequency content. However, despite these interesting properties, LFP-based BMIs have been reported to yield low decoding performances compared to spike-based BMIs. In this paper, we propose a new decoder based on long short-term memory (LSTM) network which aims to improve the decoding performance of LFP-based BMIs. We compare offline decoding performance of the proposed LSTM decoder to a commonly used Kalman filter (KF) decoder on hand kinematics prediction tasks from multichannel LFPs. We also benchmark the performance of LFP-driven LSTM decoder against KF decoder driven by two types of spike signals: singleunit activity (SUA) and multi-unit activity (MUA). Our results show that LFP-driven LSTM decoder achieves significantly better decoding performance than LFP-, SUA-, and MUAdriven KF decoders. This suggests that LFPs coupled with LSTM decoder could provide high decoding performance, robust, and low power BMIs.

Robustness and decoding accuracy remain major challenges in the clinical translation of intracortical brain-machine interface (BMI) systems. In this work, we show that a signal/decoder co-design methodology (exploiting the synergism between the input signal and decoding algorithm within the design development process) can be used to yield robust and accurate BMI decoding performance. Specifically, through applying this process, we propose the combination of using entire spiking activity (ESA) as the input signal and quasi-recurrent neural network (QRNN) based deep learning as the decoding algorithm. We evaluated the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of a non-human primate. Our proposed method yielded consistently higher decoding performance than any other methods previously reported across long-term recording sessions. Its high decoding performance could sustain, even when spikes were removed from the raw signals.Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs. this issue is by utilising a different type of input signals, namely multiunit activity (MUA). MUA, defined as all spikes detected through threshold crossing without spike sorting, offers simpler processing while providing better signal stability over time than SUA. 25,29,30 Another alternative input signal is local field potential (LFP), which is thought to mainly reflect summed synaptic activity from a local neuronal population around the recording electrodes. It is believed and has been demonstrated by experimental studies that LFPs exhibit better long-term signal stability than their spike counterparts. [31][32][33][34] Moreover, LFPs can be obtained by simpler processing and lower sampling rate that can reduce the power consumption of BMIs. Despite the appealing advantages of MUA and LFP, a considerable number of published studies have reported that the decoding accuracy of MUA 24,29,30,35, 36 and LFP 33,[36][37][38][39] are lower than that of SUA. Therefore, it is highly desirable to have an input signal that is not only stable but also yields high decoding accuracy.The decoding accuracy is also affected by the decoder, that is, an algorithm used to convert the input signal from the brain into the behavioural parameter of interest (e.g. hand kinematics). Many BMI studies employ linear decoders, e.g. Wiener filter (WF) 5,40,41 and Kalman filter (KF), 6,9,21,32,33,42 which could yield suboptimal decoding accuracy as neural signals are known to exhibit nonlinear and nonstationary properties. 43 Although there exists a nonlinear extension of WF and KF, called Wiener cascade filter (WCF) 19,39,44 and unscented Kalman filter (UKF), 45,46 WCF and UKF assume that the noise is (additive) stationary and Gaussian, respectively. If these a priori assumptions are violated, the decoding performance of both decoders will not be optimal.The rise of ...

Objective. Brain-machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs. Approach. We propose entire spiking activity (ESA)-an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique-as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks. Main results. Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long-term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data. Significance. Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.

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).

No abstract