Real-Time Decision Fusion for Multimodal Neural Prosthetic Devices

The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device., i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions.Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future

A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces

Background: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders' performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study. Methods: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm. Results: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity. Conclusions: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions

Toward optimal target placement for neural prosthetic devices

Neural prosthetic systems have been designed to estimate continuous reach trajectories ( motor prostheses) and to predict discrete reach targets ( communication prostheses). In the latter case, reach targets are typically decoded from neural spiking activity during an instructed delay period before the reach begins. Such systems use targets placed in radially symmetric geometries independent of the tuning properties of the neurons available. Here we seek to automate the target placement process and increase decode accuracy in communication prostheses by selecting target locations based on the neural population at hand. Motor prostheses that incorporate intended target information could also benefit from this consideration. We present an optimal target placement algorithm that approximately maximizes decode accuracy with respect to target locations. In simulated neural spiking data fit from two monkeys, the optimal target placement algorithm yielded statistically significant improvements up to 8 and 9% for two and sixteen targets, respectively. For four and eight targets, gains were more modest, as the target layouts found by the algorithm closely resembled the canonical layouts. We trained a monkey in this paradigm and tested the algorithm with experimental neural data to confirm some of the results found in simulation. In all, the algorithm can serve not only to create new target layouts that outperform canonical layouts, but it can also confirm or help select among multiple canonical layouts. The optimal target placement algorithm developed here is the first algorithm of its kind, and it should both improve decode accuracy and help automate target placement for neural prostheses

Encoding of Motor Behaviors by Cortical Neuronal Networks

Performance of motor behavior requires complex coordination of neural activity across diverse regions of cortex at multiples scales. At the level of coordination across large areas of cortex, this activity is thought to be related to similarly broad concepts of movement from goal identification to motor planning to generation of motor commands. At smaller scales on the level of local populations of individual neurons in motor and premotor cortex, we observe complex non-stationary firing patterns that appear to be related to the movement itself. Our understanding of the details of this relationship are incomplete, however. Earlier work by the community largely focused on the analysis of individual units in isolation. Technological advances and changes in experimental paradigms have led to the simultaneous recording of hundreds of neurons simultaneously. From an analysis standpoint, we are observing a similar shift in focus from the individual neuron to the population as a whole. This dissertation investigates encoding and decoding techniques that handle time-varying neuronal activity from within the context of a reach-to-grasp task. The first part of this work investigates the dynamics of neural coding of reach and grasp through a series of temporally localized classifiers. The second part of this thesis proposes a semi-supervised learning approach to identifying task relevant neurons for classification purposes and for identifying communities of neurons that co-modulate their activity in correlation to a common external variable. The third part of this work proposes and demonstrates an approach to modeling the firing of individual neurons as a weighted combination of other neurons with weighting dependent on the task being performed