Abstract
BackgroundThe 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.Methodology/Principal FindingsHere we propose a framework based on decision fusion, 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.ConclusionsOur 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.
Highlights
Each year,150,000 people in the United States undergo an arm or leg amputation [1]
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
We examine two algorithms for decision fusion of continuous variables: the Kalman filter and artificial neural networks (ANNs)
Summary
Each year ,150,000 people in the United States undergo an arm or leg amputation [1]. An estimated 1.7 million amputees live in the United States [2] and millions more throughout the world. Prosthetic limbs have been developed to incorporate electrical signals from indirect muscles for user control – this is known as conventional prosthetic control. The emerging field of neural prosthetics goes further, interpreting the neural activity of the user for more intuitive control of 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
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