Objective: Neural prostheses generate an extensive volume of data during neural recording and decoding, presenting challenges in data processing and storage. A strategy to address this issue is compressing the data before transmission. Methods: This paper shows the possibility of efficiently compressing nerve data while achieving improved performance for an upper-limb neural prosthetic device. The new method, named Compression-Based Feature Reduction (CBFR), is divided into two stages: (1) applying a lightweight compression method to the original signal, and (2) reducing the number of features after feature extraction, thereby enhancing the classification accuracy in task-based experiments. Results: In an in vivo experiment involving patients with amputated hands, CBFR improved the classification performance of each finger movement up to 1.6% and 6.9% using Symmlet4 Discrete Wavelet Transform (DWT) and Walsh-Hadamard Transform (WHT), respectively, over the original signal. Both methods achieved up to 3x compression ratio from the original signal. Significane: The proposed data compression technique could help advance the field of neural prosthetics by addressing data transmission challenges and enhancing the performance of upper-limb neural prosthetic devices.

Redundancy is a fundamental characteristic of many biological processes such as those in the genetic, visual, muscular, and nervous systems, yet its driven mechanism has not been fully comprehended. Until recently, the only understanding of redundancy is as a mean to attain fault tolerance, which is reflected in the design of many man-made systems. On the contrary, our previous work on redundant sensing (RS) has demonstrated an example where redundancy can be engineered solely for enhancing accuracy and precision. The design was inspired by the binocular structure of human vision, which we believe may share a similar operation. In this letter, we present a unified theory describing how such utilization of redundancy is feasible through two complementary mechanisms: representational redundancy (RPR) and entangled redundancy (ETR). We also point out two additional examples where our new understanding of redundancy can be applied to justify a system's superior performance. One is the human musculoskeletal system (HMS), a biological instance, and the other is the deep residual neural network (ResNet), an artificial counterpart. We envision that our theory would provide a framework for the future development of bio-inspired redundant artificial systems, as well as assist studies of the fundamental mechanisms governing various biological processes.

Sensing is the process of deriving signals from the environment that allows artificial systems to interact with the physical world. The Shannon theorem specifies the maximum rate at which information can be acquired. However, this upper bound is hard to achieve in many man-made systems. The biological visual systems, on the other hand, have highly efficient signal representation and processing mechanisms that allow precise sensing. In this work, we argue that redundancy is one of the critical characteristics for such superior performance. We show architectural advantages by utilizing redundant sensing, including correction of mismatch error and significant precision enhancement. For a proof-of-concept demonstration, we have designed a heuristic-based analog-to-digital converter - a zero-dimensional quantizer. Through Monte Carlo simulation with the error probabilistic distribution as a priori, the performance approaching the Shannon limit is feasible. In actual measurements without knowing the error distribution, we observe at least 2-bit extra precision. The results may also help explain biological processes including the dominance of binocular vision, the functional roles of the fixational eye movements, and the structural mechanisms allowing hyperacuity.