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.

This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e., 0.5-29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data including fully simulated, semi-simulated, and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for the detection of seizures from nonseizure epochs have been found to be easily distinguishable after artifacts are removed, and consequently, the false alarms in seizure detection are reduced. Results from an extensive experiment with these datasets prove the efficacy of the proposed algorithm, which makes it possible to use it for artifact removal in epilepsy diagnosis as well as other applications regarding neuroscience studies.

In this study, the effects of noisy links are investigated on the steady‐state performance of mobile adaptive networks with diffusion least mean‐squares strategies. The authors derive theoretical relations which explain how the steady‐state performance metrics, including the steady‐state network mean‐square deviation and steady‐state velocity mean‐square‐error is affected by noisy links. The provided analysis relies on the spatial–temporal energy conservation argument. The proposed simulation results reveal that although the noisy links degrade the performance of mobile adaptive networks; however, for suitably chosen combination coefficients the mobile adaptive network with noisy links provides a bounded estimation error. Finally, the proposed simulations verify that the derived theoretical analysis closely matches the actual steady‐state performance observed in a network.

In this paper the authors propose an adaptive estimation algorithm for in‐network processing of complex signals over distributed networks. In the proposed algorithm, as the incremental augmented complex least mean square (IAC‐LMS) algorithm, nodes of the network are allowed to collaborate via incremental cooperation mode to exploit the spatial dimension; while at the same time are equipped with LMS learning rules to endow the network with adaptation. The authors have extracted closed‐form expressions that show how IAC‐LMS algorithm performs in the steady‐state. The authors further have derived the required conditions for mean and mean‐square stability of the proposed algorithm. The authors use both synthetic benchmarks and real world non‐circular data to evaluate the performance of the proposed algorithm. Simulation results also reveal that the IAC‐LMS algorithm is able to estimate both second order circular (proper) and non‐circular (improper) signals. Moreover, IAC‐LMS algorithm outperforms the non‐cooperative solution.

This paper presents a power-efficient VLSI implementation of a feature extraction engine for the applications of real-time spike sorting. Traditional method like principal components analysis (PCA) works in a batch mode by diagonalizing the covariance matrix constructed from the whole bunch of input data, which is computationally prohibitive and does not favor real-time processing. The proposed hardware framework does not require large volumes of memories by incrementally adjusting the number of estimated principal components in an automatic fashion. Low-voltage circuit design technique has been introduced to achieve significant power saving. The VLSI implementation of the system has a peak power dissipation of 8.59 μW with a 0.5 V supply voltage, and occupies an area of 0.268 mm2.

Studying signal and noise properties of recorded neural data is critical in developing more efficient algorithms to recover the encoded information. Important issues exist in this research including the variant spectrum spans of neural spikes that make it difficult to choose a global optimal bandpass filter. Also, multiple sources produce aggregated noise that deviates from the conventional white Gaussian noise. In this work, the spectrum variability of spikes is addressed, based on which the concept of adaptive bandpass filter that fits the spectrum of individual spikes is proposed. Multiple noise sources have been studied through analytical models as well as empirical measurements. The dominant noise source is identified as neuron noise followed by interface noise of the electrode. This suggests that major efforts to reduce noise from electronics are not well spent. The measured noise from in vivo experiments shows a family of 1/f^{x} (x=1.5\pm 0.5) spectrum that can be reduced using noise shaping techniques. In summary, the methods of adaptive bandpass filtering and noise shaping together result in several dB signal-to-noise ratio (SNR) enhancement.