Artificial Neural Networks-based Real-time Classification of ENG Signals for Implanted Nerve Interfaces

• URL: http://arxiv.org/abs/2403.20234v2
• Date: Tue, 2 Apr 2024 09:26:43 GMT
• Title: Artificial Neural Networks-based Real-time Classification of ENG Signals for Implanted Nerve Interfaces
• Authors: Antonio Coviello, Francesco Linsalata, Umberto Spagnolini, Maurizio Magarini,
• Abstract summary: We explore four types of artificial neural networks (ANNs) to extract sensory stimuli from the electroneurographic (ENG) signal measured in the sciatic nerve of rats. Different sizes of the data sets are considered to analyze the feasibility of the investigated ANNs for real-time classification. Our results show that some ANNs are more suitable for real-time applications, being capable of achieving accuracies over $90%$ for signal windows of $100$ and $200,$ms with a low enough processing time to be effective for pathology recovery.
• Score: 7.335832236913667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neuropathies are gaining higher relevance in clinical settings, as they risk permanently jeopardizing a person's life. To support the recovery of patients, the use of fully implanted devices is emerging as one of the most promising solutions. However, these devices, even if becoming an integral part of a fully complex neural nanonetwork system, pose numerous challenges. In this article, we address one of them, which consists of the classification of motor/sensory stimuli. The task is performed by exploring four different types of artificial neural networks (ANNs) to extract various sensory stimuli from the electroneurographic (ENG) signal measured in the sciatic nerve of rats. Different sizes of the data sets are considered to analyze the feasibility of the investigated ANNs for real-time classification through a comparison of their performance in terms of accuracy, F1-score, and prediction time. The design of the ANNs takes advantage of the modelling of the ENG signal as a multiple-input multiple-output (MIMO) system to describe the measures taken by state-of-the-art implanted nerve interfaces. These are based on the use of multi-contact cuff electrodes to achieve nanoscale spatial discrimination of the nerve activity. The MIMO ENG signal model is another contribution of this paper. Our results show that some ANNs are more suitable for real-time applications, being capable of achieving accuracies over $90\%$ for signal windows of $100$ and $200\,$ms with a low enough processing time to be effective for pathology recovery.

Related papers

• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  In neuromorphic computing, spiking neural networks (SNNs) perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have demonstrated that embedding a few bits of payload in each spike exchanged between the spiking neurons can further enhance inference accuracy. This paper investigates a wireless neuromorphic split computing architecture employing multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier [0.0]
  RISE-iEEG stands for Robust Inter-Subject Electrode Implantation Variability iEEG. We developed an iEEG decoder model that can be applied across multiple patients' data without requiring the coordinates of electrode for each patient. Our analysis shows that the performance of RISE-iEEG is 10% higher than that of HTNet and EEGNet in terms of F1 score.
  arXiv  Detail & Related papers  (2024-08-12T18:33:19Z)
• A frugal Spiking Neural Network for unsupervised classification of continuous multivariate temporal data [0.0]
  Spiking Neural Networks (SNNs) are neuromorphic and use more biologically plausible neurons with evolving membrane potentials. We introduce here a frugal single-layer SNN designed for fully unsupervised identification and classification of multivariate temporal patterns in continuous data.
  arXiv  Detail & Related papers  (2024-08-08T08:15:51Z)
• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG [0.47248250311484113]
  Deep neural networks (DNN) show significant advantages in computer vision applications. We present here a shallow neural network that uses mainly two convolutional neural network layers, with relatively few parameters and fast to learn spectral-temporal features from EEG. Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classification accuracy of 90.68%.
  arXiv  Detail & Related papers  (2022-09-26T02:15:22Z)
• Low Power Neuromorphic EMG Gesture Classification [3.8761525368152725]
  Spiking Neural Networks (SNNs) are promising for low-power, real-time EMG gesture recognition. We present low-power, high accuracy demonstration of EMG-signal based gesture recognition using neuromorphic Recurrent Spiking Neural Networks (RSNN) Our network achieves state-of-the-art accuracy classification (90%) while using 53% than best reported art on Roshambo EMG dataset.
  arXiv  Detail & Related papers  (2022-06-04T22:09:34Z)
• POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
  We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations. The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
  arXiv  Detail & Related papers  (2022-01-19T09:26:34Z)
• An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG [0.0]
  We present a neuromorphic system that combines a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network architecture on the same die for recording, processing, and detecting High Frequency Oscillations (HFO) We show how the neuromorphic system can reliably detect HFOs: the system predicts postsurgical seizure outcome with state-of-the-art accuracy, specificity and sensitivity (78%, 100%, and 33% respectively) This is the first feasibility study towards identifying relevant features in intracranial human data in real-time, on-chip, using event-based processors and
  arXiv  Detail & Related papers  (2020-09-23T16:40:44Z)
• Non-linear Neurons with Human-like Apical Dendrite Activations [81.18416067005538]
  We show that a standard neuron followed by our novel apical dendrite activation (ADA) can learn the XOR logical function with 100% accuracy. We conduct experiments on six benchmark data sets from computer vision, signal processing and natural language processing.
  arXiv  Detail & Related papers  (2020-02-02T21:09:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.