HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks

• URL: http://arxiv.org/abs/2103.05338v1
• Date: Tue, 9 Mar 2021 10:32:53 GMT
• Title: HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks
• Authors: Pablo Ortega and Aldo Faisal
• Abstract summary: We use a convolutional neural network architecture, the HemCNN, to solve the fNIRS left/right hand force decoding problem. HemCNN learned to detect which hand executed a grasp at a naturalistic hand action speed, outperforming standard methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We solve the fNIRS left/right hand force decoding problem using a data-driven approach by using a convolutional neural network architecture, the HemCNN. We test HemCNN's decoding capabilities to decode in a streaming way the hand, left or right, from fNIRS data. HemCNN learned to detect which hand executed a grasp at a naturalistic hand action speed of $~1\,$Hz, outperforming standard methods. Since HemCNN does not require baseline correction and the convolution operation is invariant to time translations, our method can help to unlock fNIRS for a variety of real-time tasks. Mobile brain imaging and mobile brain machine interfacing can benefit from this to develop real-world neuroscience and practical human neural interfacing based on BOLD-like signals for the evaluation, assistance and rehabilitation of force generation, such as fusion of fNIRS with EEG signals.

Related papers

• Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding [13.108613110379961]
  This paper presents a novel approach utilizing a Multiscale Fusion Fusion Spiking Neural Network (MFSNN) MFSNN emulates the parallel processing and multiscale feature fusion seen in human visual perception to enable real-time, efficient, and energy-conserving neural signal decoding. MFSNN surpasses traditional artificial neural network methods, such as enhanced GRU, in both accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-09-14T09:53:30Z)
• Decoding finger velocity from cortical spike trains with recurrent spiking neural networks [6.404492073110551]
  Invasive brain-machine interfaces (BMIs) can significantly improve the life quality of motor-impaired patients. BMIs must meet strict latency and energy constraints while providing reliable decoding performance. We trained RSNNs to decode finger velocity from cortical spike trains of two macaque monkeys.
  arXiv  Detail & Related papers  (2024-09-03T10:15:33Z)
• Accurate Mapping of RNNs on Neuromorphic Hardware with Adaptive Spiking Neurons [2.9410174624086025]
  We present a $SigmaDelta$-low-pass RNN (lpRNN) for mapping rate-based RNNs to spiking neural networks (SNNs) An adaptive spiking neuron model encodes signals using $SigmaDelta$-modulation and enables precise mapping. We demonstrate the implementation of the lpRNN on Intel's neuromorphic research chip Loihi.
  arXiv  Detail & Related papers  (2024-07-18T14:06:07Z)
• When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron [7.478056407323783]
  Spiking Neural Networks (SNNs) are capable of encoding and processing temporal information in a biologically plausible way. We propose a novel method for image decoding based on temporal attention (TAID) and an adaptive Leaky-Integrate-and-Fire neuron model.
  arXiv  Detail & Related papers  (2024-06-05T08:21:55Z)
• NeuralFastLAS: Fast Logic-Based Learning from Raw Data [54.938128496934695]
  Symbolic rule learners generate interpretable solutions, however they require the input to be encoded symbolically. Neuro-symbolic approaches overcome this issue by mapping raw data to latent symbolic concepts using a neural network. We introduce NeuralFastLAS, a scalable and fast end-to-end approach that trains a neural network jointly with a symbolic learner.
  arXiv  Detail & Related papers  (2023-10-08T12:33:42Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Spiking Neural Network Decision Feedback Equalization [70.3497683558609]
  We propose an SNN-based equalizer with a feedback structure akin to the decision feedback equalizer (DFE) We show that our approach clearly outperforms conventional linear equalizers for three different exemplary channels. The proposed SNN with a decision feedback structure enables the path to competitive energy-efficient transceivers.
  arXiv  Detail & Related papers  (2022-11-09T09:19:15Z)
• Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation [70.75043144299168]
  Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. It is a challenge to efficiently train SNNs due to their non-differentiability. We propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance.
  arXiv  Detail & Related papers  (2022-05-01T12:44:49Z)
• Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [66.2457134675891]
  Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. Most existing methods imitate the backpropagation framework and feedforward architectures for artificial neural networks. We propose a novel training method that does not rely on the exact reverse of the forward computation.
  arXiv  Detail & Related papers  (2021-09-29T07:46:54Z)
• Deep Real-Time Decoding of bimanual grip force from EEG & fNIRS [3.0176686218359694]
  We show a way to achieve continuous hand force decoding using cortical signals obtained with non-invasive mobile brain imaging. Our results show a way to achieve continuous hand force decoding using cortical signals obtained with non-invasive mobile brain imaging has immediate impact for rehabilitation, restoration and consumer applications.
  arXiv  Detail & Related papers  (2021-03-09T10:28:05Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)

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.