Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interface

• URL: http://arxiv.org/abs/2204.11840v1
• Date: Fri, 22 Apr 2022 04:30:24 GMT
• Title: Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interface
• Authors: Yu Qi, Xinyun Zhu, Kedi Xu, Feixiao Ren, Hongjie Jiang, Junming Zhu, Jianmin Zhang, Gang Pan, Yueming Wang
• Abstract summary: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors. One major limitation of current BMIs is the unstable performance in online control due to the variability of neural signals. We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control.
• Score: 9.127608168119975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for mobility restoration. One major limitation of current BMIs lies in the unstable performance in online control due to the variability of neural signals, which seriously hinders the clinical availability of BMIs. Method: To deal with the neural variability in online BMI control, we propose a dynamic ensemble Bayesian filter (DyEnsemble). DyEnsemble extends Bayesian filters with a dynamic measurement model, which adjusts its parameters in time adaptively with neural changes. This is achieved by learning a pool of candidate functions and dynamically weighting and assembling them according to neural signals. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Results: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% and reduces the reach time by 13.5% in the random target pursuit task) and robustness (performs more stably over different experiment days). Conclusion: Our results demonstrate the superiority of DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and flexible framework for robust neural decoding, which is beneficial to different neural decoding applications.

Related papers

• Hybrid Spiking Neural Networks for Low-Power Intra-Cortical Brain-Machine Interfaces [42.72938925647165]
  Intra-cortical brain-machine interfaces (iBMIs) have the potential to dramatically improve the lives of people with paraplegia. Current iBMIs suffer from scalability and mobility limitations due to bulky hardware and wiring. We are investigating hybrid spiking neural networks for embedded neural decoding in wireless iBMIs.
  arXiv  Detail & Related papers  (2024-09-06T17:48:44Z)
• Topology Optimization of Random Memristors for Input-Aware Dynamic SNN [44.38472635536787]
  We introduce pruning optimization for input-aware dynamic memristive spiking neural network (PRIME) Signal representation-wise, PRIME employs leaky integrate-and-fire neurons to emulate the brain's inherent spiking mechanism. For reconfigurability, inspired by the brain's dynamic adjustment of computational depth, PRIME employs an input-aware dynamic early stop policy.
  arXiv  Detail & Related papers  (2024-07-26T09:35:02Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• Adaptation through prediction: multisensory active inference torque control [0.0]
  We present a novel multisensory active inference torque controller for industrial arms. Our controller, inspired by the predictive brain hypothesis, improves the capabilities of current active inference approaches.
  arXiv  Detail & Related papers  (2021-12-13T16:03:18Z)
• Neural optimal feedback control with local learning rules [67.5926699124528]
  A major problem in motor control is understanding how the brain plans and executes proper movements in the face of delayed and noisy stimuli. We introduce a novel online algorithm which combines adaptive Kalman filtering with a model free control approach.
  arXiv  Detail & Related papers  (2021-11-12T20:02:00Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks [20.598972933619198]
  Brain-machine interface (BMI) would be a desirable technology to collaborate with robots. EEG has low accuracy and a limited degree of freedom due to a low signal-to-noise ratio. In this study, we investigated the possibility for inter-paradigm classification of multiple endogenous BMI paradigms.
  arXiv  Detail & Related papers  (2021-06-25T05:34:15Z)
• Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks [0.0]
  We show how cellular dynamics help neurons to filter out the intense signals to help neurons keep a stable firing rate. Such an approach might be used in the machine learning domain to improve the robustness of AI systems.
  arXiv  Detail & Related papers  (2021-03-15T05:22:14Z)
• SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments [14.727296040550392]
  Spiking Neural Networks (SNNs) bear the potential of efficient unsupervised and continual learning capabilities because of their biological plausibility. We propose SpikeDyn, a framework for energy-efficient SNNs with continual and unsupervised learning capabilities in dynamic environments.
  arXiv  Detail & Related papers  (2021-02-28T08:26:23Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)

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.