A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training

• URL: http://arxiv.org/abs/2309.03209v1
• Date: Fri, 25 Aug 2023 01:24:18 GMT
• Title: A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training
• Authors: Hanwen Wang, Yu Qi, Lin Yao, Yueming Wang, Dario Farina, Gang Pan
• Abstract summary: Endogenous brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices. mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery. Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs.
• Score: 20.2015819836196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals towards an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we firstly model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel ``copy/new'' feedback paradigm to help shape the signal generation of the subject toward the optimal distribution; 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on ``good'' samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over co-adaptive approaches in both learning efficiency and effectiveness.

Related papers

• Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution [30.186917337606477]
  We introduce a sparse multitask learning framework for motor imagery (MI) and motor execution (ME) tasks. Given a dual-task CNN model for MI-ME classification, we apply a saliency-based sparsification approach to prune superfluous connections. Our results indicate that this tailored sparsity can mitigate the overfitting problem and improve the test performance with small amount of data.
  arXiv  Detail & Related papers  (2023-12-10T09:06:16Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals [7.682832730967219]
  We study the self-supervised learning framework for brain signals that can be applied to pre-train either SEEG or EEG data. Inspired by this, we propose MBrain to learn implicit spatial and temporal correlations between different channels. Our model outperforms several state-of-the-art time series SSL and unsupervised models, and has the ability to be deployed to clinical practice.
  arXiv  Detail & Related papers  (2023-06-15T09:14:26Z)
• Sequential Best-Arm Identification with Application to Brain-Computer Interface [34.87975833920409]
  A brain-computer interface (BCI) is a technology that enables direct communication between the brain and an external device or computer system. An electroencephalogram (EEG) and event-related potential (ERP)-based speller system is a type of BCI that allows users to spell words without using a physical keyboard. We propose a sequential top-two Thompson sampling (STTS) algorithm under the fixed-confidence setting and the fixed-budget setting.
  arXiv  Detail & Related papers  (2023-05-17T18:49:44Z)
• An embedding for EEG signals learned using a triplet loss [0.0]
  In a brain-computer interface (BCI), decoded brain state information can be used with minimal time delay. A challenge in such decoding tasks is posed by the small dataset sizes. We propose novel domain-specific embeddings for neurophysiological data.
  arXiv  Detail & Related papers  (2023-03-23T09:05:20Z)
• CoopInit: Initializing Generative Adversarial Networks via Cooperative Learning [50.90384817689249]
  CoopInit is a cooperative learning-based strategy that can quickly learn a good starting point for GANs. We demonstrate the effectiveness of the proposed approach on image generation and one-sided unpaired image-to-image translation tasks.
  arXiv  Detail & Related papers  (2023-03-21T07:49:32Z)
• The Predictive Forward-Forward Algorithm [79.07468367923619]
  We propose the predictive forward-forward (PFF) algorithm for conducting credit assignment in neural systems. We design a novel, dynamic recurrent neural system that learns a directed generative circuit jointly and simultaneously with a representation circuit. PFF efficiently learns to propagate learning signals and updates synapses with forward passes only.
  arXiv  Detail & Related papers  (2023-01-04T05:34:48Z)
• 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)
• 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets [89.84774119537087]
  We design two transfer learning challenges around diagnostics and Brain-Computer-Interfacing (BCI) Task 1 is centred on medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
  arXiv  Detail & Related papers  (2022-02-14T12:12:20Z)
• Credit Assignment in Neural Networks through Deep Feedback Control [59.14935871979047]
  Deep Feedback Control (DFC) is a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss-Newton optimization for a wide range of connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing.
  arXiv  Detail & Related papers  (2021-06-15T05:30:17Z)

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.