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

arXiv: https://arxiv.org/abs/2309.03209 · HTML: https://mlsanity.com/paper/2309.03209/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 81 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications](https://mlsanity.com/paper/2001.11337/) (2020)
- [Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification](https://mlsanity.com/paper/2202.02472/) (2022)
- [GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals](https://mlsanity.com/paper/2006.08924/) (2020)
- [Learning Using Privileged Information: SVM+ and Weighted SVM](https://mlsanity.com/paper/1306.3161/) (2013)
- [Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces](https://mlsanity.com/paper/1911.00714/) (2019)
- [Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal](https://mlsanity.com/paper/2103.13862/) (2021)

## Abstract (arXiv)

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.