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

TL;DR

This paper introduces a joint human-machine learning framework that accelerates and improves the training process of endogenous EEG-based BCIs by guiding users to generate more optimal brain signals through adaptive feedback and decoding.

Contribution

It proposes a novel unified formulation and a joint learning framework with adaptive algorithms for both humans and machines to enhance BCI training efficiency.

Findings

Joint learning outperforms co-adaptive approaches in efficiency.

The framework reduces training time for EEG-based BCIs.

Experimental results show improved signal quality and control accuracy.

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.