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

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 12; pp. 17534 - 17548
• Main Authors: Wang, Hanwen, Qi, Yu, Yao, Lin, Wang, Yueming, Farina, Dario, Pan, Gang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2024
• Subjects: Algorithms; Brain - physiology; Brain modeling; Brain-Computer Interfaces; Brain–computer interface (BCI); Decoding; electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Feature extraction; Humans; Imagination - physiology; Machine Learning; motor imagery (MI); neural decoding; Neural Networks, Computer; Process control; Training
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2023.3305621

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 toward an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we first 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 and 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 coadaptive approaches in both learning efficiency and effectiveness.