Generative motor imagery dynamic networks: EEG-controlled grasping via individualized model training

Improving the accuracy of non-invasive brain-computer interface (BCI) and promoting their daily use can be achieved by developing an individualized model training framework, where individual training means that the model is based on small-sample learning from individual data. In the process of data augmentation through synthetic data, the criteria for data generation needs to be further specified according to the requirements. Therefore, in this study, the proposed BCI model utilizes dynamic networks to describe electroencephalogram (EEG) activity during the motor imagery (MI) task, innovatively generates individualized dynamic networks from individual data, and ultimately achieves EEG-controlled grasping through model training. Specifically, this study involves the EEG signals of the right-hand grasping movements of eight subjects and proposes using morphological pattern spectrum (MPS) to encode EEG potentials during MI processes. The MI condition representation was achieved by combining the dynamic networks with MPS encoding, and more dynamic network EEG encoding samples were synthesized through generative adversarial network (GAN) or variational autoencoder (VAE). The AUCs based on the long short-term memory (LSTM) architecture for generating and classifying can be improved by 0.003-0.07. The optimal BCI model based on the Wasserstein GAN and Granger causality (GC) dynamic network encoded by MPS achieved a mean true/false positive rate (TPR/FPR) of 90.0%/0.0%, far better than the 52.9%/4.4% achieved without individualized modeling. Moreover, the BCI establishment of handling multi-task and complex command outputs further demonstrates the reliability of MPS encoding of the GC dynamic network in BCI modeling. The advantage of this "generative-individual" approach is that it not only reduces the sample size requirement while ensuring accuracy but also avoids building models that are applicable to all individuals, which leads to difficult convergence.