Unsupervised Few-Shot Adaptive Re-Learning for EEG-Based Motor Imagery Classification

To address the effect of both intra- and inter-subject variability in the EEG-based motor-imagery classification, adaptive schemes have been proposed whereby the pre-trained model. However, collection and labelling of additional target data is resource intensive. Furthermore, there may exist significant signal variations across time especially across multiple recording sessions which can result in model performance deterioration following adaptation. This introduces another challenging problem to training classifiers as they are typically unable to automatically determine which data is suitable for fine-tuning purposes, leading to some subjects facing performance drops even after adaptation. To address the data scarcity and variability in adaptive performance, we propose a novel machine-relearning Siamese architecture which utilizes few samples of unlabeled evaluation data to perform data efficient model re-learning. Machine re-learning optimally selects a sub-section of previously known data to update the model parameters. This is implemented via the use of comparative contrastive loss between the Gaussian distributions of target and known data to perform data selection. The highest subject-independent performance achieved an average (N=54) accuracy of 86.63% (±11.79%) without additional data and 88.23% (±10.36%) when utilizing a single unlabeled supplementary data for two-class motor imagery. The previous best accuracy on this dataset is 85.90% (±11.20%) using the best-known method in the literature. Therefore, significantly superior adaptation performance can be achieved while utilizing lesser amount of information from the target subject through reducing EEG feature variability in the training and fine-tuning sets. Codes may be found at: https://github.com/NgHanWei/EEG_Relearning