Adaptive Bayesian Meta-Learning for EEG Signal Classification.

Accurate classification of electroencephalogram (EEG) signals is crucial for brain activity understanding. However, EEG signals are characterized by data heterogeneity and label scarcity, which present a challenging low-data learning regime when building machine learning models. Existing methods tend to suffer from overfitting problem. To this end, we propose an adaptive Bayesian meta-learning framework for instance-specific learning and inference in EEG classification tasks. Specifically, first, a query set-driven dynamic parameter-based support set selection strategy is designed to adaptively fit the query set when constructing a meta-training task. Second, we employ an amortized variational inference network to generate task-specific adapted parameters given the support set, thereby achieving rapid model adaption for the inference of the data in the query set. Especially, a time- and frequency-aware representation learning encoder is leveraged to extract more task-relevant information guided by information bottleneck principle from time and frequency views, respectively, alleviating the low signal-to-noise ratio issue. Extensive experimental results on three public datasets demonstrate the superior effectiveness of our method.