Hierarchical Domain Adaptation Projective Dictionary Pair Learning Model for EEG Classification in IoMT Systems

Abstract

Epilepsy recognition based on electroencephalogram (EEG) and artificial intelligence technology is the main tool of health analysis and diagnosis in Internet of medical things (IoMT). As a distributed learning framework, federated learning can train a shared model from multiple independent edge nodes using local data, which has greatly promoted the development of IoMT. One of the main challenges of EEG-based epilepsy recognition in IoMT is that EEG records show varying distributions in different devices, different times, and different people. This nonstationary characteristic of EEG reduces the accuracy of the recognition model. To improve the classification performance in IoMT, a hierarchical domain adaptation projective dictionary pair learning (HDA-PDPL) model is developed in the study. HDA-PDPL integrates EEG signals from different domains (person, edge nodes, devices, etc.) into a set of hierarchical subspace and simultaneously learns synthesis and analysis dictionary pairs in each layer. Specifically, a nonlinear transform function is introduced to seek hierarchical feature projection. The domain adaptation term on sparse coding builds a connection between different domains. Thus, the shared synthesis and analysis dictionaries can encode domain-invariant representation and discrimination knowledge from different domains. Besides, the local preserved term of projective codes is introduced to capture the potential discriminative local structures of samples. The experimental results on two EEG epilepsy classifications verified that the HDA-PDPL model can outperform other comparisons by utilizing more shared knowledge of different domains.