Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification

Electroencephalogram (EEG) signal is widely used in brain computer interfaces (BCI), the pattern of which differs significantly across different subjects, and poses a major challenge for real world application of EEG classifiers. We propose an efficient transfer learning method, named Meta UPdate Strategy (MUPS), for the EEG cross subject classification task. The model adopts model agnostic meta learning (MAML) algorithm in the transfer process, and only need a small amount of data to adapt onto target subject. Its mechanism involves two steps: (1) extract versatile features that are effective across all source subjects, and (2) adapt the model to target subject with limited data available. The proposed model, which originates from meta learning, aims to find feature representation that is broadly suitable for different subjects, and maximizes sensitivity of the loss function on new subject such that one or a small number of gradient steps can lead to effective adaptation. The method can be applied to all deep learning oriented models. We performed extensive experiments on two public datasets, the proposed MUPS model outperforms current state of the arts in terms of both accuracy and AUC-ROC when a small amount of target data is used.