Meta Learn on Constrained Transfer Learning for Low Resource Cross Subject EEG Classification

Abstract

Electroencephalogram (EEG) signal has large variance and its pattern differs significantly across subjects. Cross subject EEG classification is a challenging task due to such pattern variation and the limited target data available, as collecting and annotating EEG data for a new user is costly and involve efforts from human experts. We model the task as a transfer learning problem and propose to tackle it with meta learning on constrained transfer learning (MLCL). MLCL is an end to end trainable learning paradigm that trains on large standard datasets of known subjects and then quickly adapt to a new subject with minimal target data. The transfer process is accelerated by applying model-agnostic meta-learning (MAML) algorithm, performed under a novel constrained setting which keeps enough flexibility to adapt to new subject while significantly reducing number of parameters to transfer. This enables the adaptation done with a small amount of target data. The method can be applied to all deep learning oriented models. We performed extensive experiments across three public datasets. The proposed model outperforms current state of the arts in terms of both accuracy and AUC-ROC score for low target resource configurations. We further conducted interpretation analysis on the model, which reveals detailed information at the resolution of individual channels for the transfer process.

Keywords

• Transfer learning
• EEG classification
• meta learning
• interpretable learning