Double Stage Transfer Learning for Brain-Computer Interfaces.

In the application of brain-computer interfaces (BCIs), electroencephalogram (EEG) signals are difficult to collect in large quantities due to the non-stationary nature and long calibration time required. Transfer learning (TL), which transfers knowledge learned from existing subjects to new subjects, can be applied to solve this problem. Some existing EEG-based TL algorithms cannot achieve good results because they only extract partial features. To achieve effective transfer, a double-stage transfer learning (DSTL) algorithm which applied transfer learning to both preprocessing stage and feature extraction stage of typical BCIs was proposed. First, Euclidean alignment (EA) was used to align EEG trials from different subjects. Second, aligned EEG trials in the source domain were reweighted by the distance between the covariance matrix of each trial in the source domain and the mean covariance matrix of the target domain. Lastly, after extracting spatial features with common spatial patterns (CSP), transfer component analysis (TCA) was adopted to reduce the differences between different domains further. Experiments on two public datasets in two transfer paradigms (multi-source to single-target (MTS) and single-source to single-target (STS)) verified the effectiveness of the proposed method. The proposed DSTL achieved better classification accuracy on two datasets: 84.64% and 77.16% in MTS, 73.38% and 68.58% in STS, which shows that DSTL performs better than other state-of-the-art methods. The proposed DSTL can reduce the difference between the source domain and the target domain, providing a new method for EEG data classification without training dataset.