A novel semi-supervised meta learning method for subject-transfer brain–computer interface

• Published in: Neural networks Vol. 163; pp. 195 - 204
• Main Authors: Li, Jingcong, Wang, Fei, Huang, Haiyun, Qi, Feifei, Pan, Jiahui
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2023
• Subjects: Algorithms; Brain; Brain-Computer Interfaces; Electroencephalography - methods; Emotion recognition; Event-related potential; Evoked Potentials; Humans; Meta learning; Semi-supervised; Sleep staging; Supervised Machine Learning; Transfer learning; Event-related potential; Emotion recognition; Transfer learning; Semi-supervised; Meta learning; Sleep staging
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2023.03.039

The brain–computer interface (BCI) provides a direct communication pathway between the human brain and external devices. However, the models trained for existing subjects perform poorly on new subjects, which is termed the subject calibration problem. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer calibration. The proposed SSML learns a model-agnostic meta learner with existing subjects and then fine-tunes the meta learner in a semi-supervised learning manner, i.e. using a few labelled samples and many unlabelled samples of the target subject for calibration. It is significant for BCI applications in which labelled data are scarce or expensive while unlabelled data are readily available. Three different BCI paradigms are tested: event-related potential detection, emotion recognition and sleep staging. The SSML achieved classification accuracies of 0.95, 0.89 and 0.83 in the benchmark datasets of three paradigms. The runtime complexity of SSML grows linearly as the number of samples of target subject increases so that is possible to apply it in real-time systems. This study is the first attempt to apply semi-supervised model-agnostic meta learning methodology for subject calibration. The experimental results demonstrated the effectiveness and potential of the SSML method for subject-transfer BCI applications.