Segment alignment based cross-subject motor imagery classification under fading data

• Published in: Computers in biology and medicine Vol. 151; no. Pt A; p. 106267
• Main Authors: Wan, Zitong, Yang, Rui, Huang, Mengjie, Alsaadi, Fuad E., Sheikh, Muntasir M., Wang, Zidong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2022; Elsevier Limited
• Subjects: Algorithms; Alignment; Artificial intelligence; Biology; Brain research; Brain-Computer Interfaces; Classification; Computer engineering; Computers; Cross subject; Customization; Data fading; Datasets; Design; Electroencephalography; Electroencephalography - methods; Experiments; Fading; Feature extraction; Feature recognition; Humans; Image classification; Imagination; Internal Medicine; Learning algorithms; Machine learning; Medical research; Mental task performance; Motor imagery; Motor skill learning; Other; Segment alignment; Segments; Signal Processing, Computer-Assisted; Signal quality; Transfer learning; Motor imagery; Data fading; Transfer learning; Artificial intelligence; Segment alignment; Cross subject
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106267

Motor imagery (MI) aims to use brain imagination without actual body activities to support motor learning, and machine learning algorithms such as common spatial patterns (CSP) are proven effective in the analysis of MI signals. In the conventional machine learning-based approaches, there are two main difficulties in feature extraction and recognition of MI signals: high personalization and data fading. The high personalization problem is due to the multi-subject nature when collecting MI signals, and the data fading problem as a recurring issue in MI signal quality is first raised by us but is not widely discussed at present. Aiming to solve the above two mentioned problems, a cross-subject fading data classification approach with segment alignment is proposed to classify the fading data of one single target with the model trained with the normal data of multiple sources in this paper. he effectiveness of proposed method is verified via two experiments: a dataset-based experiment with the dataset from BCI Competition and a lab-based experiment designed and conducted by us. The experimental results obtained from both experiments show that the proposed method can obtain optimal classification performance effectively under different fading levels with data from different subjects. •Investigate the data fading problem under cross-subject classification scenario.•Propose a motor imagery fading data classification method with segment alignment.•Illustrate the method effectiveness via dataset-based and lab-based experiments.