Intra- and Inter-Subject Common Spatial Pattern for Reducing Calibration Effort in MI-Based BCI

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; pp. 904 - 916
• Main Authors: Wei, Qingguo, Ding, Xinjie
• Format: Journal Article
• Language: English
• Published: United States IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain; Brain-computer interfaces; Calibration; Classification; Classification algorithms; Computer applications; data alignment; Discriminant analysis; Electroencephalography; Feature extraction; Human-computer interface; Implants; intra- and inter-subject common spatial pattern; Mental task performance; motor imagery; Motor task performance; Spatial filtering; Support vector machines; Task analysis; Training; Training data; Transfer learning
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2023.3236372

One major problem limiting the practicality of a brain-computer interface (BCI) is the need for large amount of labeled data to calibrate its classification model. Although the effectiveness of transfer learning (TL) for conquering this problem has been evidenced by many studies, a highly recognized approach has not yet been established. In this paper, we propose a Euclidean alignment (EA)-based intra- and inter-subject common spatial pattern (EA-IISCSP) algorithm for estimating four spatial filters, which aim at exploiting intra- and inter-subject similarities and variability to enhance the robustness of feature signals. Based on the algorithm, a TL-based classification framework was developed for enhancing the performance of motor imagery (MI) BCIs, in which the feature vector extracted by each filter is dimensionally reduced by linear discriminant analysis (LDA) and a support vector machine (SVM) is used for classification. The performance of the proposed algorithm was evaluated on two MI data sets and compared with that of three state-of-the-art TL algorithms. Experimental results showed that the proposed algorithm significantly outperforms these competing algorithms for training trials per class from 15 to 50 and can reduce the amount of training data while maintaining an acceptable accuracy, thus facilitating the practical application of MI-based BCIs.