Weighted Transfer Learning for Improving Motor Imagery-Based Brain-Computer Interface

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 7; pp. 1352 - 1359
• Main Authors: Azab, Ahmed M., Mihaylova, Lyudmila, Ang, Kai Keng, Arvaneh, Mahnaz
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain; Brain computer interface; Brain modeling; Brain-Computer Interfaces; Calibration; Classification; Classifiers; Divergence; Electroencephalography; Estimation; Healthy Volunteers; Human-computer interface; Humans; Imagery; Imagination - physiology; logistic regression; Logistics; Machine Learning; Mental task performance; motor imagery; Motor skill learning; Movement - physiology; Objective function; Parameter estimation; Psychomotor Performance; Regularization; Similarity; Space Perception - physiology; Statistical analysis; Task analysis; Training; Transfer learning
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2019.2923315

One of the major limitations of motor imagery (MI)-based brain-computer interface (BCI) is its long calibration time. Due to between sessions/subjects variations in the properties of brain signals, typically, a large amount of training data needs to be collected at the beginning of each session to calibrate the parameters of the BCI system for the target user. In this paper, we propose a novel transfer learning approach on the classification domain to reduce the calibration time without sacrificing the classification accuracy of MI-BCI. Thus, when only few subject-specific trials are available for training, the estimation of the classification parameters is improved by incorporating previously recorded data from other users. For this purpose, a regularization parameter is added to the objective function of the classifier to make the classification parameters as close as possible to the classification parameters of the previous users who have feature spaces similar to that of the target subject. In this paper, a new similarity measure based on the Kullback-Leibler divergence (KL) is used to measure the similarity between two feature spaces obtained using subject-specific common spatial patterns (CSP). The proposed transfer learning approach is applied on the logistic regression classifier and evaluated using three datasets. The results showed that compared with the subject-specific classifier, the proposed weighted transfer learning classifier improved the classification results, particularly when few subject-specific trials were available for training (p <; 0.05). Importantly, this improvement was more pronounced for users with medium and poor accuracy. Moreover, the statistical results showed that the proposed weighted transfer learning classifier performed significantly better than the considered comparable baseline algorithms.