Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface

Raza, H; Cecotti, H; Li, Y; Prasad, G

Abstract

A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain–computer interfaces (BCIs). In such systems, there is a necessity for continuous monitoring of the process behavior, and tracking the state of the covariate shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and -adaptation methodology, and its application to motor imagery-based BCIs. A covariate shift-detection test based on an exponential weighted moving average model is used to detect the covariate shift in the features extracted from motor imagery-based brain responses. Following the covariate shift-detection test, the methodology initiates an adaptation by updating the classifier during the testing/operating phase. The usefulness of the proposed method is evaluated using real-world BCI datasets (i.e. BCI competition IV dataset 2A and 2B). The results show a statistically significant improvement in the classification accuracy of the BCI system over traditional learning and semi-supervised learning methods.

Citation

Raza, H., Cecotti, H., Li, Y., & Prasad, G. (2016). Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface. Soft Computing, 20(8), 3085-3096. https://doi.org/10.1007/s00500-015-1937-5

• Journal Article Type: Article
• Online Publication Date: Nov 28, 2015
• Publication Date: Aug 1, 2016
• Deposit Date: Jan 5, 2016
• Publicly Available Date: Apr 5, 2016
• Journal: Soft Computing
• Print ISSN: 1432-7643
• Electronic ISSN: 1433-7479
• Publisher: Springer Verlag
• Volume: 20
• Issue: 8
• Pages: 3085-3096
• DOI: https://doi.org/10.1007/s00500-015-1937-5
• Publisher URL: http://dx.doi.org/10.1007/s00500-015-1937-5
• Related Public URLs: http://link.springer.com/journal/500
• Additional Information: Projects : A BCI operated hand exoskeleton based neurorehabilitation