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

Raza, H, Cecotti, H, Li, Y and Prasad, G 2016, 'Adaptive learning with covariate shift-detection for motor imagery-based brain–computer interface' , Soft Computing, 20 (8) , pp. 3085-3096.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Soft Computing
Publisher: Springer
ISSN: 1432-7643
Related URLs:
Funders: UKIERI DST Thematic
Depositing User: Yuhua Li
Date Deposited: 05 Jan 2016 16:54
Last Modified: 21 Jul 2016 08:41

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