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.
|Schools:||Schools > School of Computing, Science and Engineering|
|Journal or Publication Title:||Soft Computing|
|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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