Abstract
A common assumption in traditional supervised learning is the similar probability distribution of data betweenthe 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 covariateshift-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.
Original language | English |
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Pages (from-to) | 3085-3096 |
Number of pages | 12 |
Journal | Soft Computing |
Volume | 20 |
Early online date | 28 Nov 2015 |
DOIs | |
Publication status | Published (in print/issue) - 31 Aug 2016 |
Keywords
- Adaptive learning
- Transductive learning
- Covariate shift-detection ·
- Brain–computer interfaces