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

Haider Raza, Hubert Cecotti, Y Li, Girijesh Prasad

Research output: Contribution to journalArticlepeer-review

82 Citations (Scopus)
36 Downloads (Pure)


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 languageEnglish
Pages (from-to)3085-3096
Number of pages12
JournalSoft Computing
Early online date28 Nov 2015
Publication statusPublished (in print/issue) - 31 Aug 2016


  • Adaptive learning
  • Transductive learning
  • Covariate shift-detection ·
  • Brain–computer interfaces


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