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Objective: Classifier transfers usually come with dataset shifts. To overcomethem, online strategies have to be applied. For practical applications,limitations in the computational resources for the adaptation of batch learningalgorithms, like the SVM, have to be considered.  Approach: We review and compare several strategies for online learning withSVMs. We focus on data selection strategies which limit the size of the storedtraining data [...]  Main Results: For different data shifts, different criteria are appropriate.For the synthetic data, adding all samples to the pool of considered samplesperforms often significantly worse than other criteria. Especially, adding onlymisclassified samples performed astoundingly well. Here, balancing criteriawere very important when the other criteria were not well chosen. For thetransfer setups, the results show that the best strategy depends on theintensity of the drift during the transfer. Adding all and removing the oldestsamples results in the best performance, whereas for smaller drifts, it can besufficient to only add potential new support vectors of the SVM which reducesprocessing resources.  Significance: For BCIs based on EEG models, trained on data from acalibration session, a previous recording session, or even from a recordingsession with one or several other subjects, are used. This transfer of thelearned model usually decreases the performance and can therefore benefit fromonline learning which adapts the classifier like the established SVM. We showthat by using the right combination of data selection criteria, it is possibleto adapt the classifier and largely increase the performance. Furthermore, insome cases it is possible to speed up the processing and save computational byupdating with a subset of special samples and keeping a small subset of samplesfor training the classifier.

Classifier Transfer with Data Selection Strategies for Online Support Vector Machine Classification with Class Imbalance