  Motor imagery brain computer interface designs are considered difficult due
to limitations in subject-specific data collection and calibration, as well as
demanding system adaptation requirements. Recently, subject-independent (SI)
designs received attention because of their possible applicability to multiple
users without prior calibration and rigorous system adaptation. SI designs are
challenging and have shown low accuracy in the literature. Two major factors in
system performance are the classification algorithm and the quality of
available data. This paper presents a comparative study of classification
performance for both SS and SI paradigms. Our results show that classification
algorithms for SS models display large variance in performance. Therefore,
distinct classification algorithms per subject may be required. SI models
display lower variance in performance but should only be used if a relatively
large sample size is available. For SI models, LDA and CART had the highest
accuracy for small and moderate sample size, respectively, whereas we
hypothesize that SVM would be superior to the other classifiers if large
training sample-size was available. Additionally, one should choose the design
approach considering the users. While the SS design sound more promising for a
specific subject, an SI approach can be more convenient for mentally or
physically challenged users.
