#6268. Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI

September 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Electrical and Electronic Engineering;
Biomedical Engineering;
Human-Computer Interaction;
Behavioral Neuroscience;
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Abstract:
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. The present classification algorithms include two parametric (LDA and SVM) and two non-parametric (k-NN and CART) methods.
Keywords:
Classification; Motor imagery; Sample size; Subject-independent BCI; Subject-Specific BCI

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