Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers

• Published in: Medical & biological engineering & computing Vol. 61; no. 3; pp. 835 - 845
• Main Authors: dos Santos, Eliana M., San-Martin, Rodrigo, Fraga, Francisco J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Brain-Computer Interfaces; Calibration; Classification; Classifiers; Computer Applications; Data acquisition; data collection; Discriminant Analysis; EEG; Electroencephalography; Evaluation; Feature extraction; Human Physiology; Human-computer interface; Humans; Imagery, Psychotherapy; Imagination; Imaging; literacy; Mental task performance; Original Article; Radiology; Signal processing; Support Vector Machine; Support vector machines; Training; Brain-computer interface; Support vector machine (SVM); Linear discriminant analysis (LDA); Subject-independent BCI; BCI illiteracy; Motor imagery; Subject-specific BCI

Motor imagery brain–computer interface (MI-BCI) is one of the most used paradigms in EEG-based brain–computer interface (BCI). The current state-of-the-art in BCI involves tuning classifiers to subject-specific training data, acquired over several sessions, in order to perform calibration prior to a...