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
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-023-02769-3

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 actual use of the so-called subject-specific BCI system (SS-BCI). Herein, the goal is to provide a ready-to-use system requiring minimal effort for setup. Thus, our challenge was to design a subject-independent BCI (SI-BCI) to be used by any new user without the constraint of individual calibration. Outcomes from other studies with the same purpose were used to undertake comparisons and validate our findings. For the EEG signal processing, we used a combination of the delta (0.5–4 Hz), alpha (8–13 Hz), and beta+gamma (13–40 Hz) bands at a stage prior to feature extraction. Next, we extracted features from the 27-channel EEG using common spatial pattern (CSP) and performed binary classification (MI of right- and left-hand) with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. These analyses were done for both the SS-BCI and SI-BCI models. We employed “ leave-one-subject-out ” (LOSO) arrangement and 10-fold cross-validation to evaluate our SI-BCI and SS-BCI systems, respectively. Compared with other two studies, our work was the only one that showed higher accuracy for the LDA classifier in SI-BCI as compared to SS-BCI. On the other hand, LDA accuracy was lower than accuracy achieved with SVM in both conditions (SI-BCI and SS-BCI). Our SS-BCI accuracy reached 76.85% using LDA and 94.20% using SVM and for SI-BCI we got 80.30% with LDA and 83.23% with SVM. We conclude that SI-BCI may be a feasible and relevant option, which can be used in scenarios where subjects are not able to submit themselves to long training sessions or to fast evaluation of the so called “BCI illiteracy.” Comparatively, our strategy proved to be more efficient, giving us the best result for SI-BCI when faced against the classification performances of other three studies, even considering the caveat that different datasets were used in the comparison of the four studies. Graphical abstract