Comparison of LORETA and CSP for Brain-Computer Interface Applications

• Published in: International Multi-Conference on Systems, Signals, and Devices pp. 817 - 822
• Main Authors: Santos, Eliana M., San-Martin, Rodrigo, Fraga, Francisco J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.03.2021
• Subjects: Brain-Computer Interface; Brain-computer interfaces; Common Spatial Pattern; Electroencephalography; Feature extraction; Linear Discriminant Analysis; Location awareness; Low-Resolution Brain Electromagnetic Tomography; Motor Imagery; Performance evaluation; Support vector machine classification; Support Vector Machines; Tomography
• ISSN: 2474-0446
• DOI: 10.1109/SSD52085.2021.9429518

Motor imagery (MI) is the most used paradigm in Brain-Computer Interface (BCI). As in other BCI paradigms, it is divided into feature extraction and classification. In this study, we implemented two feature extraction methods, one based on the well-known Common Spatial Pattern (CSP) algorithm and the other based on a not so well-known electroencephalogram (EEG) source localization technique called Low-Resolution Brain Electromagnetic Tomography (LORETA). We evaluated 30 right-handed subjects from an EEG data set made publicly available through Giga Science, where participants performed MI of left- and right-hand movements. After feature extraction with CSP and LORETA, MI tasks classification were carried out using the Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naïve Bayes (NB) and Multi-Layer Perceptron (MLP) algorithms. Finally, we evaluated classification performance with all possible combination of classifiers and feature extraction methods. For all classifiers, the CSP feature extraction method performed better than LORETA. The best classification accuracy for the LORETA method was 71.2% and for the CSP method was 94.2%, both achieved with SVM.