A Comparative Analysis of Multi-Class EEG Classification for Brain Computer Interface
Classifying different electroencephalogram (EEG) patterns is one of the key components to designing a usable Brain Computer Interface (BCI). Although it is well known that Support Vector Machine (SVM) is a strong classifier, it does not replace simple Linear Discriminant Analysis (LDA) or Nearest Neighbor Classifier (NNC), which are still in use in current BCI systems. This paper presents a comparison of SVM, LDA and NNC against each other by applying to the EEG data, where two methods were used to preprocess the raw EEG data: the Common Spatial Patterns (CSP) method and the feature extraction by estimate the adaptive autoregressive (AAR) parameters.