A Comparative Analysis on Feature Extraction and Classification of EEG Signal for Brain-Computer Interface Applications

Abstract

Classification of EEG signal for Brain-Computer Interface (BCI) applications consists of three stages: Pre-processing; Feature extraction and Classification. There are different methods implemented in these stages found in existing literature. However, the performance of the methods has been measured on different datasets which made the results incomparable to each other. To address this problem, in this paper, different combination of feature extraction and classification methods has been implemented to classify a well known dataset (dataset 2A, BCI Competition IV) so that a comparative analysis can be made based on identical platform to find out the best combination of methods. In the pre-processing step, the EEG data was band-pass filtered to remove the artifacts and Common Spatial Pattern (CSP) was applied to increase the discriminativity of the data. Two types of features: Time Domain Parameters (TDP) and Adaptive Auto-Regressive (AAR) parameters were extracted from the pre-processed EEG signal. The features were classified using two types of classifiers: Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). A comparative analysis has been conducted to identify the best combination of feature and classifier. The analysis reveals that, TDP features classified using LDA classifier provides best performance and hence demands application in real time BCI system.