Two-Step Linear Discriminant Analysis for Classification of EEG Data

Abstract

We introduce a multi-step machine learning approach and use it to classify electroencephalogram (EEG) data. This approach works very well for high-dimensional spatio-temporal data with separable covariance matrix. At first all features are divided into subgroups and linear discriminant analysis (LDA) is used to obtain a score for each subgroup. Then LDA is applied to these scores, producing the overall score used for classification. In this way we avoid estimation of the high-dimensional covariance matrix of all spatio-temporal features. We investigate the classification performance with special attention to the small sample size case. We also present a theoretical error bound for the normal model with separable covariance matrix, which results in a recommendation on how subgroups should be formed for the data.