Classification Analysis of Tensor-Based Recovered Missing EEG Data

This paper discusses impact of recovering missing Electroencephalography (EEG) data on classification accuracy of hand movements using tensor-based methods. Improvement in classification accuracy is important for efficient performance of prosthesis. Classification accuracy relies on quality of the observed data. In practice, observed data is usually incomplete because of disconnection of electrodes and other artefacts, which negatively affect the classification accuracy. In this paper, we employ tensor-based imputation methods (canonical/polyadiac decomposition (CPD), weighted optimization version of CPD (CP-WOPT and Nonnegative Matrix Factorization (NMF)) to recover missing data in EEG signals and apply various classifiers to classify hand movements on recovered data. In particular, structured missing data was considered because that is how data gets missed in real-life data acquisition. Percentage of missing data was changed from 10% to 50%. Classifiers are applied on complete, missing and recovered data explicitly to test the performance of our framework. Results show that mean classification accuracy on complete data, missing data and recovered data was 71%, 53% and 64% respectively which shows applicability of our framework.