Nonnegative Matrix Factorization Algorithms using a Constraint to Increase the Discriminability of Two Classes for EEG Feature Extraction

Nonnegative matrix factorization (NMF) is an algorithm used for blind source separation. It has been reported that NMF algorithms can be utilized as an effective means to extract features from a motor-imagery related EEG spectrum, which is often used in brain-computer interfaces (BCI). BCI systems enable users to control electrical devices without moving their body parts, and are often tasked with interpreting a user's intentions through motor-imagery related EEG features. In other words, they require EEG signal classification in order to reflect user intentions. In this study, constraints are placed on NMF and kernel NMF (KNMF) algorithms to increase the discriminability between two classes by increasing the energy difference between their potential sources in a spectral EEG signal. To evaluate the proposed algorithms, the IDIAP database, which contains the motor-imagery related EEG spectrum of three subjects, was adopted to test the discrimination between two classes. Using the database, the classification accuracy of the proposed constraint was 75%, which was 7% higher than what was obtained through NMF without a constraint. Similarly, the classification accuracy of KNMF with the proposed constraint was also 4% higher than that of KNMF without a constraint, and reached 78%.