Multi-class motor motion imagery using common spatial patterns based on joint approximate diagonalization

Abstract

Motion intention can be detected from human EEG signals through BCI, which can facilitate motor motion control for disabled or paralyzed people. However, the design of multiclass BCI is a very challenging task because of the need to extract complex spatial and temporal patterns from noisy multidimensional time series generated from EEG measurements. This paper proposes a multiclass common spatial pattern (MCSP) based on Joint Approximate Diagonalization (JAD) for multiclass BCIs. The proposed method based on fast Frobenius diagonalization (FFDIAG) is compared with one versus rest common spatial patterns (CSP) on the BCI competition IV dataset 2a. The classification accuracies obtained from 10×10-fold cross-validations on the training dataset are compared using K-Nearest Neighbor, Classification Trees and Support Vector Machine classifiers. The classifiers are boosted using Stagewise Additive Modelling using a Multi-class exponential loss function (SAMME) and Adaboost.M1. The proposed MCSP based on FFDIAG yields an averaged accuracy of 54.1% compared to 28.8% given by the one versus rest CSP methods.