Classification of Five Control/Non-control States to Enhance Asynchronous Brain-Computer Interfaces

Discriminating EEG signals between control and non-control states is a key problem in asynchronous brain-computer interfaces, and recent studies have shown that fusion-based methods can significantly improve the classification accuracy of EEG signals. In this paper, we propose a Dempster-Shafer theory-based decision fusion (DST-DF) method integrating decisions from two asynchronous algorithms for improved classification accuracy. An individualized space-frequency based complex network (ISF-OCN) algorithm and an Irregular-resampling auto-spectral analysis (IRASA) algorithm were used to extract control/non-control features for classification. The DST-DF method constructed the basic probability assignment (BPA) functions for them and assigned weights considering their varied importance in decision making. The weights were optimized using a single objective Bayesian optimization method, and were assigned to the decisions of ISF-OCN and IRASA for D-S fusion. The experimental results of 10 subjects showed that the proposed method obtained higher accuracy (93.92%) by integrating the ISF-OCN (78.87%) and IRASA (91.20%) algorithms.