Using particle swarm to select frequency band and time interval for feature extraction of EEG based BCI

Many motor imagery based BCI systems will utilize the common spatial pattern (CSP) feature for task classification. However, the frequency band and time interval involved for CSP feature extraction will have large effect on the BCI performance. In this paper, with aim to find the optimal frequency band and time interval for effective CSP feature extraction, an approach based on particle swarm optimization is proposed. In this approach, the frequency band and time interval are coded with the particles and the optimal settings of them can be simultaneously detected by the evolution of particles for individual subject. The results from eighteen BCI participants confirmed that the individual frequency band and time interval provided by particles could actually improve the discriminative ability of CSP features, and it has potential application in online BCI system.