PSO-based dimension reduction of EEG recordings: Implications for subject transfer in BCI

摘要

Subject transfer is a growing area of research in EEG aiming to address the lack of having enough EEG samples required for BCI by using samples originating from individuals or groups of subjects that previously performed similar tasks. This paper investigates the feasibility of two frameworks for enhancing subject transfer through a 90%+ reduction of EEG features and electrodes using Particle Swarm Optimization (PSO). In the first framework, electrodes and features selected by PSO from individual subjects are combined into a single "meta-mask" to be applied to the new subject. In the second framework, the preprocessed EEG of multiple subjects is concatenated into a single "super subject", from which PSO selects electrodes and features for use on the new subject. The study is focused on finding the optimal mixture of subjects in either of the proposed frameworks in addition to investigating the impact of various electrode and features selections. The results indicate the important role of having an optimal mixture of expertise in the subjects' data.