Image classification based on integration of EEG and visual features Via LFDA-MSLPCCA

This paper presents a novel image classification based on the integration of EEG and visual features. In the proposed method, we first obtain classification results by separately using EEG and visual features. Then we merge the above classification results based on kernelized version of Supervised Learning from Multiple Experts (KSLME) via Multiset Supervised Locality Preserving Canonical Correlation Analysis (MSLPCCA) to obtain final classification results. It should be noted that when the number of samples is fewer than the dimension of a sample data used in MSLPCCA, we have to reduce the dimension. Therefore, we propose MSLPCCA based on Local Fisher Discriminant Analysis (LFDA) which can take class information into account. Then the integration of all of the classifications results becomes feasible by MSLPCCA based on LFDA.