Classification of intention understanding using EEG-NIRS bimodal system

When people observe the actions of others, they always try to understand the intentions underlying the actions. The neural mechanism of this understanding is referred to as the mirror neuron system (MNS). Different actions may correspond to different intentions, and the activation of the MNS in the human brain may also be slightly different. The present study distinguishes these differences according to functional brain imaging signals analyzed with machine learning. Brain signals were detected when the participants observed two types of actions: (1) grasping a cup for drinking, and (2) no meaningful contact. A synchronous measurement method for EEG and NIRS was adopted to increase the information contained in the brain signals. In order to obtain better classification accuracies, the method of functional brain networks was used. This method can be used to examine the relationship between brain regions. First, phase synchronization and Pearson correlation were used to calculate correlations for EEG channels and NIRS channels, respectively. Next, correlation matrices were converted into binary matrices, and the local properties of the networks were obtained. Finally, the feature vectors for the classifier were selected by analysis of their significance. In addition, EEG data and NIRS data were combined at the feature level and better classification results were obtained.