Study on model for predicting the intra-individual difference in left prefrontal pole electroencephalogram variability and its evaluation

This paper introduces a novel statistical model for estimating the intraindividual difference in left prefrontal cortex electroencephalogram (EEG) activity, and a method for evaluating the proposed model. It is known that EEGs contain individual characteristics. However, extraction of these individual characteristics has not been reported. The analyzed frequency components of an EEG can be subdivided into components that contain a significant number of features, and components that do not contain such features. From the viewpoint of these feature differences, we propose a model for extracting the features of an EEG. The model assumes a latent structure and employs factor analysis, treating the model error as personal error. We consider the first factor loading, which is calculated by eigenvalue decomposition, as the EEG feature. Furthermore, we use a k-nearest neighbor (kNN) algorithm for evaluating the proposed model and the extracted EEG features. In general, the distance metric used is the Euclidean distance. It is possible that the distance metric used depends on the characteristics of the extracted EEG features and on the subject. Therefore, depending on the subject, we use one of three distance metrics: the Euclidean distance, the cosine distance, or the coefficient of correlation. Finally, in order to show the effectiveness of the proposed model, we present the results of an experiment using real EEG data.