Super-Resolution Level Separation: A Method for Enhancing Electroencephalogram Classification Accuracy Through Super-Resolution Level Separation

Abstract

Accurate identification of electroencephalogram (EEG) signals forms the basis for the development and application of brain-computer interface (BCI) devices. Signal preprocessing is an essential part of most EEG classification and recognition systems.In this study, we proposed a method called Superresolution level separation (SRLS) to add dimensions of information to the classification model. First, we calculated the correlation between the EEG signals acquired by each channel and divided these channels into multiple levels. Next, we used a super-resolution method to calculate common EEGs (C-EEGs) acquired by the channels. In addition, we designed an EEG-split-informer (ES-informer) model based on an informer model to enable small-sample users to obtain highly fitting C-EEGs. We then calculated the difference between the C-EEG and true EEG (T-EEG) to obtain the unique EEGs (U-EEGs) acquired by each channel, thus, adding dimensions to the data inputted to the classification model. Utilizing the 2008 2a motor imagery (MI) EEG dataset from the BCI Competition and the P300 paradigm data collected, EEGNet was employed as the classification model to validate the efficacy of the proposed SRLS method. The results of the experiments indicated that the SRLS method augmented the input dimension of the model and amplified the classification accuracy by over 7% for MI and 3.6% for P300. These findings demonstrate that SRLS is capable of enhancing the recognition accuracy of EEG.