Personalized motor imagery prediction model based on individual difference of ERP

Abstract

Objective. Motor imagery-based brain–computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed to solve the MI individual difference. Approach. A novel paradigm named action observation-based multi-delayed matching posture task evokes ERP during a delayed matching posture task phase by retrieving picture stimuli and videos, and generates MI electroencephalogram through action observation and autonomous imagery in an action observation-based motor imagery phase. Based on the correlation between the ERP and MI, a logistic regression-based personalized MI prediction model is built to predict each individual’s suitable MI action. 32 subjects conducted the MI task with or without the help of the prediction model to select the MI action. Then classification accuracy of the MI task is used to evaluate the proposed model and three traditional MI methods. Main results. The personalized MI prediction model successfully predicts suitable action among 3 sets of daily actions. Under suitable MI action, the individual’s ERP amplitude and event-related desynchronization (ERD) intensity are the largest, which helps to improve the accuracy by 14.25%. Significance. The personalized MI prediction model that uses the temporal ERP features to predict the classification accuracy of MI is feasible for improving the individual’s MI-BCI performance, providing a new personalized solution for the individual difference and practical BCI application.