Research on Hybrid Online Brain-Computer Interface System Based on MI-mVEP

In this study, a hybrid online brain-computer interface (BCI) system was established to address the issue of low recognition accuracy and limited imagery tasks in motor imagery (MI) based on BCI systems. This hybrid system utilized both MI and motion-onset visual evoked potential (mVEP). After simultaneously acquiring MI and mVEP potentials and electroencephalogram (EEG) signal preprocessing, the filter bank common spatial pattern (FBCSP) algorithm was used to extract MI and mVEP features. These features were then employed to construct training set feature vectors. The training set feature vectors of MI and mVEP signals were classified using the backpropagation (BP) neural network and support vector machine (SVM) algorithms, respectively. Based on the established classification model, single test data were recognized online, and the results were fed back to subjects in real-time via block moving routes. The presented system can realize five different block moving paths, with faster moving effects achieved when the system induces MI and mVEP potential simultaneously along the lower left or lower right diagonal path. The experimental results showed that the average classification accuracies of the lower left and lower right diagonal moving effects are up to 84.53% and 84.60%, respectively. These results indicate that this study effectively controls the fast-moving blocks in the online BCI system of MI-mVEP, providing a theoretical basis and experimental support for the research of a hybrid online BCI system based on MI-mVEP. In summary, the proposed system has demonstrated improved performance through the combination of simultaneous MI and mVEP potential utilization and efficient classification algorithms, with promising future applications in BCI research.