Improving the Accuracy of Motor Imagery EEG Classification Using the Common Spatial Patterns Method

Abstract

In recent years, research on Brain-Computer Interface (BCI) technology using electroencephalography (EEG) based on motor imagery has gained attention as a potential means of supporting the lives of individuals with physical disabilities. However, conventional EEG devices require lengthy setup times, placing a significant burden on users. This study aims to classify motor intentions by processing data obtained from left-hand and right-hand motor imagery experiments using a simplified EEG device. Previous studies have attempted feature extraction using Event-Related Synchronization/Desynchronization (ERS/ERD), but only partial features were identified, resulting in low classification accuracy. On the other hand, using Common Spatial Patterns (CSP) has been reported to improve accuracy. To validate the effectiveness of CSP, we conducted experiments using the dataset provided by the Berlin BCI Group. The results demonstrated that CSP successfully highlighted differences in features between lefthand and right-hand motor imagery. Although CSP also improved accuracy compared to ERS/ERD in the dataset from our laboratory, further improvements are still necessary