Decoding Premovement Patterns with Task-Related Component Analysis

Noninvasive brain–computer interface (BCI)-based electroencephalograms (EEGs) have made great progress in cognitive activities detection. However, the decoding of premovements from EEG signals remains a challenge for noninvasive BCI. This work aims to decode human intention (movement or rest) before movement onset from EEG signals. We propose to decode premovement patterns from movement-related cortical potential activities with task-related component analysis and canonical correlation patterns (TRCA+CCPs). Specifically, we first optimize the MRCP data with the spatial filter TRCA. CCPs are then extracted from the optimized signals. The extracted CCPs are classified with the linear discriminated analysis classifier. We applied the classification in a sliding window, which changes from readiness potential (RP section) to movement-monitoring potential (MMP section). The classification result on event-related desynchronization (ERD) indicates that the motor cortex becomes active as the limbs move. When applying classification between elbow flexion and rest, the proposed TRCA+CCP method achieves an accuracy of 0.9001±0.0997 in the RP section. The previous methods, discriminative canonical pattern matching + common spatial pattern (DCPM+CSP) and the optimized DCPM+CSP method, exhibit accuracy values of 0.7827± 0.1276 and 0.8141±0.1295 for the RP section, respectively. Compared with these methods, the proposed TRCA+CCP method achieves higher average accuracy in the RP section. The proposed TRCA+CCP method can decode the patterns in the RP section efficiently, which indicates that the premovement patterns in EEG signals can be decoded before execution of the movement. The system is expected to assist movement detection in ERD analysis.