Decoding preparatory movement state-based motor imagery with multi layer energy decoder

Background Motor imagery (MI) is a widely used paradigm in brain-computer interface (BCI) research due to its potential applications in areas such as motor rehabilitation. However, as a purely cognitive process without actual physical execution, MI often results in weak and ambiguous EEG signals, limiting the performance and reliability of MI-based BCI systems. Methods We designed a preparatory movement state-based motor imagery (PMS-MI) paradigm that captures EEG features from both the preparation and MI phases. To decode the features effectively, we introduced a multi layer energy decoder (MLED) method, which extracts energy features both within the same frequency band and cross frequency bands. We evaluated the performance of the PMS-MI paradigm and MLED method using classification algorithms, primarily common spatial pattern (CSP), across various time window lengths. Results The PMS-MI paradigm elicited significant energy variations during the movement preparation phase and induced earlier event-related desynchronization (ERD) with broader frequency band activation during MI, compared to traditional MI paradigms. Classification performance using CSP in the PMS-MI paradigm surpassed that of the traditional paradigm at all time windows. Further accuracy improvements were achieved with the MLED method. Brain network analysis revealed distinct neural representations between the preparation and MI phases, and MLED effectively captured these differences. Feature fusion of preparation and MI stages resulted in classification accuracies exceeding 85% for both 1-second and 4-second windows. Conclusions Integration of preparatory movement states into the movement imagery process can generate distinguishable features at different stages and improve the classification performance of BCI systems. The proposed PMS-MI paradigm, combined with the MLED decoding method, provides a promising direction for developing more accurate and robust BCIs, particularly in the context of neurorehabilitation.