A hybrid method for asynchronous detection of motor imagery electroencephalogram fusing alpha rhythm and movement-related cortical

The development of brain-computer interfaces (BCIs) enables direct human-computer interaction by realtime monitoring and translation of brain signals. Motor imagery electroencephalography (MI-EEG) systems, known for their non-invasiveness and user-friendliness, are particularly promising. Asynchronous systems, offering enhanced flexibility, represent the future of practical BCI applications. However, existing asynchronous detection methods in MI-EEG systems have yet to achieve satisfactory accuracy and latency. This paper proposes a hybrid asynchronous detection method that combines alpha rhythm changes and movement-related cortical potential (MRCP) features based on weighted Dempster-Shafer theory (AMAD-DS). The AMADDS method employs a hybrid architecture and two multi-domain joint analysis algorithms to process EEG signals from different areas: detecting alpha rhythm features in the occipital area and MRCP features in the sensorimotor area. The method fuses the results of these detections at the decision level using weighted D-S theory to produce the final output. Experiments conducted on a MI-EEG-based BCI system demonstrate that AMAD-DS outperformed methods using only MRCP or alpha rhythm features, improving the true positive rate by 12.6%, reducing the false positive rate by 0.5 FPs/min, and ensuring that the detection time of motor imagery onset is less than 500 ms. Online experiments further validate the method's effectiveness, achieving true positive rate of 91.1% and a false positive rate of 0.16 FPs/min.