Research On The Recognition Method Of Motor Imagery Based On The Fusion Of Transfer Learning And Curriculum Learning

Motor imagery-based EEG signal recognition is a neurocontrol technology based on EEG signals,which recognizes imagined movements by analyzing cortical activity in the brain.This technology can be applied to brain-machine interface systems,enabling individuals to control external devices such as mobile robots and prosthetics by imagining movements.However,since motor imagery is an actively induced EEG signal,it is difficult to fully control the imagined content and intensity of participants in experiments,and differences in imagined content and quality among different participants can also lead to limitations and affect recognition accuracy.This paper focuses on improving classification recognition accuracy by studying EEG signal recognition analysis with motor imagery EEG signals as the research object.The specific research content is as follows:First,research is conducted on pre-processing methods for EEG signals to remove noise and other interference in order to facilitate more accurate subsequent analysis and recognition.The pre-processing method for EEG signals is first studied,and the independent component analysis algorithm is used to remove interference from eye and muscle electrical signals.Secondly,in the process of feature extraction from EEG signals,research is conducted on both the Riemannian manifold feature extraction algorithm and the common spatial pattern feature extraction algorithm.In the classification and recognition stage of EEG signals,a motor imagery recognition method based on sample migration and predefined curriculum learning fusion is proposed.Due to the non-stationary nature,individual differences,and differences in acquisition devices,the "quality" of such signal samples varies,and the cumbersome process of signal acquisition and labeling results in fewer training samples,thereby affecting the training effect of the model.The study uses a curriculum learning strategy to weaken the impact of low-quality samples on the classifier training,while fusing predefined curriculum learning with transfer learning to select source domain samples that are consistent with the target domain distribution to solve the problem of effective transfer of source domain samples under the condition of quality differences in the target domain samples.To this end,a fully connected network is redesigned,with both sample confidence values as inputs and outputs,and the network parameters are updated through an auxiliary classifier.Finally,method validation is performed through comparative experiments and ablation experiments.To better reduce the impact of data distribution differences between different subjects and reduce the dependence on prior knowledge of EEG signals in predefined curriculum learning,a motor imagery analysis method based on feature transformation transfer and selfpaced learning fusion is proposed.By implementing distribution adaptation,data distribution alignment is achieved,and self-paced learning strategy is introduced to weaken the influence of sample quality differences,optimize the training process,and improve the reliability of distribution adaptation.Finally,a classifier is learned by combining the above process using structural risk minimization.Unlike predefined curriculum learning,selfpaced learning can completely measure the degree of sample quality differences without human intervention,which achieves more comprehensive measurement.