In order to alleviate the catastrophic forgetting problem of vanilla fine-tuning algorithms,we propose a cross-subject motor imagery EEG classification method based on inter-domain Mixup fine-tuning strategy,i.e.,Mix-Tuning.Mix-Tuning realizes cross-domain knowledge transfer through a two-stage training manner consisting of pre-training and fine-tuning.In the pre-training stage,Mix-Tuning uses the source domain data to initialize the model parameters and mine potential information of the source domain data.In the fine-tuning stage,Mix-Tuning generates inter-domain inter-polation data to fine-tune the model parameters through inter-domain Mixup.Inter-domain Mixup data enhancement strategy introduces latent information of the source domain data,which alleviates the catastrophic forgetting problem of Vanilla Fine-tuning in sparse sample scenarios and improves the generalization performance of the model.Mix-Tuning is further applied to the motor imagery EEG classification task and achieves cross-subject positive knowledge transfer.Mix-Tuning achieved an average classification accuracy of 85.50%on motor imagery task BMIdataset.Compared with 58.72%and 84.01%for Subject-specific and Subject-independent training manner,Mix-Tuning increased by 26.78%and 1.49%,respectively.The analysis results in this paper can provide a reference for cross-subject motor imagery EEG classification algorithm.
维普
万方数据