Transfer learning is increasingly used to decode multi-class motor imagery tasks. Previous transfer learning ignored the optimizability of the source model, weakened the adaptability to the target domain and limited the performance. This paper first proposes the multi-loss fusion convolutional neural network (MF-CNN) to make an optimizable source model. Then we propose a novel source optimized transfer learning (SOTL), which optimizes the source model to make it more in line with the target domain's features to improve the target model's performance. We transfer the model trained from 16 healthy subjects to 16 stroke patients. The average classification accuracy achieves 51.2 ± 0.17% in the four types of unilateral upper limb motor imagery tasks, which is significantly higher than the classification accuracy of deep learning (p < 0.001) and transfer learning (p < 0.05). In this paper, an MI model from the data of healthy subjects can be used for the classification of stroke patients and can demonstrate good classification results, which provides experiential support for the study of transfer learning and the modeling of stroke rehabilitation training.

迁移学习越来越多地用于解码多类运动想象任务。以往的迁移学习忽视了源模型的可优化性，削弱了对目标领域的适应性，限制了性能。本文首先提出了多损失融合卷积神经网络（MF-CNN）来制作可优化的源模型。然后，我们提出了一种新颖的源优化迁移学习（SOTL），它优化源模型，使其更符合目标领域的特征，从而提高目标模型的性能。我们将 16 名健康受试者训练的模型转移到 16 名中风患者身上。在四类单侧上肢运动想象任务中，平均分类精度达到51.2±0.17%，显着高于深度学习（p  <0.001）和迁移学习（p  <0.05）的分类精度。本文利用健康受试者数据建立的MI模型可用于脑卒中患者的分类，并表现出良好的分类效果，为迁移学习的研究和脑卒中康复训练的建模提供了经验支持。