Xu Dongqin, Li Ming'ai
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Feb 25;39(1):28-38. doi: 10.7507/1001-5515.202108060.
Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects' optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.
迁移学习在基于运动想象脑电图（MI-EEG）的脑机接口（BCI）康复系统中具有潜在的研究价值和应用前景，源域分类模型和迁移策略是直接影响目标域模型性能和迁移效率的两个重要方面。因此，我们提出了一种基于浅层视觉几何组网络（PTL-sVGG）的参数迁移学习方法。首先，使用皮尔逊相关系数筛选源域的受试者，并对每个选定受试者的MI-EEG数据进行短时傅里叶变换，以获取时频谱图图像（TFSI）。然后，简化VGG-16的架构并进行模块设计，并用源域的TFSI对改进后的sVGG模型进行预训练。此外，设计了一种基于模块的冻结微调迁移策略，以快速找到并冻结对sVGG模型贡献最大的模块，其余模块通过使用目标受试者的TFSI进行微调，以获得目标域分类模型。基于公共MI-EEG数据集进行了大量实验，PTL-sVGG的平均识别率和卡帕值分别为94.9%和0.898。结果表明，受试者优化有利于提高源域模型性能，基于模块的迁移策略可以提高迁移效率，实现在不同通道数数据集上跨受试者的模型参数快速有效迁移。这有利于减少BCI系统的校准时间，促进BCI技术在康复工程中的应用。
