Chang Zhanyuan, Zhang Congcong, Li Chuanjiang
College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China.
Micromachines (Basel). 2022 Jun 10;13(6):927. doi: 10.3390/mi13060927.
For the successful application of brain-computer interface (BCI) systems, accurate recognition of electroencephalography (EEG) signals is one of the core issues. To solve the differences in individual EEG signals and the problem of less EEG data in classification and recognition, an attention mechanism-based multi-scale convolution network was designed; the transfer learning data alignment algorithm was then introduced to explore the application of transfer learning for analyzing motor imagery EEG signals. The data set 2a of BCI Competition IV was used to verify the designed dual channel attention module migration alignment with convolution neural network (MS-AFM). Experimental results showed that the classification recognition rate improved with the addition of the alignment algorithm and adaptive adjustment in transfer learning; the average classification recognition rate of nine subjects was 86.03%.
对于脑机接口（BCI）系统的成功应用，准确识别脑电图（EEG）信号是核心问题之一。为了解决个体EEG信号的差异以及分类识别中EEG数据较少的问题，设计了一种基于注意力机制的多尺度卷积网络；然后引入迁移学习数据对齐算法，探索迁移学习在分析运动想象EEG信号中的应用。使用BCI竞赛IV的数据集2a来验证所设计的双通道注意力模块迁移对齐卷积神经网络（MS-AFM）。实验结果表明，在迁移学习中添加对齐算法和自适应调整后，分类识别率有所提高；九名受试者的平均分类识别率为86.03%。
