收录:
摘要:
Deep neural network is a promising method to recognize motor imagery electroencephalography (MI-EEG), which is often used as the source signal of a rehabilitation system; and the core issues are the data representation and the matched neural networks. MI-EEG images is one of the main expressions, however, all the measured data of a trial are usually integrated into one image, causing information loss, especially in the time dimension; and the neural network architecture might not fully extract the features over the alpha and beta frequency bands, which are closely related to MI. In this paper, we propose a key band imaging method (KBIM). A short time Fourier transform is applied to each electrode of the MI-EEG signal to generate a time-frequency image, and the parts corresponding to the alpha and beta bands are intercepted, fused, and further arranged into the EEG electrode map by the nearest neighbor interpolation method, forming two key band image sequences. In addition, a hybrid deep neural network named the parallel multimodule convolutional neural network and long short-term memory network (PMMCL) is designed for the extraction and fusion of the spatial-spectral and temporal features of two key band image sequences to realize automatic classification of MI-EEG signals. Extensive experiments are conducted on two public datasets, and the accuracies after 10-fold cross-validation are 97.42% and 77.33%, respectively. Statistical analysis shows the superb discrimination ability for multiclass MI-EEG too. The results demonstrate that KBIM can preserve the integrity of the feature information, and they well match with PMMCL.
关键词:
通讯作者信息:
电子邮件地址: