Wen Dong, Li Rou, Tang Hao, Liu Yijun, Wan Xianglong, Dong Xianling, Saripan M Iqbal, Lan Xifa, Song Haiqing, Zhou Yanhong
IEEE Trans Neural Syst Rehabil Eng. 2022;30:1041-1051. doi: 10.1109/TNSRE.2022.3166224. Epub 2022 Apr 25.
In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spatial cognitive training. Besides, the multi-dimensional conditional mutual information method was used to extract the frequency band features of the EEG data. And the coupling features under the combination of multi-frequency bands were transformed into multi-spectral images. At the same time, the idea of Densenet was introduced to improve the multi-scale convolutional neural network. Firstly, according to the discreteness of multispectral EEG image features, two-scale convolution kernels were used to calculate and learn useful channel and frequency band feature information in multispectral image data. Secondly, to enhance feature propagation and reduce the number of parameters, the dense network was connected after the multi-scale convolutional network, and the learning rate change function of the stochastic gradient descent algorithm was optimized to objectively evaluate the training effect. The experimental results showed that compared with the classical convolution neural network (CNN) and multi-scale convolution neural network, the proposed MHCNN had better classification performance in the six frequency band combinations with the highest accuracy of 98%: Theta-Alpha2-Gamma, Alpha2-Beta2-Gamma, Beta1-Beta2-Gamma, Theta-Beta2-Gamma, Theta- Alpha1-Gamma, and Alpha1-Alpha2-Gamma. By comparing the classification results of six frequency band combinations, it was found that the combination of the Theta-Beta2-Gamma band had the best classification effect. The MHCNN classification method proposed in this research could be used as an effective biological indicator of spatial cognitive training effect and could be extended to other brain function evaluations.
本研究提出一种用于空间认知能力评估的多尺度高密度卷积神经网络（MHCNN）分类方法，旨在实现空间认知训练前后任务状态脑电信号的二分类。此外，采用多维条件互信息方法提取脑电数据的频带特征，并将多频带组合下的耦合特征转换为多光谱图像。同时，引入Densenet的思想对多尺度卷积神经网络进行改进。首先，根据多光谱脑电图像特征的离散性，使用两尺度卷积核计算并学习多光谱图像数据中有用的通道和频带特征信息。其次，为增强特征传播并减少参数数量，在多尺度卷积网络之后连接密集网络，并优化随机梯度下降算法的学习率变化函数以客观评估训练效果。实验结果表明，与经典卷积神经网络（CNN）和多尺度卷积神经网络相比，所提出的MHCNN在六个频带组合中具有更好的分类性能，最高准确率达98%，这六个频带组合分别为：Theta-Alpha2-Gamma、Alpha2-Beta2-Gamma、Beta1-Beta2-Gamma、Theta-Beta2-Gamma、Theta-Alpha1-Gamma和Alpha1-Alpha2-Gamma。通过比较六个频带组合的分类结果，发现Theta-Beta2-Gamma频带组合的分类效果最佳。本研究提出的MHCNN分类方法可作为空间认知训练效果的有效生物学指标，并可扩展到其他脑功能评估中。
