基于CNN-LSTM的脑电P300信号检测

A P300 signal detection algorithm based on CNN and LSTM

为提高对无创脑机接口(BCI)中P300脑电信号的检测准确度,本文根据卷积神经网络(CNN)与长短期记忆(LSTM)网络,提出一种CNN-LSTM组合网络模型。卷积网络采取分层结构,同时设计匹配不同特征维度的一维卷积核;长短期记忆网络(LSTM)用来发掘数据时序相互依赖性,学习全局特征的相关性以实现目标分类。试验结果表明,本文提出的模型对于实验诱发出的单试次P300信号,检测准确率达到91.28%,与EEGNet网络和支持向量机算法对比,准确率分别提升2.18%、8.31%。在精确率、召回率、F1分数、AUC值的评价指标下也达到最优性能,具有较强的泛化性能。

In order to improve the detection accuracy of P300 EEG signals in non-invasive brain-computer interface(BCI) system, this paper proposes a CNN-LSTM combined network model based on convolutional neural network(CNN) and long short-term memory(LSTM) network. The convolutional network adopts a hierarchical structure, and designs a one-dimensional convolution kernel that matches different feature dimensions;long short-term memory network(LSTM) is used to explore the interdependence of data time series, learning Correlation of global features for object classification. The test results show that the model proposed in this paper has a detection accuracy of 91.28% for the single-trial P300 signal induced by the experiment. Compared with the EEGNet network and the support vector machine(SVM) algorithm, the accuracy is increased by 2.18% and 8.31%, respectively. It also achieves the optimal performance under the evaluation indicators of Precision, Recall, F1 score and AUC value, and has strong generalization performance.