IncepA-EEGNet: 融合Inception网络和注意力机制的P300信号检测方法

为了实现更高效的P300信号特征提取，提出融合Inception网络和注意力机制模块的卷积网络模型，即IncepA-EEGNet. 该模型使用不同感受野的卷积层进行并行连接，增强网络提取和表达脑电信号的能力. 引入注意力机制实现不同过滤器特征的权重分配，提取P300信号中的重要信息. 模型在BCI Competition III数据集II的2个受试者数据上进行验证. 与其他深度学习模型相比，IncepA-EEGNet的字符识别率在5个实验轮次后达到平均75.5%，在3个轮次后受试者B的信息传输速率达到33.44 bit/min. 实验结果表明，IncepA-EEGNet有效提高了P300信号的识别精度，减少了重复试验的时间，改善了P300拼写器的实用性.

IncepA-EEGNet: P300 signal detection method based on fusion of Inception network and attention mechanism

A novel EEGNet variation based on the fusion of the Inception and attention mechanism modules was proposed, called IncepA-EEGNet, in order to achieve more efficient P300 signal feature extraction. Convolutional layers with different receptive fields were connected in parallel. The network’s ability to extract and express EEG signals were enhanced. Then the attention mechanism was introduced to assign weights to the features of different filters, and important information was extracted from the P300 signal. The validation experiment was conducted on two subjects of BCI Competition III dataset II. Results showed that the IncepA-EEGNet recognition accuracy reached 75.5% after just 5 epochs compared with other deep learning models. The information transmission rate was up to 33.44 bits/min on subject B after 3 epochs. These experimental results demonstrate that the IncepA-EEGNet effectively improves the recognition accuracy of the P300 signal, reduces the time of repeated trials, and enhances the applicability of the P300 speller.