Du Pu, Li Penghai, Cheng Longlong, Li Xueqing, Su Jianxian
School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin, China.
China Electronics Cloud Brain Technology Co., Ltd., Tianjin, China.
Front Neurosci. 2023 Feb 22;17:1132290. doi: 10.3389/fnins.2023.1132290. eCollection 2023.
Currently, it is still a challenge to detect single-trial P300 from electroencephalography (EEG) signals. In this paper, to address the typical problems faced by existing single-trial P300 classification, such as complex, time-consuming and low accuracy processes, a single-trial P300 classification algorithm based on multiplayer data fusion convolutional neural network (CNN) is proposed to construct a centralized collaborative brain-computer interfaces (cBCI) for fast and highly accurate classification of P300 EEG signals.
In this paper, two multi-person data fusion methods (parallel data fusion and serial data fusion) are used in the data pre-processing stage to fuse multi-person EEG information stimulated by the same task instructions, and then the fused data is fed as input to the CNN for classification. In building the CNN network for single-trial P300 classification, the Conv layer was first used to extract the features of single-trial P300, and then the Maxpooling layer was used to connect the Flatten layer for secondary feature extraction and dimensionality reduction, thereby simplifying the computation. Finally batch normalisation is used to train small batches of data in order to better generalize the network and speed up single-trial P300 signal classification.
In this paper, the above new algorithms were tested on the Kaggle dataset and the Brain-Computer Interface (BCI) Competition III dataset, and by analyzing the P300 waveform features and EEG topography and the four standard evaluation metrics, namely Accuracy, Precision, Recall and F1-score,it was demonstrated that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed other classification algorithms.
The results show that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed the single-person model, and that the single-trial P300 classification algorithm with two multi-person data fusion CNNs involves smaller models, fewer training parameters, higher classification accuracy and improves the overall P300-cBCI classification rate and actual performance more effectively with a small amount of sample information compared to other algorithms.
目前，从脑电图（EEG）信号中检测单次试验P300仍然是一项挑战。本文针对现有单次试验P300分类面临的典型问题，如过程复杂、耗时且准确率低，提出了一种基于多数据融合卷积神经网络（CNN）的单次试验P300分类算法，以构建用于快速、高精度分类P300脑电信号的集中协作式脑机接口（cBCI）。
本文在数据预处理阶段采用两种多人数据融合方法（并行数据融合和串行数据融合），融合由相同任务指令刺激产生的多个人的脑电信息，然后将融合后的数据作为输入馈入CNN进行分类。在构建用于单次试验P300分类的CNN网络时，首先使用卷积层提取单次试验P300的特征，然后使用最大池化层连接展平层进行二次特征提取和降维，从而简化计算。最后使用批量归一化来训练小批量数据，以便更好地泛化网络并加快单次试验P300信号分类。
本文在Kaggle数据集和脑机接口（BCI）竞赛III数据集上对上述新算法进行了测试，并通过分析P300波形特征、脑电地形图以及四个标准评估指标，即准确率、精确率、召回率和F1分数，证明了经过两次多人数据融合的CNN后的单次试验P300分类算法明显优于其他分类算法。
结果表明，经过两次多人数据融合的CNN后的单次试验P300分类算法明显优于单人模型，并且与其他算法相比，具有两次多人数据融合的CNN的单次试验P300分类算法涉及的模型更小、训练参数更少、分类准确率更高，并且能够利用少量样本信息更有效地提高整体P300-cBCI分类率和实际性能。
