论文标题
使用大型多主体P300数据集评估卷积神经网络
Evaluation of convolutional neural networks using a large multi-subject P300 dataset
论文作者
论文摘要
深度神经网络（DNN）已在各个机器学习领域进行了研究。例如，事件相关电位（ERP）信号分类是一个高度复杂的任务，可能适合DNN，因为信噪比较低，并且基本的空间和时间模式显示出较大的内部和主体间可变性。卷积神经网络（CNN）已与基线传统模型，即线性判别分析（LDA）和支持向量机器（SVM）进行单次试验分类，使用大型多种受试者公开可用的P300 P300 P300数据集（138名男性和112名女性）。对于单个试验分类，对于所有测试的分类模型，分类准确性保持在62％至64％之间。将训练有素的分类模型应用于平均试验时，精度提高到76-79％，而分类模型之间没有显着差异。对于测试的数据集，CNN并未证明优于基线。讨论了与相关文献，局限性和未来方向的比较。
Deep neural networks (DNN) have been studied in various machine learning areas. For example, event-related potential (ERP) signal classification is a highly complex task potentially suitable for DNN as signal-to-noise ratio is low, and underlying spatial and temporal patterns display a large intra- and intersubject variability. Convolutional neural networks (CNN) have been compared with baseline traditional models, i.e. linear discriminant analysis (LDA) and support vector machines (SVM) for single trial classification using a large multi-subject publicly available P300 dataset of school-age children (138 males and 112 females). For single trial classification, classification accuracy stayed between 62% and 64% for all tested classification models. When applying the trained classification models to averaged trials, accuracy increased to 76-79% without significant differences among classification models. CNN did not prove superior to baseline for the tested dataset. Comparison with related literature, limitations and future directions are discussed.
