Convolutional Neural Network Transfer Learning Applied to the Affective Auditory P300-Based BCI
Convolutional Neural Network Transfer Learning Applied to the Affective Auditory P300-Based BCI
复制标题
DOI：
10.20965/jrm.2020.p0731
复制
发表时间：
2020-08
期刊：
影响因子：
--
通讯作者：
Akinari Onishi
中科院分区：
文献类型：
--
作者：
Akinari Onishi
脑机接口（BCI）使我们能够通过脑电信号（EEG）与外部世界进行交互。最近，深度学习方法已应用于BCI，以减少记录训练数据所需的时间。然而，由于缺乏比较，需要更多的证据。为了揭示更多的证据，这项研究提出了一种名为时间卷积神经网络（TWCNN）的深度学习方法，并将其应用于BCI数据集。在评价中，使用先前记录的其他受试者的EEG数据对受试者的EEG数据进行分类。结果，TWCNN显示出最高的准确率，显著高于通常使用的分类器。结果表明，深度学习方法可能有助于减少训练数据的记录时间。
Brain-computer interface (BCI) enables us to interact with the external world via electroencephalography (EEG) signals. Recently, deep learning methods have been applied to the BCI to reduce the time required for recording training data. However, more evidence is required due to lack of comparison. To reveal more evidence, this study proposed a deep learning method named time-wise convolutional neural network (TWCNN), which was applied to a BCI dataset. In the evaluation, EEG data from a subject was classified utilizing previously recorded EEG data from other subjects. As a result, TWCNN showed the highest accuracy, which was significantly higher than the typically used classifier. The results suggest that the deep learning method may be useful to reduce the recording time of training data.