论文标题
使用EAR-EEG信号预测内存检索性能
Prediction of Memory Retrieval Performance Using Ear-EEG Signals
论文作者
论文摘要
许多研究在执行记忆任务期间探索了大脑信号，以预测后来的记忆项目。但是，预测方法在现实生活中仍然很差，并且由于使用了从头皮中记录的脑电图（EEG），因此不实用。由于将其应用于现实世界环境时，EAR-EEG由于其灵活性而被用于测量大脑信号。在这项研究中，我们试图预测使用EAR-EEG是否会记住或忘记显示的刺激，并将其与头皮EEG进行比较。我们的结果表明，EAR-EEG和头皮EEG之间没有显着差异。此外，使用卷积神经网络（刺激前：74.06％，持续刺激：69.53％）获得了较高的预测精度，并将其与其他基线方法进行了比较。这些结果表明，可以使用EAR-EEG信号来预测内存任务的性能，并且可以用于预测实用的大脑计算机接口中的内存检索。
Many studies have explored brain signals during the performance of a memory task to predict later remembered items. However, prediction methods are still poorly used in real life and are not practical due to the use of electroencephalography (EEG) recorded from the scalp. Ear-EEG has been recently used to measure brain signals due to its flexibility when applying it to real world environments. In this study, we attempt to predict whether a shown stimulus is going to be remembered or forgotten using ear-EEG and compared its performance with scalp-EEG. Our results showed that there was no significant difference between ear-EEG and scalp-EEG. In addition, the higher prediction accuracy was obtained using a convolutional neural network (pre-stimulus: 74.06%, on-going stimulus: 69.53%) and it was compared to other baseline methods. These results showed that it is possible to predict performance of a memory task using ear-EEG signals and it could be used for predicting memory retrieval in a practical brain-computer interface.
