论文标题
EEG4学生：脑电图数据收集和机器学习分析的实验设计
EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis
论文作者
论文摘要
使用机器学习和深度学习来预测脑电图（EEG）信号的认知任务，一直是脑部计算机界面（BCI）的快速发展领域。然而，在Covid-19大流行期间，数据收集和分析可能更具挑战性。大流行期间的远程实验会带来一些挑战，我们讨论了可能的解决方案。本文探讨了可以在BCI分类任务上有效运行的机器学习算法。结果表明，随机森林和RBF SVM在脑电图分类任务方面表现良好。此外，我们研究了如何使用负担得起的消费级设备进行此类BCI实验以收集基于脑电图的BCI数据。此外，我们开发了数据收集协议EEG4-Students，该协议授予对此类数据收集指南感兴趣的非专家。我们的代码和数据可以在https://github.com/guangyaodou/eeg4students上找到。
Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals has been a fast-developing area in Brain-Computer Interfaces (BCI). However, during the COVID-19 pandemic, data collection and analysis could be more challenging. The remote experiment during the pandemic yields several challenges, and we discuss the possible solutions. This paper explores machine learning algorithms that can run efficiently on personal computers for BCI classification tasks. The results show that Random Forest and RBF SVM perform well for EEG classification tasks. Furthermore, we investigate how to conduct such BCI experiments using affordable consumer-grade devices to collect EEG-based BCI data. In addition, we have developed the data collection protocol, EEG4Students, that grants non-experts who are interested in a guideline for such data collection. Our code and data can be found at https://github.com/GuangyaoDou/EEG4Students.
