论文标题
Brainactivity1：脑电图数据收集和机器学习分析的框架
BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students
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比以前更具挑战性。本文探索了机器学习算法，这些算法可以在个人计算机上进行BCI分类任务有效运行。此外，我们还研究了一种通过变焦进行远程进行此类BCI实验的方法。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。大流行期间的远程实验提出了一些挑战，我们讨论了可能的解决方案。尽管如此，我们开发了一项协议，该协议授予对此类数据收集指南感兴趣的非专家。
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 than before. This paper explored machine learning algorithms that can run efficiently on personal computers for BCI classification tasks. Also, we investigated a way to conduct such BCI experiments remotely via Zoom. The results showed that Random Forest and RBF SVM performed well for EEG classification tasks. The remote experiment during the pandemic yielded several challenges, and we discussed the possible solutions; nevertheless, we developed a protocol that grants non-experts who are interested a guideline for such data collection.
