EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis
This provides a practical guideline for non-experts to collect and analyze EEG data using affordable devices, addressing remote experiment challenges during the pandemic, but it is incremental as it builds on existing methods.
The paper tackled the challenge of conducting EEG-based BCI experiments during the COVID-19 pandemic by developing a data collection protocol (EEG4Students) and evaluating machine learning algorithms for classification, finding that Random Forest and RBF SVM perform well on personal computers.
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.