EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis

TL;DR

This paper presents EEG4Students, a practical protocol for collecting EEG data using affordable devices and machine learning algorithms, addressing challenges of remote data collection during the COVID-19 pandemic.

Contribution

It introduces a new data collection protocol and demonstrates effective machine learning methods for EEG classification with consumer-grade devices.

Findings

Random Forest and RBF SVM perform well for EEG classification

The protocol enables non-experts to collect EEG data remotely

Affordable devices can be effectively used for BCI data collection

Abstract

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.