BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students

TL;DR

This paper presents a framework for EEG data collection and machine learning analysis tailored for college students, emphasizing remote data collection during COVID-19 and evaluating efficient algorithms like Random Forest and RBF SVM.

Contribution

It introduces a practical protocol for remote EEG data collection and analysis suitable for non-experts, addressing pandemic-related challenges in BCI research.

Findings

Random Forest and RBF SVM achieved high accuracy in EEG classification

A feasible remote EEG data collection protocol was developed during COVID-19

Challenges of remote EEG experiments were identified and discussed

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 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.