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

arXiv: https://arxiv.org/abs/2207.13239 · HTML: https://mlsanity.com/paper/2207.13239/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2022.

## Built on by (most cited first, 6 of 6)

- [Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers](https://mlsanity.com/paper/2307.02819/) (2023)
- [Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression](https://mlsanity.com/paper/2404.15311/) (2024)
- [AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection](https://mlsanity.com/paper/2405.03075/) (2024)
- [EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures](https://mlsanity.com/paper/2408.03449/) (2024)
- [Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study](https://mlsanity.com/paper/2408.03472/) (2024)
- [Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data](https://mlsanity.com/paper/2408.03478/) (2024)

## Abstract (arXiv)

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.