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

arXiv: https://arxiv.org/abs/2208.11743 · HTML: https://mlsanity.com/paper/2208.11743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels](https://mlsanity.com/paper/2203.12454/) (2022)
- [BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading](https://mlsanity.com/paper/1905.06312/) (2019)
- [Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks](https://mlsanity.com/paper/2012.04479/) (2020)
- [Mental State Recognition via Wearable EEG](https://mlsanity.com/paper/1602.00985/) (2016)
- [Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading](https://mlsanity.com/paper/2010.15344/) (2020)
- [Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso](https://mlsanity.com/paper/2009.14310/) (2020)
- [A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis](https://mlsanity.com/paper/1806.10698/) (2018)
- [Intelligent Sight and Sound: A Chronic Cancer Pain Dataset](https://mlsanity.com/paper/2204.04214/) (2022)

## 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. 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.