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

• URL: http://arxiv.org/abs/2208.11743v1
• Date: Wed, 24 Aug 2022 19:10:11 GMT
• Title: EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis
• Authors: Guangyao Dou, Zheng Zhou
• Abstract summary: This paper explores machine learning algorithms that can run efficiently on personal computers for BCI classification tasks. We investigate how to conduct such BCI experiments using affordable consumer-grade devices to collect EEG-based BCI data. We have developed the data collection protocol, EEG4Students, that grants non-experts who are interested in a guideline for such data collection.
• Score: 3.8224226881450187
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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