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

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HCI International 2022 – Late Breaking Posters (HCII 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1654))

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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.

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Authors and Affiliations

  1. Brandeis University, Waltham, MA, 02453, USA

    Zheng Zhou, Guangyao Dou & Xiaodong Qu

  2. Swarthmore College, Swarthmore, PA, 19081, USA

    Xiaodong Qu

Authors
  1. Zheng Zhou
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  2. Guangyao Dou
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  3. Xiaodong Qu
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Corresponding author

Correspondence to Zheng Zhou .

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Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Foundation for Research and Technology Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  3. Foundation for Research and Technology Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

  4. University of Central Florida, Orlando, FL, USA

    Gavriel Salvendy

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Zhou, Z., Dou, G., Qu, X. (2022). BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1654. Springer, Cham. https://doi.org/10.1007/978-3-031-19679-9_16

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  • DOI: https://doi.org/10.1007/978-3-031-19679-9_16

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