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Reading List: Brain Computer Interfaces for Education

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Edited by: Leah Friedman

In the following reading list, you’ll find popular science and academic articles that provide overviews of Brain Computer Interfaces (BCI) for education.

Included under each article is a brief bulleted overview of what each article covers and how much technical background each article requires.

Last updated: 6/16/2021

Overview

Brain Computer Interface in Enhanced Learning System
Resource Type: Academic Primer
Citation: Zaharija, G., Bogunović, P., & Mladenović, S. (2018) Brain Computer Interface in Enhanced Learning System. INTED2018 Proceedings, 198-205. DOI: 10.21125/inted.2018.1029
Background Needed: Low
Covers:

  • High level components of BCI
  • High level overview of some BCI applications
  • High level overview of EEG for BCI

Brain Computer Interfaces for Education
Resource Type: Popular Science Article written by a BCI Company
Citation: Impulse Neiry (2020, Jul 22). Brain Computer Interfaces for Education. Medium. https://medium.com/impulse-neiry/brain-computer-interfaces-for-education-afa3bccf606d
Background Needed: None
Covers:

  • Adaptive and personalized learning
  • Basic EEG technique for accessing and assessing cognition
  • Very basic machine learning for BCI
  • How EEG measures apply to education

Brain-computer interfaces and education: the state of technology and imperatives for the future
Resource Type: Academic Article – Literature Review and Field Overview
Citation: Wegemer, C. (2019). Brain-computer interfaces and education: the state of technology and imperatives for the future. International Journal of Learning Technology. 141(14) 10.1504/IJLT.2019.101848.
Background Needed: Medium
Covers:

  • Describes the state of BCI technology
  • Presents current learning science research that utilises BCI’s
  • Discusses the potential of BCI technology for educational applications
  • Summarizes historical relationship between education technology and academic outcomes
  • Draws relevant parallels to offer suggestions for researchers and policymakers

Academic Articles

The following are some samples of emerging research on using brain assessment to augment/complement/assess students and learning. The selection of articles also covers a few different tools being used in this area such as EEG and NIRS. Open sources articles are listed first.

Brain Computer Interfaces for Educational Applications
Citation: Spüler, M., Krumpe, T., Walter, C., Scharinger, C., Rosenstiel, W., & Gerjets, P. (2017). Brain-Computer Interfaces for Educational Applications. 10.1007/978-3-319-64274-1_8.
Background Needed: High
Covers:

  • Overview of research that aims to identify cognitive workload of learners
  • Description of study that aims to assess and predict workload with electroencephalography during arithmetic exercises

Put your thinking cap on: detecting cognitive load using EEG during learning
Citation: Mills, C., Fridman, I., Soussou, W., Waghray, D., Olney, A.M., & D’Mello, S.K. (2017) Put your thinking cap on: detecting cognitive load using EEG during learning. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (LAK ’17). Association for Computing Machinery, New York, NY, USA, 80–89. DOI:https://doi.org/10.1145/3027385.3027431
Background Needed: High
Covers:

  • Describes a study that uses electroencephalography (EEG) to assess cognitive load while using an intelligent tutoring system
  • Demonstrates viability of using EEG to model learners’ mental states

Predicting Student Performance Using Machine Learning in fNIRS Data
Citation: Oku, A., & Sato, J. R. (2021). Predicting Student Performance Using Machine Learning in fNIRS Data. Frontiers in human neuroscience, 15, 622224. https://doi.org/10.3389/fnhum.2021.622224
Background Needed: High
Covers:

  • Describes a study that uses functional Near Infrared Spectroscopy (fNIRS) to assess attention during a video lecture
  • Study also uses machine learning to predict lapses in attention during lecture

Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment
Citation: Walter, C., Rosenstiel, W., Bogdan, M., Gerjets, P., and Spüler, M. (2017). Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment. Front. Hum. Neurosci., 11. DOI=10.3389/fnhum.2017.00286
Background Needed: High
Covers:

  • Demonstrates closed-loop EEG that adapts learning material to improve success in learning arithmetic
  • Developed adaptive learning environment that adjust material based on brain state

Tracking Students’ Mental Engagement Using EEG Signals during an Interaction with a Vitrual Learning Environment
Citation: Khedher, A., Jraidi, I. and Frasson, C. (2019) Tracking Students’ Mental Engagement Using EEG Signals during an Interaction with a Virtual Learning Environment. Journal of Intelligent Learning Systems and Applications, 11, 1-14. doi: 10.4236/jilsa.2019.111001.
Background Needed: High
Covers:

  • Explores feasibility of using electroencephalographic signals (EEG) as a tool to monitor the mental engagement index of novice medicine students during a reasoning process

fNIRS-based classification of mind-wandering with personalized window selection for multimodal learning interfaces
Citation: Liu, R., Walker, E., Friedman, L. et al. (2020) fNIRS-based classification of mind-wandering with personalized window selection for multimodal learning interfaces. J Multimodal User Interfaces. https://doi.org/10.1007/s12193-020-00325-z
Background Needed: High
Note: this article is not open source
Covers:

  • Describes a study that uses functional Near Infrared Spectroscopy (fNIRS) to assess mind wandering (lapses in attention)
  • Study also uses machine learning to predict lapses in attention

NIRS-Based Language Learning BCI System
Citation: Watanabe, K., Tanaka, H., Takahasi, K., Niimura, Y., Watanabe, K., Kurihara, Y., (2016). NIRS-based Language Learning BCI System. IEEE Sensors Journal 16(8) doi: 10.1109/JSEN.2016.2519886
Background Needed: High
Note: this article is not open source
Covers:

  • Describes a study non-invasive near-infrared spectroscopy to assess language learning and listening

Additional Readings and Resources

The following resources provide different perspectives on and overviews of BCI for education. There is an article written by an academic, a teacher, and a news organization focused mostly on BCI for Education in China.

Brain Data, Neurotechnology, and Education
Resource Type: Academic blog post on educational BCI
Citation: Williamson, B. (2017, May 4). Brain data, neurotechnology and education [web log].https://codeactsineducation.wordpress.com/2017/05/04/brain-data-neurotechnology-and-education/.
Background Needed: Medium
Covers:

  • Brain Data: general BCI and brain-inspired R&D, especially in industry
    Ed-Neurotech: brief review of neurotechnology and education studies in academia & industry
  • Mentions government and policy interest in BCI research, including investment from DARPA
  • Neurotechnology Governance: brief review of emerging ethical and government guidelines

Learning Assessment Using Brain-Computer Interfaces: Are You Paying Attention?
Resource Type: Journal article on an industry website
Citation: Hamza-Lup, F. (2019, February 24). Learning Assessment Using Brain-Computer Interfaces: Are You Paying Attention? [web log].https://elearningindustry.com/brain-computer-interfaces-paying-attention-learning-assessment.
Background Needed: Low
Covers:

  • Brief overview of types Brain-Computer Interfaces and what they measure
  • Includes types of “brain data” that BCI’s often “read”

Brain Computer Interfaces, how does it connect with Education as we know it?
Resource Type: Blog post for a teacher about BCI for education
Citation: Kemp, C. (2014, August 29). Brain Computer Interfaces, how does it connect with Education as we know it? [web log].hhttp://mrkempnz.com/2014/08/brain-computer-interfaces-how-does-it-connect-with-education-as-we-know-it.html#:~:text=Brain%20Computer%20Interfaces%3F,they%20will%20apply%20those%20skills.
Background Needed: None
Covers:

  • Links to existing BCI work, not specific to education
  • Speculation about impacts in the classroom
  • Speculative tips on how teachers can prepare and adapt for BCI in the classroom

Could brain-machine interface help education?
Resource Type: News article on educational BCI implementation (primarily in China)
Citation: Khan, Q. (2019, November 7). Could Brain-Machine-Interface Help Education? EqualOcean. https://equalocean.com/news/2019110712186.
Background Needed: Middle
Covers:

  • BrainCo, an Educational BCI company in China
  • Public skepticism of BCI in China and abroad
  • Brief mention of data privacy concerns