Optimal Sensor Set for Decoding Motor Imagery from EEG

Arnau Dillen, Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Uros Marusic, Sidney Grosprêtre, Ann Nowe, Romain Meeusen, Kevin De Pauw

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Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with devices by detecting their intention from brain activity. A common approach to BCI is to decode movement intention from motor imagery (MI), the mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition devices with a high number of sensors are typically necessary to achieve the spatial resolution required for reliable analysis. This entails high monetary and computational costs that make these approaches impractical for everyday use. This study investigates the trade-off between accuracy and complexity when decoding MI from fewer EEG sensors. Data were acquired from 15 healthy participants performing MI with a 64-channel research-grade EEG device. After performing a quality assessment by identifying visually evoked potentials, several decoding pipelines were trained on these data using different subsets of electrode locations. No significant differences (p = [0.18–0.91]) in the average decoding accuracy were found when using a reduced number of sensors. Therefore, decoding MI from a limited number of sensors is feasible. Hence, using commercial sensor devices for this purpose should be attainable, reducing both monetary and computational costs for BCI control.

Original languageEnglish
Article number4438
Number of pages15
JournalApplied Sciences
Issue number7
Publication statusPublished - Apr 2023

Bibliographical note

Funding Information:
The authors would like to thank the people who participated in the data-gathering experiments and the students who assisted in the execution of these experiments. This research was made possible thanks to the EUTOPIA Ph.D. co-tutelle program and the Strategic Research Program Exercise and the Brain in Health and Disease: The Added Value of Human-Centered Robotics. UM gratefully acknowledges funding from the European Union’s Horizon 2020 Research and Innovation Program under grant agreement no. 952401 (TwinBrain—TWINning the BRAIN with machine learning for neuro-muscular efficiency).

Publisher Copyright:
© 2023 by the authors.

Copyright 2023 Elsevier B.V., All rights reserved.


  • brain-computer interface
  • Machine Learning
  • Feature selection
  • Experimental Study
  • Motor imagery
  • Electroencephalography


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