EventsThe 1st International Online Conference on Bioengineering
Published
This submission belongs to the session 5. Biosignal Processing of the event The 1st International Online Conference on Bioengineering
Published date
11 Oct, 2024
Academic Editor
author-avatarAndrea Cataldo
Citation
Maxim Bakaev, Anton Gerasimov, Alexey Kozin, Anton Pashkov, Olga Razumnikova, Effect of Individual Abilities for Mental 3D Rotation in Learning EEG-Controlled BCIs, in Proceedings of The 1st International Online Conference on Bioengineering, 16 October–18 October 2024, MDPI: Basel, Switzerland
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Effect of Individual Abilities for Mental 3D Rotation in Learning EEG-Controlled BCIs

Alexey Kozin 1
image
1. Novosibirsk State Technical University, Russia, Russia
2. Federal Centre for Neurosurgery, Novosibirsk, Russia
Abstract

Introduction:

Brain--computer interface (BCI) systems aim to transmit control commands generated through brain activity to computer applications. Mastery of BCIs generally requires special training, in which some of the users (the so-called BCI-illiterates) might be lacking. Our research paper examines the relation between the users’ individual mental rotation abilities and the effort associated with mastering motor-imagery-based BCI systems.

Methods:

The mental rotation abilities in the test subjects were assessed with a dedicated test that complies with the paradigm developed by Vandenberg and Kuse. The number of correct answers on 3D figure rotations provided during the 90 seconds allocated for the test was recorded. Subsequently, the subjects were asked to control a robotic claw arm to transfer cubes from one place to another, within the motor imagination BCI paradigm. To reflect the BCI learning effort and effectiveness, the number of cubes transferred and the average time to transfer one cube were recorded.

Results and Discussion:

We report on the positive relation between the individual mental 3D rotation abilities and the mastery of motor imagery BCI system. Then we put forward and discuss the ensuing recommendations for organizing effective and time-efficient training for this BCI-illiterate category of users. Particularly, we propose using a brain model topographic map of the subjects’ EEG to visualize the feedback during the training process.

Conclusions:

BCI illiteracy is one of the biggest challenges for the wider adoption of effective BCI systems that have the potential to improve life quality and experience for many users. The reported relation is a step forward towards both understanding the causes of this phenomenon and the design of successful training programs. One way to mitigate BCI illiteracy might be through 3D figure mental rotation training, but this necessitates further research.

Keywords
Human-Computer Interaction
Motor Imagery BCI
EEG Signals
User Training
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