@inproceedings{cf28f1781d0146de9b2b68965560132f,
title = "Prediction of Inefficient BCI Users Based on Cognitive Skills and Personality Traits",
abstract = "BCI inefficiency is one of the major challenges of Motor Imagery Brain-Computer Interfaces (MI-BCI). Past research suggests that certain cognitive skills and personality traits correlate with MI-BCI real-time performance. Other studies have examined sensorimotor rhythm changes (known as μ suppression) as a valuable indicator of successful execution of the motor imagery task. This research aims to combine these insights by investigating whether cognitive factors and personality traits can predict a user{\textquoteright}s ability to modulate μ rhythms during a MI-BCI task. Data containing 55 subjects who completed a MI task was employed, and a stepwise linear regression model was implemented to select the most relevant features for μ suppression prediction. The most accurate model was based on: Spatial Ability, Visuospatial Memory, Autonomy, and Vividness of Visual Imagery. Further correlation analyses showed that a novice user{\textquoteright}s μ suppression during a MI-BCI task can be predicted based on their visuospatial memory, as measured by the Design Organization Test (DOT).",
author = "Hagedorn, {Laura J.} and Nikki Leeuwis and Maryam Alimardani",
year = "2021",
doi = "10.1007/978-3-030-92310-5_10",
language = "English",
isbn = "9783030923099",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "81--89",
editor = "T. Mantoro and M. Lee and M.A. Ayu and K.W. Wong and A.N. Hidayanto",
booktitle = "Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings",
note = "28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",
}