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Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces.
Leeuwis, Nikki; Paas, Alissa; Alimardani, Maryam.
Afiliação
  • Leeuwis N; Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands.
  • Paas A; Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands.
  • Alimardani M; Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands.
Front Hum Neurosci ; 15: 634748, 2021.
Article em En | MEDLINE | ID: mdl-33889080
ABSTRACT
Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users' mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Hum Neurosci Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Holanda

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Hum Neurosci Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Holanda