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Rebeca Corralejo
Researcher at University of Valladolid
Publications - 15
Citations - 344
Rebeca Corralejo is an academic researcher from University of Valladolid. The author has contributed to research in topics: Motor imagery & Neurofeedback. The author has an hindex of 8, co-authored 15 publications receiving 280 citations.
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Journal ArticleDOI
Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain-computer interfaces
TL;DR: A semi-supervised classification algorithm whereby the model is gradually enhanced with unlabeled data collected online and a processing stage is introduced before classification to adaptively reduce the small fluctuations between the features from training and evaluation sessions.
Journal ArticleDOI
Adaptive Stacked Generalization for Multiclass Motor Imagery-Based Brain Computer Interfaces
TL;DR: This paper proposes a processing framework to address non-stationarity, as well as handle spectral, temporal, and spatial characteristics associated with execution of motor tasks, and demonstrates its effectiveness in binary and multiclass settings.
Journal ArticleDOI
A P300-based brain–computer interface aimed at operating electronic devices at home for severely disabled people
TL;DR: The results suggest that neither the type nor the degree of disability is a relevant issue to suitably operate a P300-based BCI and could be useful to assist disabled people at home improving their personal autonomy.
Journal ArticleDOI
Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly
TL;DR: Evidence is established in the association between NFT performed by a motor imagery-based BCI and enhanced cognitive performance and it could be a novel approach to help elderly people.
Proceedings ArticleDOI
Feature selection using a genetic algorithm in a motor imagery-based Brain Computer Interface
TL;DR: Preliminary results demonstrated that the proposed methodology could be useful to control motor imagery-based BCI applications and improve the classification results using extracted features separately.