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High-wearable EEG-based transducer for engagement detection in pediatric rehabilitation

High-wearable EEG-based transducer for engagement detection in pediatric rehabilitation A method for high-wearable EEG-based assessment of pediatric emotional and cognitive engagement in neuro-motor rehabilitation is proposed. A specific easy calibration is provided in the perspective of a personalized medicine. Due to the lack of studies evaluating pediatric multidimensional engagement, an observational non-interventional protocol was adopted for collecting the EEG data related to the high/low levels of engagement. The experimental validation of the proposed method involved four children performing a rehabilitation exercise in five 8-min sessions. Due to the age and frailty of the subjects, no negative emotions were expressly induced and an unbalanced dataset was obtained. Different Synthetic Minority Oversampling Technique (SMOTE)-based strategies for unbalanced dataset management and classification methods were compared. The highest performances were achieved by combining Artificial Neural Network (ANN) models with the KMeansSMOTE oversampling method. Balanced accuracies of 71.2 % and 74.5 % for the emotional engagement and the cognitive engagement are obtained, respectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

High-wearable EEG-based transducer for engagement detection in pediatric rehabilitation

High-wearable EEG-based transducer for engagement detection in pediatric rehabilitation

Abstract

A method for high-wearable EEG-based assessment of pediatric emotional and cognitive engagement in neuro-motor rehabilitation is proposed. A specific easy calibration is provided in the perspective of a personalized medicine. Due to the lack of studies evaluating pediatric multidimensional engagement, an observational non-interventional protocol was adopted for collecting the EEG data related to the high/low levels of engagement. The experimental validation of the proposed method involved...
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Publisher
Taylor & Francis
Copyright
© 2021 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2021.2015149
Publisher site
See Article on Publisher Site

Abstract

A method for high-wearable EEG-based assessment of pediatric emotional and cognitive engagement in neuro-motor rehabilitation is proposed. A specific easy calibration is provided in the perspective of a personalized medicine. Due to the lack of studies evaluating pediatric multidimensional engagement, an observational non-interventional protocol was adopted for collecting the EEG data related to the high/low levels of engagement. The experimental validation of the proposed method involved four children performing a rehabilitation exercise in five 8-min sessions. Due to the age and frailty of the subjects, no negative emotions were expressly induced and an unbalanced dataset was obtained. Different Synthetic Minority Oversampling Technique (SMOTE)-based strategies for unbalanced dataset management and classification methods were compared. The highest performances were achieved by combining Artificial Neural Network (ANN) models with the KMeansSMOTE oversampling method. Balanced accuracies of 71.2 % and 74.5 % for the emotional engagement and the cognitive engagement are obtained, respectively.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Jul 3, 2022

Keywords: Engagement assessment; Pediatric automated rehabilitation system; EEG; unbalanced data

References