Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks
Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we de...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/10/10/707 |
| _version_ | 1797551746930704384 |
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| author | Luisa Velasquez-Martinez Julian Caicedo-Acosta Carlos Acosta-Medina Andres Alvarez-Meza German Castellanos-Dominguez |
| author_facet | Luisa Velasquez-Martinez Julian Caicedo-Acosta Carlos Acosta-Medina Andres Alvarez-Meza German Castellanos-Dominguez |
| author_sort | Luisa Velasquez-Martinez |
| collection | DOAJ |
| description | Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects. |
| first_indexed | 2024-03-10T15:50:25Z |
| format | Article |
| id | doaj.art-23719c172ddf4032ab00c14ccd9dcd5c |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T15:50:25Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-23719c172ddf4032ab00c14ccd9dcd5c2023-11-20T16:03:54ZengMDPI AGBrain Sciences2076-34252020-10-01101070710.3390/brainsci10100707Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery TasksLuisa Velasquez-Martinez0Julian Caicedo-Acosta1Carlos Acosta-Medina2Andres Alvarez-Meza3German Castellanos-Dominguez4Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170004, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170004, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170004, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170004, ColombiaSignal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales 170004, ColombiaMotor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects.https://www.mdpi.com/2076-3425/10/10/707sensorimotor rhythmevent-related de/synchronizationbrain-computer inefficiencyregression networks |
| spellingShingle | Luisa Velasquez-Martinez Julian Caicedo-Acosta Carlos Acosta-Medina Andres Alvarez-Meza German Castellanos-Dominguez Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks Brain Sciences sensorimotor rhythm event-related de/synchronization brain-computer inefficiency regression networks |
| title | Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks |
| title_full | Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks |
| title_fullStr | Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks |
| title_full_unstemmed | Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks |
| title_short | Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks |
| title_sort | regression networks for neurophysiological indicator evaluation in practicing motor imagery tasks |
| topic | sensorimotor rhythm event-related de/synchronization brain-computer inefficiency regression networks |
| url | https://www.mdpi.com/2076-3425/10/10/707 |
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