Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns
Assessment of brain dynamics elicited by motor imagery (MI) tasks contributes to clinical and learning applications. In this regard, Event-Related Desynchronization/Synchronization (ERD/S) is computed from Electroencephalographic signals, which show considerable variations in complexity. We present...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/6/703 |
| _version_ | 1797564262032343040 |
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| author | Luisa Velasquez-Martinez Julián Caicedo-Acosta Germán Castellanos-Dominguez |
| author_facet | Luisa Velasquez-Martinez Julián Caicedo-Acosta Germán Castellanos-Dominguez |
| author_sort | Luisa Velasquez-Martinez |
| collection | DOAJ |
| description | Assessment of brain dynamics elicited by motor imagery (MI) tasks contributes to clinical and learning applications. In this regard, Event-Related Desynchronization/Synchronization (ERD/S) is computed from Electroencephalographic signals, which show considerable variations in complexity. We present an Entropy-based method, termed <i>VQEnt</i>, for estimation of ERD/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that <i>VQEnt</i> holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the <i>VQEnt</i> estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set. |
| first_indexed | 2024-03-10T18:54:53Z |
| format | Article |
| id | doaj.art-043f8dbec3574a398dec72db0f3dcd3b |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T18:54:53Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-043f8dbec3574a398dec72db0f3dcd3b2023-11-20T04:49:21ZengMDPI AGEntropy1099-43002020-06-0122670310.3390/e22060703Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized PatternsLuisa Velasquez-Martinez0Julián Caicedo-Acosta1Germán Castellanos-Dominguez2Signal 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, ColombiaAssessment of brain dynamics elicited by motor imagery (MI) tasks contributes to clinical and learning applications. In this regard, Event-Related Desynchronization/Synchronization (ERD/S) is computed from Electroencephalographic signals, which show considerable variations in complexity. We present an Entropy-based method, termed <i>VQEnt</i>, for estimation of ERD/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that <i>VQEnt</i> holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the <i>VQEnt</i> estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set.https://www.mdpi.com/1099-4300/22/6/703event-related de/synchronizationentropymotor imageryvector quantization |
| spellingShingle | Luisa Velasquez-Martinez Julián Caicedo-Acosta Germán Castellanos-Dominguez Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns Entropy event-related de/synchronization entropy motor imagery vector quantization |
| title | Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns |
| title_full | Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns |
| title_fullStr | Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns |
| title_full_unstemmed | Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns |
| title_short | Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns |
| title_sort | entropy based estimation of event related de synchronization in motor imagery using vector quantized patterns |
| topic | event-related de/synchronization entropy motor imagery vector quantization |
| url | https://www.mdpi.com/1099-4300/22/6/703 |
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