Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation
Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/1/415 |
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| author | Vicente Quiles Laura Ferrero Eduardo Iáñez Mario Ortiz José M. Cano José M. Azorín |
| author_facet | Vicente Quiles Laura Ferrero Eduardo Iáñez Mario Ortiz José M. Cano José M. Azorín |
| author_sort | Vicente Quiles |
| collection | DOAJ |
| description | Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimum features and electrodes is proposed. This is carried out comparing three different classification models: monotonous walk vs. increasing and decreasing change speed intentions, monotonous walk vs. only increasing intention, and monotonous walk vs. only decreasing intention. The results indicate that, among the features tested, the most suitable parameter to represent these models are the Hjorth statistics in alpha and beta frequency bands. The average offline classification accuracy for the offline cross-validation of the three models obtained is 68 ± 11%. This selection is also tested following a pseudo-online analysis, simulating a real-time detection of the subject’s intentions to change speed. The average results indices of the three models during this pseudoanalysis are of a 42% true positive ratio and a false positive rate per minute of 9. Finally, in order to check the viability of the approach with an exoskeleton, a case of study is presented. During the experimental session, the pros and cons of the implementation of a closed-loop control of speed change for the H3 exoskeleton through EEG analysis are commented. |
| first_indexed | 2024-03-10T03:49:24Z |
| format | Article |
| id | doaj.art-89b62fe9e0b547eb92415e8e45b5cf77 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:49:24Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-89b62fe9e0b547eb92415e8e45b5cf772023-11-23T11:12:40ZengMDPI AGApplied Sciences2076-34172022-01-0112141510.3390/app12010415Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop ValidationVicente Quiles0Laura Ferrero1Eduardo Iáñez2Mario Ortiz3José M. Cano4José M. Azorín5Brain-Machine Interface System Lab, Miguel Hernández University of Elche, 03202 Elche, SpainBrain-Machine Interface System Lab, Miguel Hernández University of Elche, 03202 Elche, SpainBrain-Machine Interface System Lab, Miguel Hernández University of Elche, 03202 Elche, SpainBrain-Machine Interface System Lab, Miguel Hernández University of Elche, 03202 Elche, SpainSystems and Automation Engineering Department, Technical University of Cartagena, 30202 Cartagena, SpainBrain-Machine Interface System Lab, Miguel Hernández University of Elche, 03202 Elche, SpainControl of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimum features and electrodes is proposed. This is carried out comparing three different classification models: monotonous walk vs. increasing and decreasing change speed intentions, monotonous walk vs. only increasing intention, and monotonous walk vs. only decreasing intention. The results indicate that, among the features tested, the most suitable parameter to represent these models are the Hjorth statistics in alpha and beta frequency bands. The average offline classification accuracy for the offline cross-validation of the three models obtained is 68 ± 11%. This selection is also tested following a pseudo-online analysis, simulating a real-time detection of the subject’s intentions to change speed. The average results indices of the three models during this pseudoanalysis are of a 42% true positive ratio and a false positive rate per minute of 9. Finally, in order to check the viability of the approach with an exoskeleton, a case of study is presented. During the experimental session, the pros and cons of the implementation of a closed-loop control of speed change for the H3 exoskeleton through EEG analysis are commented.https://www.mdpi.com/2076-3417/12/1/415exoskeletonbrain–machine interfaceelectroencefalographycevent related (de)syncronization |
| spellingShingle | Vicente Quiles Laura Ferrero Eduardo Iáñez Mario Ortiz José M. Cano José M. Azorín Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation Applied Sciences exoskeleton brain–machine interface electroencefalographyc event related (de)syncronization |
| title | Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation |
| title_full | Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation |
| title_fullStr | Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation |
| title_full_unstemmed | Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation |
| title_short | Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation |
| title_sort | detecting the speed change intention from eeg signals from the offline and pseudo online analysis to an online closed loop validation |
| topic | exoskeleton brain–machine interface electroencefalographyc event related (de)syncronization |
| url | https://www.mdpi.com/2076-3417/12/1/415 |
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