Applying Combined Action Observation and Motor Imagery to Enhance Classification Performance in a Brain–Computer Interface System for Stroke Patients
Motor imagery (MI) and action observation (AO) are mental practices commonly applied in brain–computer interface (BCI) systems for stroke rehabilitation. However, previous studies have reported that combined AO and MI (AOMI) is more effective than MI or AO alone in terms of enhanced event...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9828024/ |
| _version_ | 1811296097661878272 |
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| author | Nuttawat Rungsirisilp Yodchanan Wongsawat |
| author_facet | Nuttawat Rungsirisilp Yodchanan Wongsawat |
| author_sort | Nuttawat Rungsirisilp |
| collection | DOAJ |
| description | Motor imagery (MI) and action observation (AO) are mental practices commonly applied in brain–computer interface (BCI) systems for stroke rehabilitation. However, previous studies have reported that combined AO and MI (AOMI) is more effective than MI or AO alone in terms of enhanced event-related desynchronization (ERD), which expresses cortical excitability and improves the classification performance of the BCI system in healthy subjects. Nonetheless, evidence the use of this strategy in stroke patients is still lacking. Hence, this study aimed to investigate the effect of AOMI on ERD and classification performance in chronic stroke patients. Ten chronic stroke participants were recruited for this study. Each participant was asked to perform both MI (control condition) and AOMI (experimental condition) tasks. For the MI task, the participants requested to perform MI while gazing at a static arrow picture. For the AOMI task, the participants were given a video-guided movement while executing MI. An array of 16 Ag/AgCl electrodes were used to record electroencephalographic (EEG) data during the mental tasks to analyze ERD amplitudes. Common spatial patterns (CSPs) combined with support vector machines (SVMs) were employed to evaluate the classification performance (offline analysis) of the baseline and imagery classes under each condition. Our results indicated that the ERD values and classification accuracy in AOMI were significantly greater than those under MI conditions. Moreover, a significant negative correlation between ERD values and classification performance was also found. In other words, enhanced ERD values (more negative values) also increased classification performance. |
| first_indexed | 2024-04-13T05:43:17Z |
| format | Article |
| id | doaj.art-bae257c8b0f5417d82a4445192dec099 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T05:43:17Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bae257c8b0f5417d82a4445192dec0992022-12-22T03:00:02ZengIEEEIEEE Access2169-35362022-01-0110731457315510.1109/ACCESS.2022.31907989828024Applying Combined Action Observation and Motor Imagery to Enhance Classification Performance in a Brain–Computer Interface System for Stroke PatientsNuttawat Rungsirisilp0https://orcid.org/0000-0001-8695-7345Yodchanan Wongsawat1https://orcid.org/0000-0002-6541-0305Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, ThailandDepartment of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, ThailandMotor imagery (MI) and action observation (AO) are mental practices commonly applied in brain–computer interface (BCI) systems for stroke rehabilitation. However, previous studies have reported that combined AO and MI (AOMI) is more effective than MI or AO alone in terms of enhanced event-related desynchronization (ERD), which expresses cortical excitability and improves the classification performance of the BCI system in healthy subjects. Nonetheless, evidence the use of this strategy in stroke patients is still lacking. Hence, this study aimed to investigate the effect of AOMI on ERD and classification performance in chronic stroke patients. Ten chronic stroke participants were recruited for this study. Each participant was asked to perform both MI (control condition) and AOMI (experimental condition) tasks. For the MI task, the participants requested to perform MI while gazing at a static arrow picture. For the AOMI task, the participants were given a video-guided movement while executing MI. An array of 16 Ag/AgCl electrodes were used to record electroencephalographic (EEG) data during the mental tasks to analyze ERD amplitudes. Common spatial patterns (CSPs) combined with support vector machines (SVMs) were employed to evaluate the classification performance (offline analysis) of the baseline and imagery classes under each condition. Our results indicated that the ERD values and classification accuracy in AOMI were significantly greater than those under MI conditions. Moreover, a significant negative correlation between ERD values and classification performance was also found. In other words, enhanced ERD values (more negative values) also increased classification performance.https://ieeexplore.ieee.org/document/9828024/Brain-computer interfacemotor imageryaction observationstrokemachine learning |
| spellingShingle | Nuttawat Rungsirisilp Yodchanan Wongsawat Applying Combined Action Observation and Motor Imagery to Enhance Classification Performance in a Brain–Computer Interface System for Stroke Patients IEEE Access Brain-computer interface motor imagery action observation stroke machine learning |
| title | Applying Combined Action Observation and Motor Imagery to Enhance Classification Performance in a Brain–Computer Interface System for Stroke Patients |
| title_full | Applying Combined Action Observation and Motor Imagery to Enhance Classification Performance in a Brain–Computer Interface System for Stroke Patients |
| title_fullStr | Applying Combined Action Observation and Motor Imagery to Enhance Classification Performance in a Brain–Computer Interface System for Stroke Patients |
| title_full_unstemmed | Applying Combined Action Observation and Motor Imagery to Enhance Classification Performance in a Brain–Computer Interface System for Stroke Patients |
| title_short | Applying Combined Action Observation and Motor Imagery to Enhance Classification Performance in a Brain–Computer Interface System for Stroke Patients |
| title_sort | applying combined action observation and motor imagery to enhance classification performance in a brain x2013 computer interface system for stroke patients |
| topic | Brain-computer interface motor imagery action observation stroke machine learning |
| url | https://ieeexplore.ieee.org/document/9828024/ |
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