A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface.
A brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to se...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0218181 |
| _version_ | 1819144274850086912 |
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| author | Asier Salazar-Ramirez Jose I Martin Raquel Martinez Andoni Arruti Javier Muguerza Basilio Sierra |
| author_facet | Asier Salazar-Ramirez Jose I Martin Raquel Martinez Andoni Arruti Javier Muguerza Basilio Sierra |
| author_sort | Asier Salazar-Ramirez |
| collection | DOAJ |
| description | A brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to send some message to a certain device. This study presents a proposal, based on a hierarchical structured system, for recognising intentional and non-intentional mental tasks on a BCI system by applying machine learning techniques to the EEG signals. First-level clustering is performed to distinguish between intentional control (IC) and non-intentional control (NC) state patterns. Then, the patterns recognised as IC are passed on to a second stage where supervised learning techniques are used to classify them. In BCI applications, it is critical to correctly classify NC states with a low false positive rate (FPR) to avoid undesirable effects. According to the literature, we selected a maximum FPR of 10%. Under these conditions, our proposal achieved an average test accuracy of 66.6%, with an 8.2% FPR, for the BCI competition IIIa dataset. The main contribution of this paper is the hierarchical approach, based on machine learning paradigms, which performs intentional and non-intentional discrimination and, depending on the case, classifies the intended command selected by the user. |
| first_indexed | 2024-12-22T12:39:32Z |
| format | Article |
| id | doaj.art-74250056a061433092b1ab80e2a48639 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T12:39:32Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-74250056a061433092b1ab80e2a486392022-12-21T18:25:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01146e021818110.1371/journal.pone.0218181A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface.Asier Salazar-RamirezJose I MartinRaquel MartinezAndoni ArrutiJavier MuguerzaBasilio SierraA brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to send some message to a certain device. This study presents a proposal, based on a hierarchical structured system, for recognising intentional and non-intentional mental tasks on a BCI system by applying machine learning techniques to the EEG signals. First-level clustering is performed to distinguish between intentional control (IC) and non-intentional control (NC) state patterns. Then, the patterns recognised as IC are passed on to a second stage where supervised learning techniques are used to classify them. In BCI applications, it is critical to correctly classify NC states with a low false positive rate (FPR) to avoid undesirable effects. According to the literature, we selected a maximum FPR of 10%. Under these conditions, our proposal achieved an average test accuracy of 66.6%, with an 8.2% FPR, for the BCI competition IIIa dataset. The main contribution of this paper is the hierarchical approach, based on machine learning paradigms, which performs intentional and non-intentional discrimination and, depending on the case, classifies the intended command selected by the user.https://doi.org/10.1371/journal.pone.0218181 |
| spellingShingle | Asier Salazar-Ramirez Jose I Martin Raquel Martinez Andoni Arruti Javier Muguerza Basilio Sierra A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface. PLoS ONE |
| title | A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface. |
| title_full | A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface. |
| title_fullStr | A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface. |
| title_full_unstemmed | A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface. |
| title_short | A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface. |
| title_sort | hierarchical architecture for recognising intentionality in mental tasks on a brain computer interface |
| url | https://doi.org/10.1371/journal.pone.0218181 |
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