Language Model Applications to Spelling with Brain-Computer Interfaces
Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/14/4/5967 |
| _version_ | 1818000927261458432 |
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| author | Anderson Mora-Cortes Nikolay V. Manyakov Nikolay Chumerin Marc M. Van Hulle |
| author_facet | Anderson Mora-Cortes Nikolay V. Manyakov Nikolay Chumerin Marc M. Van Hulle |
| author_sort | Anderson Mora-Cortes |
| collection | DOAJ |
| description | Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models applied to them. These language models are classified according to their functionality in the context of BCI-based spelling: the static/dynamic nature of the user interface, the use of error correction and predictive spelling, and the potential to improve their classification performance by using language models. To conclude, the review offers an overview of the advantages and challenges when implementing language models in BCI-based communication systems when implemented in conjunction with other AAL technologies. |
| first_indexed | 2024-04-14T03:28:12Z |
| format | Article |
| id | doaj.art-7ed397b89d384f2793f002b29555d373 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T03:28:12Z |
| publishDate | 2014-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7ed397b89d384f2793f002b29555d3732022-12-22T02:15:05ZengMDPI AGSensors1424-82202014-03-011445967599310.3390/s140405967s140405967Language Model Applications to Spelling with Brain-Computer InterfacesAnderson Mora-Cortes0Nikolay V. Manyakov1Nikolay Chumerin2Marc M. Van Hulle3Laboratorium voor Neuro- en Psychofysiologie, KU Leuven, Campus Gasthuisberg, O&N2, Herestraat 49, Leuven B-3000, BelgiumLaboratorium voor Neuro- en Psychofysiologie, KU Leuven, Campus Gasthuisberg, O&N2, Herestraat 49, Leuven B-3000, BelgiumLaboratorium voor Neuro- en Psychofysiologie, KU Leuven, Campus Gasthuisberg, O&N2, Herestraat 49, Leuven B-3000, BelgiumLaboratorium voor Neuro- en Psychofysiologie, KU Leuven, Campus Gasthuisberg, O&N2, Herestraat 49, Leuven B-3000, BelgiumWithin the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models applied to them. These language models are classified according to their functionality in the context of BCI-based spelling: the static/dynamic nature of the user interface, the use of error correction and predictive spelling, and the potential to improve their classification performance by using language models. To conclude, the review offers an overview of the advantages and challenges when implementing language models in BCI-based communication systems when implemented in conjunction with other AAL technologies.http://www.mdpi.com/1424-8220/14/4/5967Ambient Assisted LivingBrain-Computer Interfacesspelling systemselectroencephalographycommunication systemslanguage models |
| spellingShingle | Anderson Mora-Cortes Nikolay V. Manyakov Nikolay Chumerin Marc M. Van Hulle Language Model Applications to Spelling with Brain-Computer Interfaces Sensors Ambient Assisted Living Brain-Computer Interfaces spelling systems electroencephalography communication systems language models |
| title | Language Model Applications to Spelling with Brain-Computer Interfaces |
| title_full | Language Model Applications to Spelling with Brain-Computer Interfaces |
| title_fullStr | Language Model Applications to Spelling with Brain-Computer Interfaces |
| title_full_unstemmed | Language Model Applications to Spelling with Brain-Computer Interfaces |
| title_short | Language Model Applications to Spelling with Brain-Computer Interfaces |
| title_sort | language model applications to spelling with brain computer interfaces |
| topic | Ambient Assisted Living Brain-Computer Interfaces spelling systems electroencephalography communication systems language models |
| url | http://www.mdpi.com/1424-8220/14/4/5967 |
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