A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked Potentials
Brain−computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs) typically utilize a synchronous approach to identify targets (i.e., after preset time periods the system produces command outputs). Hence, users have only a limited amount of time to fixate a desired...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/8/2/33 |
| _version_ | 1828233412411916288 |
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| author | Felix Gembler Ivan Volosyak |
| author_facet | Felix Gembler Ivan Volosyak |
| author_sort | Felix Gembler |
| collection | DOAJ |
| description | Brain−computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs) typically utilize a synchronous approach to identify targets (i.e., after preset time periods the system produces command outputs). Hence, users have only a limited amount of time to fixate a desired target. This hinders the usage of more complex interfaces, as these require the BCI to distinguish between intentional and unintentional fixations. In this article, we investigate a dynamic sliding window mechanism as well as the implementation of software-based stimulus synchronization to enable the threshold-based target identification for the c-VEP paradigm. To further improve the usability of the system, an ensemble-based classification strategy was investigated. In addition, a software-based approach for stimulus on-set determination is proposed, which allows for an easier setup of the system, as it reduces additional hardware dependencies. The methods were tested with an eight-target spelling application utilizing an <i>n</i>-gram word prediction model. The performance of eighteen participants without disabilities was tested; all participants completed word- and sentence spelling tasks using the c-VEP BCI with a mean information transfer rate (ITR) of 75.7 and 57.8 bpm, respectively. |
| first_indexed | 2024-04-12T19:39:14Z |
| format | Article |
| id | doaj.art-e3662c1391a34a729191f634474ac49f |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-04-12T19:39:14Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-e3662c1391a34a729191f634474ac49f2022-12-22T03:19:08ZengMDPI AGComputers2073-431X2019-04-01823310.3390/computers8020033computers8020033A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked PotentialsFelix Gembler0Ivan Volosyak1Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, GermanyFaculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, GermanyBrain−computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs) typically utilize a synchronous approach to identify targets (i.e., after preset time periods the system produces command outputs). Hence, users have only a limited amount of time to fixate a desired target. This hinders the usage of more complex interfaces, as these require the BCI to distinguish between intentional and unintentional fixations. In this article, we investigate a dynamic sliding window mechanism as well as the implementation of software-based stimulus synchronization to enable the threshold-based target identification for the c-VEP paradigm. To further improve the usability of the system, an ensemble-based classification strategy was investigated. In addition, a software-based approach for stimulus on-set determination is proposed, which allows for an easier setup of the system, as it reduces additional hardware dependencies. The methods were tested with an eight-target spelling application utilizing an <i>n</i>-gram word prediction model. The performance of eighteen participants without disabilities was tested; all participants completed word- and sentence spelling tasks using the c-VEP BCI with a mean information transfer rate (ITR) of 75.7 and 57.8 bpm, respectively.https://www.mdpi.com/2073-431X/8/2/33brain–computer interface (BCI)electroencephalogram (EEG)visual evoked potentials (VEP)code-modulated visual evoked potentials (c-VEP) |
| spellingShingle | Felix Gembler Ivan Volosyak A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked Potentials Computers brain–computer interface (BCI) electroencephalogram (EEG) visual evoked potentials (VEP) code-modulated visual evoked potentials (c-VEP) |
| title | A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked Potentials |
| title_full | A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked Potentials |
| title_fullStr | A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked Potentials |
| title_full_unstemmed | A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked Potentials |
| title_short | A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked Potentials |
| title_sort | novel dictionary driven mental spelling application based on code modulated visual evoked potentials |
| topic | brain–computer interface (BCI) electroencephalogram (EEG) visual evoked potentials (VEP) code-modulated visual evoked potentials (c-VEP) |
| url | https://www.mdpi.com/2073-431X/8/2/33 |
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