Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs
In this paper we compared different visual feedback methods, informing users about classification progress in a steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) speller application. According to results from our previous studies, changes in stimulus size and contrast...
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| Format: | Article |
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MDPI AG
2020-04-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/10/4/240 |
| _version_ | 1797570310929645568 |
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| author | Mihaly Benda Ivan Volosyak |
| author_facet | Mihaly Benda Ivan Volosyak |
| author_sort | Mihaly Benda |
| collection | DOAJ |
| description | In this paper we compared different visual feedback methods, informing users about classification progress in a steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) speller application. According to results from our previous studies, changes in stimulus size and contrast as online feedback of classification progress have great impact on BCI performance in SSVEP-based spellers. In this experiment we further investigated these effects, and tested a 4-target SSVEP speller interface with a much higher number of subjects. Five different scenarios were used with variations in stimulus size and contrast, “<i>no feedback</i>”, “<i>size increasing</i>”, “<i>size decreasing</i>”, “<i>contrast increasing</i>”, and “<i>contrast decreasing</i>”. With each of the five scenarios, 24 participants had to spell six letter words (at least 18 selections with this three-steps speller). The fastest feedback modalities were different for the users, there was no visual feedback which was generally better than the others. With the used interface, six users achieved significantly better Information Transfer Rates (ITRs) compared to the “<i>no feedback</i>” condition. Their average improvement by using the individually fastest feedback method was 46.52%. This finding is very important for BCI experiments, as by determining the optimal feedback for the user, the speed of the BCI can be improved without impairing the accuracy. |
| first_indexed | 2024-03-10T20:23:08Z |
| format | Article |
| id | doaj.art-5733241871ad40aeb0f2a36a6e05101d |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-10T20:23:08Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-5733241871ad40aeb0f2a36a6e05101d2023-11-19T22:00:29ZengMDPI AGBrain Sciences2076-34252020-04-0110424010.3390/brainsci10040240Comparison of Different Visual Feedback Methods for SSVEP-Based BCIsMihaly Benda0Ivan 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, GermanyIn this paper we compared different visual feedback methods, informing users about classification progress in a steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) speller application. According to results from our previous studies, changes in stimulus size and contrast as online feedback of classification progress have great impact on BCI performance in SSVEP-based spellers. In this experiment we further investigated these effects, and tested a 4-target SSVEP speller interface with a much higher number of subjects. Five different scenarios were used with variations in stimulus size and contrast, “<i>no feedback</i>”, “<i>size increasing</i>”, “<i>size decreasing</i>”, “<i>contrast increasing</i>”, and “<i>contrast decreasing</i>”. With each of the five scenarios, 24 participants had to spell six letter words (at least 18 selections with this three-steps speller). The fastest feedback modalities were different for the users, there was no visual feedback which was generally better than the others. With the used interface, six users achieved significantly better Information Transfer Rates (ITRs) compared to the “<i>no feedback</i>” condition. Their average improvement by using the individually fastest feedback method was 46.52%. This finding is very important for BCI experiments, as by determining the optimal feedback for the user, the speed of the BCI can be improved without impairing the accuracy.https://www.mdpi.com/2076-3425/10/4/240brain–computer interface (BCI)steady-state visual evoked potential (SSVEP)user feedbackelectroencephalography (EEG) |
| spellingShingle | Mihaly Benda Ivan Volosyak Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs Brain Sciences brain–computer interface (BCI) steady-state visual evoked potential (SSVEP) user feedback electroencephalography (EEG) |
| title | Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs |
| title_full | Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs |
| title_fullStr | Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs |
| title_full_unstemmed | Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs |
| title_short | Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs |
| title_sort | comparison of different visual feedback methods for ssvep based bcis |
| topic | brain–computer interface (BCI) steady-state visual evoked potential (SSVEP) user feedback electroencephalography (EEG) |
| url | https://www.mdpi.com/2076-3425/10/4/240 |
| work_keys_str_mv | AT mihalybenda comparisonofdifferentvisualfeedbackmethodsforssvepbasedbcis AT ivanvolosyak comparisonofdifferentvisualfeedbackmethodsforssvepbasedbcis |