Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP)
The paper describes the research on the classifiers for brain-computer interface (BCI) based on Steady State Visually Evoked Potential (SSVEP). Authors presented research on the checking the usability of classifiers for recognizing an EEG signal during the stimulus. Three classifiers have been check...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2017-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://doi.org/10.1051/itmconf/20171502003 |
| _version_ | 1818616020685815808 |
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| author | Kubacki Arkadiusz Jakubowski Arkadiusz |
| author_facet | Kubacki Arkadiusz Jakubowski Arkadiusz |
| author_sort | Kubacki Arkadiusz |
| collection | DOAJ |
| description | The paper describes the research on the classifiers for brain-computer interface (BCI) based on Steady State Visually Evoked Potential (SSVEP). Authors presented research on the checking the usability of classifiers for recognizing an EEG signal during the stimulus. Three classifiers have been checked: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and one based on Artificial Neural Network (ANN). First part is concentrated on brain-computer interfaces and classification of them. The second part describes algorithms of all using classifiers. In the next part, authors present test stand and how the experiment is built. The last part consists of results of these tests. The best was the classifier based on Artificial Neural Network – up to 95% of correct identified. The worst results were obtained from Support Vector Machine – about 70%. |
| first_indexed | 2024-12-16T16:43:10Z |
| format | Article |
| id | doaj.art-cb4c1f1ba76448749afb6a1324de8837 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-12-16T16:43:10Z |
| publishDate | 2017-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-cb4c1f1ba76448749afb6a1324de88372022-12-21T22:24:15ZengEDP SciencesITM Web of Conferences2271-20972017-01-01150200310.1051/itmconf/20171502003itmconf_cmes-17_02003Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP)Kubacki ArkadiuszJakubowski ArkadiuszThe paper describes the research on the classifiers for brain-computer interface (BCI) based on Steady State Visually Evoked Potential (SSVEP). Authors presented research on the checking the usability of classifiers for recognizing an EEG signal during the stimulus. Three classifiers have been checked: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and one based on Artificial Neural Network (ANN). First part is concentrated on brain-computer interfaces and classification of them. The second part describes algorithms of all using classifiers. In the next part, authors present test stand and how the experiment is built. The last part consists of results of these tests. The best was the classifier based on Artificial Neural Network – up to 95% of correct identified. The worst results were obtained from Support Vector Machine – about 70%.https://doi.org/10.1051/itmconf/20171502003 |
| spellingShingle | Kubacki Arkadiusz Jakubowski Arkadiusz Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) ITM Web of Conferences |
| title | Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) |
| title_full | Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) |
| title_fullStr | Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) |
| title_full_unstemmed | Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) |
| title_short | Classifier testing for the brain-machine interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) |
| title_sort | classifier testing for the brain machine interface bci based on steady state visually evoked potential ssvep |
| url | https://doi.org/10.1051/itmconf/20171502003 |
| work_keys_str_mv | AT kubackiarkadiusz classifiertestingforthebrainmachineinterfacebcibasedonsteadystatevisuallyevokedpotentialssvep AT jakubowskiarkadiusz classifiertestingforthebrainmachineinterfacebcibasedonsteadystatevisuallyevokedpotentialssvep |