Robot Arm Control With for SSVEP-Based Brain Signals In Brain Computer Interface
| dc.authorid | Tuysuz, Mehmet Fatih/0000-0002-8955-9710 | |
| dc.authorid | Mühendislik Fakültesi, Harran Üniversitesi/0000-0001-7407-1635 | |
| dc.authorid | ÇİĞ, HARUN/0000-0003-0419-9531 | |
| dc.authorid | Hanbay, Davut/0000-0003-2271-7865 | |
| dc.authorwosid | Tuysuz, Mehmet Fatih/AAG-6097-2019 | |
| dc.authorwosid | Mühendislik Fakültesi, Harran Üniversitesi/GXH-7079-2022 | |
| dc.authorwosid | ÇİĞ, HARUN/ABA-3476-2020 | |
| dc.authorwosid | Hanbay, Davut/AAG-8511-2019 | |
| dc.contributor.author | Cig, Harun | |
| dc.contributor.author | Hanbay, Davut | |
| dc.contributor.author | Tuysuz, Fatih | |
| dc.date.accessioned | 2024-08-04T21:01:03Z | |
| dc.date.available | 2024-08-04T21:01:03Z | |
| dc.date.issued | 2017 | |
| dc.department | İnönü Üniversitesi | en_US |
| dc.description | 2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY | en_US |
| dc.description.abstract | Hilbert Transform (HT) and Multi Wavelet Transform (MWT) has been used to recognize the same frequency harmonics that occur in the brain with the Steady State Visual Evoked Potentials(SSVEP). In this study, harmonics of certain frequencies in brain are used which are detected by SSVEP and visual stimulus potentials to be used in Robot Arm Control. This stimulus has been made using shapes of box that oscillated at certain frequencies. The signal components were clustered according to the same direction and stimulus frequency on the data set for the desired work, task or movement. These signals were processed by the band pass filters at 5-30 Hz then HD process were applied. The filtered signals classified by Neural Network and Cubic-Support Vector Machine after MWT analysis were applied to these. Evaluated average success rate is over 90 %. Finally, the test brain signals recorded for 3 tasks over the trained network have been successfully used for Robot Arm Control. The use of the proposed HD-MWT method is promising for the development of a real-time robot control with SSVEP-based BCI. | en_US |
| dc.description.sponsorship | IEEE Turkey Sect,Anatolian Sci | en_US |
| dc.identifier.isbn | 978-1-5386-1880-6 | |
| dc.identifier.uri | https://hdl.handle.net/11616/104055 | |
| dc.identifier.wos | WOS:000426868700119 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.language.iso | tr | en_US |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | 2017 International Artificial Intelligence and Data Processing Symposium (Idap) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Hilbert Transform | en_US |
| dc.subject | Multi Wavelet Transform | en_US |
| dc.subject | EEG | en_US |
| dc.subject | Steady State Visually Evoked Potentials | en_US |
| dc.subject | Neural Network | en_US |
| dc.subject | Classification | en_US |
| dc.title | Robot Arm Control With for SSVEP-Based Brain Signals In Brain Computer Interface | en_US |
| dc.type | Conference Object | en_US |
