A Safe and Efficient Brain-Computer Interface Using Moving Object Trajectories and LED-Controlled Activation
Yazarlar (3)
Gümüşhane Üniversitesi, Türkiye
Gümüşhane Üniversitesi, Türkiye
Doç. Dr. Levent GÖKREM
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
|
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Micromachines |
| Dergi ISSN | 2072-666X Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 03-2025 |
| Cilt No | 16 |
| Sayı | 3 |
| Sayfalar | 1 / 34 |
| DOI Numarası | 10.3390/mi16030340 |
| Makale Linki | https://doi.org/10.3390/mi16030340 |
| Özet |
| Nowadays, brain–computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier. |
| Anahtar Kelimeler |
| brain–computer interface | classification | electroencephalography | electrooculography | Emotiv Epoc | machine learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 4 |
| SCOPUS | 4 |
| Google Scholar | 5 |