Selçuk Üniversitesi Öğretim Görevlisi Yetiştirme Programı Koordinatörlüğü
2017-ÖYP-045
A Brain-Computer Interface (BCI) is a communication system that decodes and transfers information directly from the brain to external devices. The electroencephalogram (EEG) technique is used to measure the electrical signals corresponding to commands occurring in the brain to control functions. The signals used for control applications in BCI are called Motor Imagery (MI) EEG signals. EEG signals are noisy, so it is important to use the right methods to recognize patterns correctly. This study examined the performances of different classification schemes to train networks using Ensemble Subspace Discriminant classifier. Also, the most efficient feature space was found using Neighborhood Component Analysis. The maximum average accuracy in classifying MI signals corresponding to right-direction and left-direction was 80.4% with a subject-specific classification scheme and 250 features.
BCI classification scheme eeg feature selecetion subject-independent subject-specific
2017-ÖYP-045
Birincil Dil | İngilizce |
---|---|
Konular | Yapay Zeka, Yazılım Testi, Doğrulama ve Validasyon, Yazılım Mühendisliği (Diğer) |
Bölüm | Araştırma Makalesi |
Yazarlar | |
Proje Numarası | 2017-ÖYP-045 |
Yayımlanma Tarihi | 30 Nisan 2023 |
Gönderilme Tarihi | 17 Ekim 2022 |
Kabul Tarihi | 10 Ocak 2023 |
Yayımlandığı Sayı | Yıl 2023 Cilt: 27 Sayı: 2 |
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