Kişiden Bağımsız Çevrimiçi P300-Tabanlı Beyin-Bilgisayar Arayüzü Sınıflandırma Modeli Oluşturulması

Year 2022, , 73 - 85, 25.04.2022

Onur Erdem Korkmaz , Önder Aydemir , Emin Argun Oral , Yücel Özbek

https://doi.org/10.53433/yyufbed.1077648

Abstract

Beyin-bilgisayar arayüzleri, elektroensefalografi sinyallerini bilgisayar komutlarına çevirerek insan beyni ile bilgisayar veya harici cihazlar arasında iletişim kurmaya yarayan sistemlerdir. Bu sistemlerin en büyük sınırlamalarından biri, kişiye özgü modelin geliştirilmesinin uzun sürmesi, böylelikle de hasta bireylerin tak-çalıştır konforundan yararlanamamasıdır. Yapılan bu çalışma ile geliştirilen yeni paradigma kullanılarak çevrimdışı oturumda 10 katılımcıdan toplanılan verilerle kişiden bağımsız çalışan sınıflandırma modeli geliştirildi. Öncelikle olay ilişkili potansiyel ve olay ilişkili olmayan potansiyel tespitinin gerçekleştirildiği bu ikili sınıflandırma probleminde 50 kez tekrarlanan sınıflandırma işlemi sonucunda %99.40 ± 0.21 test doğruluğu elde edilmiş ve kişiden bağımsız model olarak kaydedilmiştir. Geliştirilen bu model farklı 30 katılımcının kendilerinin belirlediği kelimelerin yazdırılması şeklinde test edilmiştir. Önerilen kişiden bağımsız bu modelin çevrimiçi karakter tespit etme başarısı ise %95.41 olarak hesaplanmıştır.

Keywords

Beyin-bilgisayar arayüzü, Çevrimiçi, Kişiden bağımsız model, Olay ilişkili potansiyeller, EEG

Supporting Institution

Atatürk Üniversitesi

Project Number

FOA-2018-6524

Thanks

Bu çalışma Atatürk Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından FOA-2018-6524 proje numarası ile desteklenmiştir.

Creating an Online Subject Independent P300-Based Brain-Computer Interface Classification Model

Abstract

Brain-computer interfaces convert electroencephalography signals
into computer commands to communicate between the human brain and computer
or external devices. However, one of the most significant limitations of these
systems is that it takes a long time to develop a subject-independent model, so
patients cannot benefit from the plug-and-play comfort. With this study, we
created our data set with the data we collected from 10 people in the offline
session using new paradigm we developed. A brain-computer interface
classification model that works subject-independent was created with this data
set. First of all, in the binary classification problem in which event-related
potential and non-event-related potential detection were performed, 99.40% ±
0.21 test accuracy was obtained due to the classification process being repeated
50 times, and this model was saved. This developed model was tested by printing
the words that 30 different participants determined themselves. This model's
online character detection success, subject independent, was 95.41%

Keywords

Brain-computer interface, Online, Subject-independent model, Event related potentials, EEG

Project Number

FOA-2018-6524

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