Emotiv Epoc ile Durağan Hal Görsel Uyarılmış Potansiyel Temelli Beyin Bilgisayar Arayüzü Uygulaması

Beyin Bilgisayar Arayüzü (BBA), geleneksel iletişim yöntemlerinin kullanılmasını engelleyen sinir-kashastalıklarına sahip olan bireyler için yeni bir iletişim seçeneği sunmaktadır. Durağan hal görsel uyarılmışpotansiyel (DHGUP) temelli BBA sistemleri sağladığı yüksek hız ve kullanım kolaylığı sebebi ile dikkatleriüzerine çekmektedir. Bu çalışmada Emotiv Epoc elektroansefalografi (EEG) cihazı kullanılarak DHGUP temelliBBA uygulaması gerçekleştirilmiştir. Çalışma, 5 kullanıcının katılımı ile ön hazırlık ve gerçek zamanlı deneylerolmak üzere iki adımdan oluşmaktadır. Ön hazırlık deneyleri ile gerçek zamanlı BBA sisteminde kullanılacakDHGUP tespit metodu ve EEG sinyali toplama süresinin belirlenmesi istenmiştir. Ön hazırlıkta farklı frekansasahip 12 adet görsel uyaran kullanıcıya sıra ile sunulmakta ve 5 saniye boyunca sinyal kaydı yapılmaktadır. Önhazırlık sinyallerinde 2 saniyelik EEG pencerelerinde %82.2 DHGUP tespit doğruluğu ve 68.8 bit/sn. bilgi aktarımhızına ulaşılmıştır. Gerçek zamanlı BBA sisteminde ise tuş takımı biçiminde tasarlanan görsel uyaran düzeneği ilekullanıcıların yalnız beyin sinyalleri ile telefon numaralarını yazmalarına imkân sağlanmıştır. Sistem 2 sn. seçimsüresi ve 0.5 sn. tespit ve geri besleme süresi olmak üzere 2.5 saniyede 1 karakterin yazımına imkan sağlamaktadır.Deneylerde tasarlanan BBA ile 11 haneli telefon numarasının ortalama 40 saniyede yazdırılabildiği gösterilmiştir.Ayrıca çalışmada yüksek DHGUP tespit doğruluğu sağlayan eğitim verisi destekli bir yöntemin Emotiv Epoc ileuygulanabilirliği incelenmiştir. Yöntem, yaygın yöntemlere göre daha yüksek DHGUP tespiti sağlamamıştır. Bumakale DHGUP temelli BBA uygulaması için kılavuz niteliğini taşımaktadır.

Implementation of Steady State Visual Evoked Potential based Brain Computer Interface with Emotiv EPOC

Brain computer interface (BCI) offers a new communication pathway to individuals with neuromuscular disorders that prevent the use of traditional communication channels. The steady state visual evoked potential (SSVEP) based BCI systems take attention since it has high speed and ease of use. In this study, SSVEP based BCI implementation was performed by using Emotiv Epoc electroencephalography (EEG) device. This study consists of two steps, preliminary preparation and real-time experiments with participation of 5 subjects. In preliminary preparation stage, the SSVEP detection method and the EEG signal length that will be used in the real-time BBA were wanted to be decide. In the preliminary preparation stage, 12 visual stimuli with different frequencies are presented to the user in sequence and signal recording is performed for 5 seconds. In preparation signals, 82.2% SSVEP detection accuracy and 68.8 bits/sec. information transfer rate were reached in the 2-second EEG epochs. In the real-time BCI, the visual stimuli designed in the form of a keypad allows users to write phone numbers only with the brain signals. The system allows entering a character in 2.5 seconds, including 2 sec selection time and 0.5 sec detection and feedback time. Experiments with 5 users showed that 11-digit phone number can be entered about 40 seconds with the BCI. In addition, it was investigated the applicability of a training data supported method which provides high SSVEP detection accuracy using Emotiv Epoc. The method did not provide higher SSVEP detection accuracy than traditional methods. This article is a guideline for SSVEP-based BCI application.

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