Subject-Specific Sinusoid Approach for A Brain–Computer Interface Based on Single-Channel Steady-State Visual Evoked Potential

Year 2021, Volume: 3 Issue: 1, 1 - 12, 30.06.2021

Abdullah Talha Sözer

Abstract

The aim of brain–computer interface (BCI) is to support the daily life of individuals with severe disabilities. For practical BCI, ease of use is one of the most important factors, which is enhanced when fewer electrodes are used. However, using fewer electrode affect the performance of BCI negatively. In this study, a novel single-channel steady-state visual evoked potential (SSVEP) detection method with subject-specific sinusoids approach (SSSA) was developed to enhance the performance of single channel SSVEP based BCI, therefore, to assist the ease of use. For the SSSA, subject-specific sinusoids were defined from training data based on SSVEP frequency and phase features. To detect the SSVEP response, defined sinusoids were used as reference. To evaluate the detection performance of the developed method, it was compared with the well-known power spectral density analysis (PSDA), least absolute shrinkage and selection operator (LASSO) and advanced canonical correlation analysis (CCA) methods on a benchmark dataset. The experimental results showed significantly greater detection accuracy and information transfer rate (ITR) with the SSSA method compared to the PSDA, LASSO and advanced CCA methods. And it is worth to noting that subject-specific sinusoids better represent SSVEP response than template signals that used in advanced CCA. Also proposed method reached one of the highest ITRs reported with max 125 and average 81 bits/min ITRs for single-channel SSVEP based BCI.

Keywords

Brain Computer Interface, Steady-State Visual Evoked Potential, Single Channel Detection, Subject-Specific Sinusoid

Tek Kanallı Durağan Hal Görsel Uyandırılmış Potansiyel Temelli Beyin-Bilgisayar Arayüzü İçin Deneğe Özgü Sinüzoit Yaklaşımı

Abstract

Beyin-bilgisayar arayüzünün (BBA) amacı, ciddi engelli bireylerin günlük yaşamlarını desteklemektir. Pratik BBA için en önemli faktörlerden biri olan kullanım kolaylığı, az sayıda elektrot kullanıldığında artmaktadır. Ancak az sayıda elektrot kullanılması BBA performansını olumsuz yönde etkiler. Bu çalışmada, tek kanallı durağan hal görsel uyarılmış potansiyel (DHGUP) temelli BBA’nın performansını artırmak ve böylece kullanım kolaylığını desteklemek için, deneğe özgü sinüzoit yaklaşımı (DÖSY) ile yeni bir tek kanallı DHGUP algılama yöntemi geliştirilmiştir. DÖSY’de deneğe özgü sinüzoitler, eğitim aşamasında DHGUP’nin frekans ve faz özelliklerinden faydalanılarak tanımlanmıştır. Tanımlanan bu sinüzoitler, test aşamasında, DHGUP yanıtının tespitinde referans olarak kullanılmıştır. Geliştirilen yöntemin tespit performansı, bir kıyaslama veri setinde, iyi bilinen güç spektral yoğunluk analizi (GSYA), minimum mutlak büzülme ve seçim operatörü (MMBSO) ve gelişmiş kanonik korelasyon analizi (KKA) yöntemleri ile karşılaştırılarak test edilmiştir. Deneysel sonuçlar, DÖSY yöntemiyle, GSYA, MMBSO ve gelişmiş KKA yöntemlerine kıyasla önemli ölçüde daha yüksek tespit doğruluğu ve bilgi aktarım hızı (BAH) göstermiştir. Ve deneğe özgü sinüzoitlerin gelişmiş KKA’da kullanılan şablon sinyallerden daha iyi DHGUP yanıtını temsil ettiği gösterilmiştir. Ek olarak önerilen yöntem, tek kanallı DHGUP tabanlı BBA için maksimum 125 ve ortalama 81 bit / dak BAH ile, bildirilen en yüksek BAH değerlerinden birine ulaşmıştır.

Keywords

Beyin Bilgisayar Arayüzü, Durağan Hal Görsel Uyarılmış Potansiyel, Tek Kanal Tespit, Deneğe Özgü Sinüzoit

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