Evaluation of a steady-state visual evoked potential controlled quadcopter path using different performance measures

Year 2023, , 131 - 142, 18.10.2023

Kaan Delihasan , Zafer İşcan

https://doi.org/10.53070/bbd.1350921

Abstract

This study focuses on controlling a quadcopter system using a steady-state visual evoked potential (SSVEP)-based brain-computer interface system. In the literature, researchers report the accuracy and information transfer rate. However, these measures do not provide sufficient information about the predicted and target path similarity. The drone is expected to follow a certain square-shaped path and return to its starting position. We calculated the final and mean distances as additional outcome measures using several classifiers. The results emphasize the importance of having a balanced confusion matrix in the performance of quadcopter control and provide a more complete picture in the evaluation of the quadcopter’s performance. Focusing on the relationship between classification accuracy and spatial deviation might create a new perspective for BCI-based control systems.

Keywords

Unmanned Aerial Vehicles (UAV), Path following, Control, Brain-computer interface

Farklı performans ölçümleri kullanılarak kararlı durum görsel uyarılmış potansiyel kontrollü quadcopter yolunun değerlendirilmesi

Abstract

Bu çalışma, kararlı durum görsel uyarılmış potansiyel (SSVEP) tabanlı beyin-bilgisayar arayüz sistemi kullanılarak bir quadcopter sisteminin kontrol edilmesine odaklanmaktadır. Literatürde araştırmacılar doğruluğu ve bilgi aktarım hızını bildirmektedir. Ancak bu ölçümler tahmin edilen ve hedeflenen yol benzerliği hakkında yeterli bilgi sağlamamaktadır. Drone'un kare şeklinde belli bir yol izlemesi ve başlangıç ​​pozisyonuna dönmesi bekleniyor. Çeşitli sınıflandırıcılar kullanarak ek sonuç ölçüleri olarak nihai ve ortalama mesafeleri hesapladık. Sonuçlar, quadcopter kontrolünün performansında dengeli bir karışıklık matrisine sahip olmanın önemini vurguluyor ve quadcopter performansının değerlendirilmesinde daha eksiksiz bir resim sağlıyor. Sınıflandırma doğruluğu ile mekansal sapma arasındaki ilişkiye odaklanmak BCI tabanlı kontrol sistemleri için yeni bir bakış açısı yaratabilir.

Keywords

İnsansız Hava Araçları (İHA), Yol Takip, Kontrol, Beyin-Bilgisayar Arayüzü

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