Title: Review of Signal Processing Methods for EEG-Based Wrist Rehabilitation Robots

Abstract: Rehabilitation is a crucial aspect of recovery for individuals affected by accidents, injuries, or medical conditions. Its objective is to restore functionality and enhance quality of life through a range of therapeutic techniques. This review emphasizes the pivotal role of electroencephalography (EEG) in advancing rehabilitation technologies, particularly through its integration with robotic systems. EEG devices, in conjunction with brain-computer interfaces (BCIs), offer profound insights into patient neural activities, enabling the tailored application of therapeutic exercises. Furthermore, machine learning techniques are employed to interpret EEG data, enhancing the precision and adaptability of rehabilitation interventions. This paper discusses the development and application of advanced machine learning algorithms that classify EEG signals for effective control of rehabilitation robots. These innovations promise to personalize treatment procedures, optimize recovery outcomes, and improve patient autonomy by facilitating direct brain-to-device communication. The continuous evolution of EEG and BCI technologies is set to revolutionize rehabilitation practices, offering new pathways to restore independence and improve the quality of life for patients globally.

Keywords: EEG, brain-computer interface, rehabilitation robots, machine learning, classification

Başlık: EEG Tabanlı Bilek Rehabilitasyon Robotları için Sinyal İşleme Yöntemlerinin İncelenmesi

Özet: Rehabilitasyon, kazalardan, yaralanmalardan veya tıbbi durumlardan etkilenen bireylerin iyileşme sürecinde kritik bir rol oynar. Temel amacı, çeşitli terapötik tekniklerle işlevselliği yeniden sağlamak ve yaşam kalitesini artırmaktır. Bu derleme, özellikle robotik sistemlerle entegrasyonu yoluyla, rehabilitasyon teknolojilerinin geliştirilmesinde elektroensefalografinin (EEG) önemli rolünü vurgulamaktadır. EEG cihazları, beyin-bilgisayar arayüzleri (BCI'ler) ile birlikte, hastaların sinirsel aktivitelerine ilişkin derinlemesine bilgiler sunarak terapötik egzersizlerin hassas bir şekilde uygulanmasına olanak tanır. Ayrıca, EEG verilerini yorumlamak için makine öğrenimi tekniklerinin kullanılması, rehabilitasyon müdahalelerinin doğruluğunu ve uyarlanabilirliğini artırmaktadır. Bu makale, rehabilitasyon robotlarının etkili kontrolü için EEG sinyallerini sınıflandıran gelişmiş makine öğrenme algoritmalarının geliştirilmesini ve uygulanmasını tartışmaktadır. Bu yenilikler, tedavi prosedürlerini kişiselleştirmeyi, iyileşme sonuçlarını optimize etmeyi ve beyinden cihaza doğrudan iletişimi kolaylaştırarak hasta özerkliğini artırmayı vaat etmektedir. EEG ve BCI teknolojilerinin sürekli gelişimi, rehabilitasyon uygulamalarında devrim yaratacak ve dünya çapında hastaların bağımsızlığını yeniden kazanması ve yaşam kalitesini iyileştirmesi için yeni yollar sunacaktır.

Anahtar kelimeler: EEG, beyin-bilgisayar arayüzü, rehabilitasyon robotlar, makine öğrenmesi, sınıflandırma

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