Sağlıklı Yaşlı Bireylerde Yeni Seslere Yönlendirme Yanıtının Tespiti: EEG Özelliklerini Kullanan Bir Makine Öğrenimi Yaklaşımı

Öz

Çevrede meydana gelen beklenmedik olaylar, insanı tehlikeli durumlardan koruyan yönlendirici tepkiyi ortaya çıkarır ve bu olayların tespit edilmesi özellikle yaşlanma sürecinde büyük önem taşır. Mevcut çalışmanın amacı, EEG’ye dayalı olaya ilişkin spektral pertürbasyon (ERSP) özellikleri aracılığıyla hangi sınıflandırma modelinin en iyi performansı gösterdiğini bulmaya çalışmak ve hangi frekans bantlarının ve zaman pencerelerinin dış uyaranın sınıflandırılması için en çok katkıda bulunduğunu anlamaktır. 20 sağlıklı yaşlı katılımcının verileri çalışmaya dahil edilmiştir ve katılımcılara 3-Stimülasyon işitsel oddball paradigması uygulanmıştır. Lineer ve Polinom çekirdek fonksiyonlu Destek Vektör Makinesi (DVM), Lineer Diskriminant Analizi (LDA) ve Naive Bayes gibi farklı sınıflandırıcılar, değişen frekans bantlarından ve zaman alanlarından elde edilen ERSP öznitelikleri ile beslenmiştir. Sınıflandırma işlemi, Matlab R2018b altında çalışan MVPA-light araç kutusu ile entegre FieldTrip Toolbox (sürüm no: 20220104) aracılığıyla özel yazılmış komutlar kullanılarak gerçekleştirilmiştir. En iyi performans erken zaman penceresinde (0.1 – 0.5 s) teta yanıtı (4 – 8 HZ) ile beslenen lineer DVM tarafından standart uyaranların yeni uyaranlardan ayırt edilmesi durumunda %90 doğrulukla elde edilmiştir. Delta yanıtları ayrıca LDA (%87 doğruluk) ve polinom DVM (%86 doğruluk) çalıştırarak standart ve yeni uyaranlar için ayırt edici özellikler sergilemektedir. Bu bulgular, delta ve teta yanıtlarının, DVM ve LDA’nın dikkate değer performanslarıyla standart ve yeni seslerin algılanmasına katkıda bulunduğunu göstermektedir.

Anahtar Kelimeler

• Delta
• Teta
• İşitsel Uyaran
• Makine Öğrenmesi

Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features

Öz

Unexpected events in the environment elicit the orienting response that protects humans from dangerous situations and there is great importance in identifying these events, especially in aging. The aims of the current study are attempting to find which classification model exhibits the best performance by means of event-related spectral perturbation (ERSP) features based on EEG and to understand which frequency bands, and time windows, contribute most to the classification of external stimuli. The data of 20 healthy elderly participants were included in the study and the 3-Stimulation auditory oddball paradigm was applied to participants. Different classifiers including Support Vector Machine (SVM) with Linear and Polynomial kernels, Linear Discriminant Analysis (LDA), and Naive Bayes were fed by ERSP features obtained from varying frequency bands and time domains. The classification process was fulfilled using custom-written scripts via the FieldTrip Toolbox (version no: 20220104) integrated with the MVPA-light toolbox running under Matlab R2018b. The best performance was obtained by linear SVM which was fed by theta response (4 – 8 HZ) in the early time window (0.1 – 0.5 s) with 90% accuracy in the case of standard stimuli distinguished from novel stimuli. Delta responses also exhibit distinctive characteristics for standard and novel stimuli by running LDA (87% accuracy) and polynomial SVM (86% accuracy). These findings show that the delta and theta responses have contributed to detecting standard and novel sounds with remarkable performances of SVM and LDA.

Anahtar Kelimeler

• Delta
• Theta
• Auditory Stimuli
• Machine Learning

Kaynakça

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