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

Authors : Emine Elif Tülay

Pages : 71-80

Doi:10.26650/acin.1234106

View : 29 | Download : 63

Publication Date : 2024-01-02

Article Type : Research Paper

Abstract :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.
Keywords : Delta, Teta, İşitsel Uyaran, Makine Öğrenmesi