Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features
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 performa...
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Istanbul University Press
2023-06-01
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| Series: | Acta Infologica |
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| Online Access: | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/5A043AE975384F98920740EBE0D6082C |
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| author | Emine Elif Tülay |
| author_facet | Emine Elif Tülay |
| author_sort | Emine Elif Tülay |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-41df52e7ff7742eebf1ad710702366d5 |
| institution | OA Journals |
| issn | 2602-3563 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Istanbul University Press |
| record_format | Article |
| series | Acta Infologica |
| spelling | doaj-art-41df52e7ff7742eebf1ad710702366d52025-08-20T02:13:52ZengIstanbul University PressActa Infologica2602-35632023-06-0171718010.26650/acin.1234106123456Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG FeaturesEmine Elif Tülay0https://orcid.org/0000-0003-0150-5476Muğla Sıtkı Koçman Üniversitesi, Mugla, TurkiyeUnexpected 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.https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/5A043AE975384F98920740EBE0D6082Cdeltathetaauditory stimulimachine learning |
| spellingShingle | Emine Elif Tülay Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features Acta Infologica delta theta auditory stimuli machine learning |
| title | Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features |
| title_full | Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features |
| title_fullStr | Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features |
| title_full_unstemmed | Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features |
| title_short | Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features |
| title_sort | detection of orienting response to novel sounds in healthy elderly subjects a machine learning approach using eeg features |
| topic | delta theta auditory stimuli machine learning |
| url | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/5A043AE975384F98920740EBE0D6082C |
| work_keys_str_mv | AT emineeliftulay detectionoforientingresponsetonovelsoundsinhealthyelderlysubjectsamachinelearningapproachusingeegfeatures |