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

Year 2023, Volume: 7 Issue: 1, 71 - 80, 02.01.2024
https://doi.org/10.26650/acin.1234106

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.

References

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  • Haenschel, C., Baldeweg, T., Croft, R. J., Whittington, M., & Gruzelier, J. (2000). Gamma and beta frequency oscillations in response to novel auditory stimuli: A comparison of human electroencephalogram (EEG) data with in vitro models. Proceedings of the National Academy of Sciences, 97(13), 7645-7650. https://doi.org/10.1073/pnas.120162397 google scholar
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Sağlıklı Yaşlı Bireylerde Yeni Seslere Yönlendirme Yanıtının Tespiti: EEG Özelliklerini Kullanan Bir Makine Öğrenimi Yaklaşımı

Year 2023, Volume: 7 Issue: 1, 71 - 80, 02.01.2024
https://doi.org/10.26650/acin.1234106

Abstract

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.

References

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  • Alarcao, S. M., & Fonseca, M. J. (2019). Emotions Recognition Using EEG Signals: A Survey. IEEE Transactions on Affective Computing, 10(3), 374-393. https://doi.org/10.1109/taffc.2017.2714671 google scholar
  • Aliakbaryhosseinabadi, S., Kamavuako, E. N., Jiang, N., Farina, D., & Mrachacz-Kersting, N. (2019). Classification of Movement Preparation Between Attended and Distracted Self-Paced Motor Tasks. IEEE Transactions on Biomedical Engineering, 66(11), 3060-3071. https://doi.org/10.1109/ tbme.2019.2900206 google scholar
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  • Borra, D., & Magosso, E. (2021). Deep learning-based EEG analysis: investigating P3 ERP components. Journal of Integrative Neuroscience, 20(4), 791-811. https://doi.org/10.31083/j.jin2004083 google scholar
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  • Cavanagh, J. F., Kumar, P., Mueller, A. A., Richardson, S. P., & Mueen, A. (2018). Diminished EEG habituation to novel events effectively classifies google scholar
  • Parkinson’s patients. Clinical Neurophysiology, 129(2), 409-418. https://doi.org/10.1016/j.clinph.2017.11.023 google scholar
  • Chung, J., & Teo, J. (2022). Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges. Applied Computational Intelligence and Soft Computing, 2022, e9970363. https://doi.org/10.1155/2022/9970363 google scholar
  • Debener, S., Makeig, S., Delorme, A., & Engel, A. K. (2005). What is novel in the novelty oddball paradigm? Functional significance of the novelty P3 event-related potential as revealed by independent component analysis. Cognitive Brain Research, 22(3), 309-321. https://doi.org/10.1016/j. cogbrainres.2004.09.006 google scholar
  • Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009 google scholar
  • Demiralp, T., Yordanova, J., Kolev, V., Ademoğlu, A., Devrim, M., & Samar, V.J. (1999). Time-frequency analysis of single-sweep event-related potentials by means of fast wavelet transform. Brain Lang. 66, 129-145. https://doi:10.1006/brln.1998.2028 google scholar
  • Emek-Savaş, D. D., Güntekin, B., Yener, G. G., & Başar, E. (2016). Decrease of delta oscillatory responses is associated with increased age in healthy elderly. International Journal of Psychophysiology, 103, 103-109. https://doi.org/10.1016/j.ijpsycho.2015.02.006 google scholar
  • Güntekin, B., & Başar, E. (2016). Review of evoked and event-related delta responses in the human brain. International Journal of Psychophysiology, 103, 43-52. https://doi.org/10.1016/j.ijpsycho.2015.02.001 google scholar
  • Haenschel, C., Baldeweg, T., Croft, R. J., Whittington, M., & Gruzelier, J. (2000). Gamma and beta frequency oscillations in response to novel auditory stimuli: A comparison of human electroencephalogram (EEG) data with in vitro models. Proceedings of the National Academy of Sciences, 97(13), 7645-7650. https://doi.org/10.1073/pnas.120162397 google scholar
  • Harmony, T. (2013). The functional significance of delta oscillations in cognitive processing. Frontiers in Integrative Neuroscience, 7. https://doi. org/10.3389/fnint.2013.00083 google scholar
  • Ho, M.-C., Huang, C.-F., Chou, C.-Y., Lin, Y.-T., Shih, C.-S., Wu, M.-T., ... Liu, C.-J. (2012). Task-related brain oscillations in normal aging. Health, 04(09), 762-768. https://doi.org/10.4236/health.2012.429118 google scholar
  • Hosseini, M.-P., Hosseini, A., & Ahi, K. (2021). A Review on Machine Learning for EEG Signal Processing in Bioengineering. IEEE Reviews in Biomedical Engineering, 14, 204-218. https://doi.org/10.1109/rbme.2020.2969915 google scholar
  • Huizeling, E., Wang, H., Holland, C., & Kessler, K. (2021). Changes in theta and alpha oscillatory signatures of attentional control in older and middle age. European Journal of Neuroscience, 54(1), 4314-4337. https://doi.org/10.1111/ejn.15259 google scholar
  • Karakaç, S. (2020). A review of theta oscillation and its functional correlates. International Journal of Psychophysiology. https://doi.org/10.1016Zj. ijpsycho.2020.04.008 google scholar
  • Li, K. Z. H., & Lindenberger, U. (2002). Relations between aging sensory/sensorimotor and cognitive functions. Neuroscience & Biobehavioral Reviews, 26(7), 777-783. https://doi.org/10.1016/s0149-7634(02)00073-8 google scholar
  • Liebherr, M., Corcoran, A. W., Alday, P. M., Coussens, S., Bellan, V., Howlett, C. A., . Bornkessel-Schlesewsky, I. (2021). EEG and behavioral correlates of attentional processing while walking and navigating naturalistic environments. Scientific Reports, 11(1). https://doi.org/10.1038/ s41598-021-01772-8 google scholar
  • Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018). A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. Journal of Neural Engineering, 15(3), 031005. https://doi.org/10.1088/1741-2552/aab2f2 google scholar
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  • Makeig, S. (1993). Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalography and Clinical Neurophysiology, 86(4), 283-293. https://doi.org/10.1016/0013-4694(93)90110-h google scholar
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There are 49 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Emine Elif Tülay 0000-0003-0150-5476

Publication Date January 2, 2024
Submission Date January 16, 2023
Published in Issue Year 2023 Volume: 7 Issue: 1

Cite

APA Tülay, E. E. (2024). Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features. Acta Infologica, 7(1), 71-80. https://doi.org/10.26650/acin.1234106
AMA Tülay EE. Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features. ACIN. January 2024;7(1):71-80. doi:10.26650/acin.1234106
Chicago Tülay, Emine Elif. “Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features”. Acta Infologica 7, no. 1 (January 2024): 71-80. https://doi.org/10.26650/acin.1234106.
EndNote Tülay EE (January 1, 2024) Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features. Acta Infologica 7 1 71–80.
IEEE E. E. Tülay, “Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features”, ACIN, vol. 7, no. 1, pp. 71–80, 2024, doi: 10.26650/acin.1234106.
ISNAD Tülay, Emine Elif. “Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features”. Acta Infologica 7/1 (January 2024), 71-80. https://doi.org/10.26650/acin.1234106.
JAMA Tülay EE. Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features. ACIN. 2024;7:71–80.
MLA Tülay, Emine Elif. “Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features”. Acta Infologica, vol. 7, no. 1, 2024, pp. 71-80, doi:10.26650/acin.1234106.
Vancouver Tülay EE. Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features. ACIN. 2024;7(1):71-80.