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

Yıl 2023, Cilt: 7 Sayı: 1, 71 - 80, 02.01.2024

Emine Elif Tülay

https://doi.org/10.26650/acin.1234106

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

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

Kaynakça

• Aellen, F. M., Göktepe-Kavis, P., Apostolopoulos, S., & Tzovara, A. (2021). Convolutional neural networks for decoding electroencephalography responses and visualizing trial by trial changes in discriminant features. Journal of Neuroscience Methods, 364, 109367. https://doi.org/10.1016/j.jneumeth.2021.109367 google scholar
• Akhter, R., Lawal, K., Tanvir, Md., & Ahmed, S. (2020). Classification of Common and Uncommon Tones by P300 Feature Extraction and Identification of Accurate P300 Wave by Machine Learning Algorithms. International Journal of Advanced Computer Science and Applications, 11(10). https:// doi.org/10.14569/ijacsa.2020.0111080 google scholar
• 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
• Barry, R. J. (2009). Habituation of the orienting reflex and the development of Preliminary Process Theory. Neurobiology of Learning and Memory, 92(2), 235-242. https://doi.org/10.1016/j.nlm.2008.07.007 google scholar
• Bradley, M.M., & Lang, P.J. (1999). International Affective Digitized Sounds (IADS-1): Stimuli, instruction manual, and affective ratings. Technical Report No B-2. University of Florida, Center for Research in Psychophysiology: Gainesville, FL, USA. google scholar
• Başar, E. (2006). The theory of the whole-brain-work. International Journal of Psychophysiology, 60(2), 133-138. https://doi.org/10.1016/j.ijpsycho.2005.12.007 google scholar
• Başar, E. (2013). A review of gamma oscillations in healthy subjects and in cognitive impairment. International Journal of Psychophysiology, 90(2), 99-117. https://doi.org/10.1016/j.ijpsycho.2013.07.005 google scholar
• Başar, E., Başar-Eroglu, C., Karakaş, S., & Schürmann, M. (2001). Gamma, alpha, delta, and theta oscillations govern cognitive processes. International Journal of Psychophysiology, 39(2-3), 241-248. https://doi.org/10.1016/s0167-8760(00)00145-8 google scholar
• Basar, E., Demiralp, T., Schürmann, M., Basar-Eroglu, C., & Ademoglu, A. (1999). Oscillatory Brain Dynamics, Wavelet Analysis, and Cognition. Brain and Language, 66(1), 146-183. https://doi.org/10.1006/brln.1998.2029 google scholar
• Başar, E., & Güntekin, B. (2012). A short review of alpha activity in cognitive processes and in cognitive impairment. International Journal of Psychophysiology, 86(1), 25-38. https://doi.org/10.1016/j.ijpsycho.2012.07.001 google scholar
• Başar-Eroğlu, C., Başar, E., Demiralp, T., & Schürmann, M. (1992). P300-response: possible psychophysiological correlates in delta and theta frequency channels. A review. International Journal of Psychophysiology, 13(2), 161-179. https://doi.org/10.1016/0167-8760(92)90055-g google scholar
• Başar-Eroğlu, C., & Demiralp, T. (2001). Event-related theta oscillations: an integrative and comparative approach in the human and animal brain. International Journal of Psychophysiology, 39(2-3), 167-195. https://doi.org/10.1016/s0167-8760(00)00140-9 google scholar
• Behforuzi, H., Feng, N. C., Billig, A. R., Ryan, E., Tusch, E. S., Holcomb, P. J., ... Daffner, K. R. (2019). Markers of Novelty Processing in Older Adults Are Stable and Reliable. Frontiers in Aging Neuroscience, 11. https://doi.org/10.3389/fnagi.2019.00165 google scholar
• Berti, S., Vossel, G., & Gamer, M. (2017). The Orienting Response in Healthy Aging: Novelty P3 Indicates No General Decline but Reduced Efficacy for Fast Stimulation Rates. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.01780 google scholar
• 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
• Cavanagh, J.F. (2021). EEG: 3-Stim Auditory Oddball and Rest in Parkinson’s. OpenNeuro; [Dataset] doi: 10.18112/openneuro.ds003490.v1.1.0 google scholar
• 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
• Lynn, R. (1966). Attention, Arousal, and the Orientation Reaction. Oxford: Pergamon Press. google scholar
• 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
• Makinen, V. T., May, P. J. C., & Tiitinen, H. (2004). Human auditory event-related processes in the time-frequency plane. NeuroReport, 15(11), 1767-1771. https://doi.org/10.1097/01.wnr.0000134841.48507.5a google scholar
• Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011). FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Computational Intelligence and Neuroscience, 2011, 1-9. https://doi.org/10.1155/2011/156869 google scholar
• Parvar, H., Sculthorpe-Petley, L., Satel, J., Boshra, R., D’Arcy, R. C. N., & Trappenberg, T. P. (2014). Detection of event-related potentials in individual subjects using support vector machines. Brain Informatics, 2(1), 1-12. https://doi.org/10.1007/s40708-014-0006-7 google scholar
• Rahman, Md. M., Sarkar, A. K., Hossain, Md. A., Hossain, Md. S., Islam, Md. R., Hossain, Md. B., . Moni, M. A. (2021). Recognition of human emotions using EEG signals: A review. Computers in Biology and Medicine, 136, 104696. https://doi.org/10.1016/j.compbiomed.2021.104696 google scholar
• Riis, J. L., Chong, H., Ryan, K. K., Wolk, D. A., Rentz, D. M., Holcomb, P. J., & Daffner, K. R. (2008). Compensatory neural activity distinguishes different patterns of normal cognitive aging. NeuroImage, 39(1), 441-454. https://doi.org/10.1016/j.neuroimage.2007.08.034 google scholar
• Saeidi, M., Karwowski, W., Farahani, F. V., Fiok, K., Taiar, R., Hancock, P. A., & Al-Juaid, A. (2021). Neural Decoding of EEG Signals with Machine Learning: A Systematic Review. Brain Sciences, 11(11), 1525. https://doi.org/10.3390/brainsci11111525 google scholar
• Schmiedt-Fehr, C., & Basar-Eroglu, C. (2011). Event-related delta and theta brain oscillations reflect age-related changes in both a general and a specific neuronal inhibitory mechanism. Clinical Neurophysiology, 122(6), 1156-1167. https://doi.org/10.1016/j.clinph.2010.10.045 google scholar
• Schmiedt-Fehr, C., Dühl, S., & Basar-Eroglu, C. (2011). Age-related increases in within-person variability: Delta and theta oscillations indicate that the elderly are not always old. Neuroscience Letters, 495(2), 159-163. https://doi.org/10.1016/j.neulet.2011.03.062 google scholar
• Tjandrasa, H., & Djanali, S. (2018). Classification of P300 event-related potentials using wavelet transform, MLP, and soft margin SVM. 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI). https://doi.org/10.1109/icaci.2018.8377481 google scholar
• Treder, M.S. (2020). MVPA-Light: a classification and regression toolbox for multi- dimensional data. Frontiers in Neuroscience 2020; 14: 289. https:// doi.org/10.3389/ fnins.2020.00289. google scholar
• Villena-González, M., Palacios-García, I., Rodríguez, E., & López, V. (2018). Beta Oscillations Distinguish Between Two Forms of Mental Imagery While Gamma and Theta Activity Reflects Auditory Attention. Frontiers in Human Neuroscience, 12. https://doi.org/10.3389/fnhum.2018.00389 google scholar
• Wang, J., & Wang, M. (2021). Review of the emotional feature extraction and classification using EEG signals. Cognitive Robotics, 1, 29-40. https://doi. org/10.1016/j.cogr.2021.04.001 google scholar
• Wei, J., Zhao, L., Yan, G., Duan, R., & Li, D. (1998). The temporal and spatial features of event-related EEG spectral changes in 4 mental conditions. Electroencephalography and Clinical Neurophysiology, 106(5), 416-423. https://doi.org/10.1016/s0013-4694(97)00161-2 google scholar