Brainwave Signals of Language Learning Anxiety
A systematic review of EEG studies
DOI:
https://doi.org/10.36733/sphota.v18i1.13679Keywords:
brainwave signals, EEG studies, language learning anxiety, cognitive neuroscienceAbstract
Language learning anxiety poses a significant challenge in Indonesian education; however, traditional methods frequently neglect its neurophysiological foundations. Therefore, it is imperative to investigate learning anxiety through EEG-based brainwave monitoring. This review study examines how EEG assessments identify specific brainwave types, brain regions, and treatments for learning anxiety. A systematic literature review (SLR) approach adhered to PRISMA guidelines. The flow diagram results indicate that out of 250 articles identified from Scopus-indexed journals, 140 passed the initial screening stage, 90 were selected for further review, and only 30 met the inclusion criteria. These articles were categorized into three main themes: brainwave types, brain regions, and EEG treatment for anxiety. The findings indicated that EEG studies consistently and accurately detect learning anxiety through increased beta wave activity and decreased alpha and theta wave activity, particularly in the prefrontal cortex, which is linked to cognitive stress and emotional regulation dysfunction. Additionally, significant brain activation was observed in the frontal, prefrontal, parietal, and temporal regions, as well as in the limbic system. EEG-based interventions, such as neurofeedback and vagus nerve stimulation, were also found to be effective in reducing anxiety through non-pharmacological means. However, current research is still limited to experimental studies conducted in laboratory settings, which do not fully capture the dynamic nature of learning anxiety in real classroom contexts for better input of teaching strategies. Future studies should aim to integrate EEG with other affective factors and test the effectiveness of neuroscience-based interventions in authentic and sustainable learning environments.
References
Aldayel, M., & Al-Nafjan, A. (2024). A comprehensive exploration of machine learning techniques for EEG-based anxiety detection. PeerJ Computer Science, 10, e1829. https://doi.org/10.7717/peerj-cs.1829
Al-Ezzi, A., Al-Shargabi, A. A., Al-Shargie, F., & Zahary, A. T. (2022). Complexity analysis of EEG in patients with social anxiety disorder using fuzzy entropy and machine learning techniques. IEEE Access, 10, 39926–39938. https://doi.org/10.1109/ACCESS.2022.3165199
Al-Ezzi, A., Yahya, N., Kamel, N., Faye, I., Alsaih, K., & Gunaseli, E. (2021). Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity. IEEE Access, 9, 86899–86913. https://doi.org/10.1109/ACCESS.2021.3089358
Ancillon, L., Elgendi, M., & Menon, C. (2022). Machine learning for anxiety detection using biosignals: A review. Em Diagnostics (Vol. 12, Número 8). https://doi.org/10.3390/diagnostics12081794
Apicella, A., Arpaia, P., Frosolone, M., Improta, G., Moccaldi, N., & Pollastro, A. (2022). EEG-based measurement system for monitoring student engagement in learning 4.0. Scientific Reports, 12(1), 5857. https://doi.org/10.1038/s41598-022-09578-y
Aristizabal, J. P., Pereira Jr, A., Gomes, B. D., Goulart, P. R. K., de Souza, W. C., & Rocha, F. A. (2024). Neurofeedback beta down training in women with high state-trait anxiety and elevated beta patterns in temporal lobes: A pilot study. NeuroRegulation, 11(3), 284. https://doi.org/10.15540/nr.11.3.284
Arsalan, A., Majid, M., & Anwar, S. M. (2020). Electroencephalography Based Machine Learning Framework for Anxiety Classification BT - Intelligent Technologies and Applications (I. S. Bajwa, T. Sibalija, & D. N. A. Jawawi, Orgs.; p. 187–197). Springer Singapore.
Aznar, S., & Klein, A. B. (2013). Regulating prefrontal cortex activation: An emerging role for the 5-HT2A serotonin receptor in the modulation of emotion-based actions? Molecular Neurobiology, 48(3), 841–853. https://doi.org/10.1007/s12035-013-8472-0
Bagheri, M., & Power, S. D. (2020). EEG-based detection of mental workload level and stress: the effect of variation in each state on classification of the other. Journal of Neural Engineering, 17(5), 56015. https://doi.org/10.1088/1741-2552/abbc27
Bennett, C. (2018). The impact of a neurofeedback training intervention on college Ssudents’ levels of anxiety, stress, depression, and cortisol. http://purl.fcla.edu/fcla/etd/CFE0007052
Berggren, N., & Derakshan, N. (2013). Attentional control deficits in trait anxiety: Why you see them and why you don’t. Biological Psychology, 92(3), 440–446. https://doi.org/10.1016/j.biopsycho.2012.03.007
Bremner, J. D., Gurel, N. Z., Wittbrodt, M. T., Shandhi, M. H., Rapaport, M. H., Nye, J. A., Pearce, B. D., Vaccarino, V., Shah, A. J., Park, J., Bikson, M., & Inan, O. T. (2020). Application of noninvasive vagal nerve stimulation to stress-related psychiatric disorders. Em Journal of Personalized Medicine (Vol. 10, Número 3). https://doi.org/10.3390/jpm10030119
Çapan, S. A., & Pektas, R. (2013). An Empirical Analysis of the Relationship between Foreign Language Reading Anxiety and Reading Strategy Training. English Language Teaching, 6(12), 181–188. https://doi.org/10.5539/elt.v6n12p181
Chang, E., Billinghurst, M., & Yoo, B. (2023). Brain activity during cybersickness: a scoping review. Virtual Reality, 27(3), 2073–2097. https://doi.org/10.1007/s10055-023-00795-y
Cheema, K. (2018). Investigating the neural circuitry of spelling in reading impairments: A functional connectivity approach. https://doi.org/10.48730/33n0-px64
Chellappa, S. L., & Aeschbach, D. (2022). Sleep and anxiety: From mechanisms to interventions. Sleep Medicine Reviews, 61, 101583. https://doi.org/10.1016/j.smrv.2021.101583
Chow, B. W.-Y., Mo, J., & Dong, Y. (2021). Roles of reading anxiety and working memory in reading comprehension in English as a second language. Learning and Individual Differences, 92, 102092. https://doi.org/10.1016/j.lindif.2021.102092
da Silva, F. L. (2022). EEG: Origin and measurement BT - EEG - fMRI: Physiological basis, technique, and applications (C. Mulert & L. Lemieux, Orgs.; p. 23–48). Springer International Publishing. https://doi.org/10.1007/978-3-031-07121-8_2
Dehghani, A., Soltanian-Zadeh, H., & Hossein-Zadeh, G.-A. (2023). Neural modulation enhancement using connectivity-based EEG neurofeedback with simultaneous fMRI for emotion regulation. NeuroImage, 279, 120320. https://doi.org/10.1016/j.neuroimage.2023.120320
Desai, R., Tailor, A., & Bhatt, T. (2015). Effects of yoga on brain waves and structural activation: A review. Complementary Therapies in Clinical Practice, 21(2), 112–118. https://doi.org/10.1016/j.ctcp.2015.02.002
Dias, S. B., Jelinek, H. F., & Hadjileontiadis, L. J. (2024). Wearable neurofeedback acceptance model for students’ stress and anxiety management in academic settings. Plos one, 19(10), e0304932. https://doi.org/10.1371/journal.pone.0304932
Edwards, A. A., Daucourt, M. C., Hart, S. A., & Schatschneider, C. (2023). Measuring reading anxiety in college students. Reading and Writing, 36(5), 1145–1180. https://doi.org/10.1007/s11145-022-10324-z
Ehrlich, T. J. (2020). Worry and the Functional Connectivity of the Central Executive and Salience Networks. Palo Alto University.
Fishstrom, S., Capin, P., Fall, A.-M., Roberts, G., Grills, A. E., & Vaughn, S. (2024). Understanding the relation between reading and anxiety among upper elementary students with reading difficulties. Annals of Dyslexia, 74(1), 123–141. https://doi.org/10.1007/s11881-024-00299-7
García‐Monge, A., Rodríguez‐Navarro, H., Bores‐García, D., & González‐Calvo, G. (2024). Electroencephalography in naturalistic and semi‐naturalistic educational contexts: A systematic review. Review of Education, 12(3), e70020. https://doi.org/10.1002/rev3.70020
Gerber, C., & Matuschek, P. (2023). Neural Mechanisms of Mindfulness-Based Interventions in Anxiety Disorders: A Systematic Review. Archives of Clinical Psychiatry, 50(6). https://archivespsy.com/menu-script/index.php/ACF/article/view/2182
Gruzelier, J. (2009). A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cognitive Processing, 10(1), 101–109. https://doi.org/10.1007/s10339-008-0248-5
Hammond, D. C. (2005). Neurofeedback with anxiety and affective disorders. Child and Adolescent Psychiatric Clinics, 14(1), 105–123. https://doi.org/10.1016/j.chc.2004.07.008
Hein, T. P., & Herrojo Ruiz, M. (2022). State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning. NeuroImage, 249, 118895. https://doi.org/10.1016/j.neuroimage.2022.118895
Heinrich, H., Gevensleben, H., & Strehl, U. (2007). Annotation: Neurofeedback–train your brain to train behaviour. Journal of Child Psychology and Psychiatry, 48(1), 3–16. https://doi.org/10.1111/j.1469-7610.2006.01665.x
Hobson, J. A., & Pace-Schott, E. F. (2002). The cognitive neuroscience of sleep: neuronal systems, consciousness and learning. Nature Reviews Neuroscience, 3(9), 679–693. https://doi.org/10.1038/nrn915
Horwitz, E. K., Tallon, M., & Luo, H. (2010). Foreign language anxiety. Anxiety in schools: The causes, consequences, and solutions for academic anxieties, 2, 96–115.
Jaiswal, S., Tsai, S.-Y., Juan, C.-H., Muggleton, N. G., & Liang, W.-K. (2019). Low delta and high alpha power are associated with better conflict control and working memory in high mindfulness, low anxiety individuals. Social Cognitive and Affective Neuroscience, 14(6), 645–655. https://doi.org/10.1093/scan/nsz038
Jiménez-Mijangos, L. P., Rodríguez-Arce, J., Martínez-Méndez, R., & Reyes-Lagos, J. J. (2023). Advances and challenges in the detection of academic stress and anxiety in the classroom: A literature review and recommendations. Education and Information Technologies, 28(4), 3637–3666. https://doi.org/10.1007/s10639-022-11324-w
Kaplan, P. W., & Rossetti, A. O. (2011). EEG Patterns and Imaging Correlations in Encephalopathy: Encephalopathy Part II. Journal of Clinical Neurophysiology, 28(3). https://journals.lww.com/clinicalneurophys/fulltext/2011/06000/eeg_patterns_and_imaging_correlations_in.1.aspx
Khosrowabadi, R., Quek, C., Ang, K. K., Tung, S. W., & Heijnen, M. (2011). A brain-Ccomputer interface for classifying EEG correlates of chronic mental stress. The 2011 International Joint Conference on Neural Networks, 757–762. https://doi.org/10.1109/IJCNN.2011.6033297
Kianinezhad, N. (2024). Educational Background and Workplace Context: Shaping Iranian EFL Teachers’ Attitudes Towards Online Teaching. Studies in Humanities and Education, 5(1), 29–43. https://doi.org/10.48185/she.v5i1.878
Kishore Kanna, R., Athawale, S. V, Naniwadekar, M. Y., Choudhari, C. S., Talhar, N. R., & Dhengre, S. (2024). Anxiety controlling application using EEG neurofeedback system. EAI Endorsed Transactions on Pervasive Health and Technology, 10(SE-Technical Article). https://doi.org/10.4108/eetpht.10.5432
Knyazev, G. G., Savostyanov, A. N., & Levin, E. A. (2005). Uncertainty, anxiety, and brain oscillations. Neuroscience Letters, 387(3), 121–125. https://doi.org/10.1016/j.neulet.2005.06.016
Kuper, N., Käckenmester, W., & Wacker, J. (2019). Resting frontal EEG asymmetry and personality traits: A meta–analysis. European Journal of Personality, 33(2), 154–175. https://doi.org/10.1002/per.2197
Lee, J., Hwang, J. Y., Park, S. M., Jung, H. Y., Choi, S.-W., Kim, D. J., Lee, J.-Y., & Choi, J.-S. (2014). Differential resting-state EEG patterns associated with comorbid depression in Internet addiction. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 50, 21–26. https://doi.org/10.1016/j.pnpbp.2013.11.016
Li, Y., Zhang, L., Shiau, W.-L., Xu, L., & Liu, Q. (2023). Psychophysiological responses to mobile reading: evidence from frontal EEG signals under a distracting reading environment and different text genres. Information Technology & People, 36(3), 1048–1075. https://doi.org/10.1108/ITP-02-2021-0111
Liew, J., Erbeli, F., Nyanamba, J. M., & Li, D. (2020). Pathways to Reading Competence: Emotional Self-regulation, Literacy Contexts, and Embodied Learning Processes. Reading Psychology, 41(7), 633–659. https://doi.org/10.1080/02702711.2020.1783145
Lin, B., Guo, B., Zhuang, L., Zhang, D., & Wang, F. (2024). Neural oscillations predict flow experience. Cognitive Neurodynamics, 19(1), 1. https://doi.org/10.1007/s11571-024-10205-x
Lin, I.-M., Chen, T.-C., Lin, H.-Y., Wang, S.-Y., Sung, J.-L., & Yen, C.-W. (2021). Electroencephalogram patterns in patients comorbid with major depressive disorder and anxiety symptoms: Proposing a hypothesis based on hypercortical arousal and not frontal or parietal alpha asymmetry. Journal of Affective Disorders, 282, 945–952. https://doi.org/10.1016/j.jad.2021.01.001
Liu, S., Wang, J., Li, S., & Cai, L. (2023). Epileptic Seizure Detection and Prediction in EEGs Using Power Spectra Density Parameterization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 3884–3894. https://doi.org/10.1109/TNSRE.2023.3317093
López, S. R. T. (2020). Raising awareness related to strategies to reduce the effects of foreign language anxiety. Greensboro College.
Luck, S. J. (2014). An introduction to the event-related potential technique. MIT press.
Marrero, H., Urrutia, M., Beltrán, D., Gámez, E., & Díaz, J. M. (2017). Understanding approach and avoidance in verbal descriptions of everyday actions: An ERP study. Cognitive, Affective, & Behavioral Neuroscience, 17(3), 612–624. https://doi.org/10.3758/s13415-017-0500-5
Martínez-Briones, B. J., Fernández-Harmony, T., Garófalo Gómez, N., Biscay-Lirio, R. J., & Bosch-Bayard, J. (2020). Working memory in children with learning Disorders: An EEG power spectrum analysis. Em Brain Sciences (Vol. 10, Número 11). https://doi.org/10.3390/brainsci10110817
Marzbani, H., Marateb, H. R., & Mansourian, M. (2016). Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications. Basic and Clinical Neuroscience, 7(2), 143–158. https://doi.org/10.15412/J.BCN.03070208
McGregor, N. W., Dimatelis, J. J., Van Zyl, P. J., Hemmings, S. M. J., Kinnear, C., Russell, V. A., Stein, D. J., & Lochner, C. (2018). A translational approach to the genetics of anxiety disorders. Behavioural Brain Research, 341, 91–97. https://doi.org/10.1016/j.bbr.2017.12.030
Mihajlović, V., Grundlehner, B., Vullers, R., & Penders, J. (2015). Wearable, wireless EEG solutions in daily life pplications: What are we missing? IEEE Journal of Biomedical and Health Informatics, 19(1), 6–21. https://doi.org/10.1109/JBHI.2014.2328317
Mishra, S., Seth, S., Jain, S., Pant, V., Parikh, J., Chugh, N., & Puri, Y. (2025). An emotionally intelligent haptic system – An efficient solution for anxiety detection and mitigation. Computer Methods and Programs in Biomedicine, 260, 108590. https://doi.org/10.1016/j.cmpb.2025.108590
Mogg, K., & Bradley, B. P. (2016). Anxiety and attention to threat: Cognitive mechanisms and treatment with attention bias modification. Behaviour Research and Therapy, 87, 76–108. https://doi.org/10.1016/j.brat.2016.08.001
Munteanu, D., & Munteanu, N. (2019). Comparison between assisted training and classical training in nonformal learning based on automatic attention measurement using a neurofeedback device. eLearning & Software for Education, 1. https://doi.org/10.12753/2066-026X-19-041
Owen, R. T. (2007). Pregabalin: its efficacy, safety and tolerability profile in generalized anxiety. Drugs of Today (Barcelona, Spain : 1998), 43(9), 601–610. https://doi.org/10.1358/dot.2007.43.9.1133188
Parsa, M., Rad, H. Y., Vaezi, H., Hossein-Zadeh, G.-A., Setarehdan, S. K., Rostami, R., Rostami, H., & Vahabie, A.-H. (2023). EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions. Computer Methods and Programs in Biomedicine, 240, 107683. https://doi.org/https://doi.org/10.1016/j.cmpb.2023.107683
Parums, D. V. (2021). Editorial: Review Articles, Systematic Reviews, Meta-Analysis, and the Updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Guidelines. Em Medical science monitor : international medical journal of experimental and clinical research (Vol. 27, p. e934475). https://doi.org/10.12659/MSM.934475
Patterson, G., Cummings, K. K., Jung, J., Okada, N. J., Tottenham, N., Bookheimer, S. Y., Dapretto, M., & Green, S. A. (2021). Effects of sensory distraction and salience priming on emotion identification in autism: an fMRI study. Journal of Neurodevelopmental Disorders, 13(1), 42. https://doi.org/10.1186/s11689-021-09391-0
Piccolo, L. R., Giacomoni, C. H., Julio-Costa, A., Oliveira, S., Zbornik, J., Haase, V. G., & Salles, J. F. (2017). Reading Anxiety in L1: Reviewing the Concept. Early Childhood Education Journal, 45(4), 537–543. https://doi.org/10.1007/s10643-016-0822-x
Putman, P., Verkuil, B., Arias-Garcia, E., Pantazi, I., & van Schie, C. (2014). EEG theta/beta ratio as a potential biomarker for attentional control and resilience against deleterious effects of stress on attention. Cognitive, Affective, & Behavioral Neuroscience, 14(2), 782–791. https://doi.org/10.3758/s13415-013-0238-7
Qiao, Z., Poppelaars, E. S., & Li, X. (2024). In the anticipation of threat: Neural regulatory activity indicated by delta-beta correlation and its relation to anxiety. Biological Psychology, 187, 108769. https://doi.org/10.1016/j.biopsycho.2024.108769
Rajab, A., Zakaria, W. Z. W., Rahman, H. A., Hosni, A. D., & Hassani, S. (2012). Reading Anxiety among Second Language Learners. Procedia - Social and Behavioral Sciences, 66, 362–369. https://doi.org/10.1016/j.sbspro.2012.11.279
Rajkumar, V. R. (2024). Investigating the relationships of the N400 and P600 event-related potentials to meaning integration and retrieval processes. University of Pittsburgh. https://d-scholarship.pitt.edu/47286/1/Vivek_Rajkumar_ETDFinal_2024.pdf
Ramani, B., Warish, P., & and Solanki, K. (2022). Stress Ocare : An advance IoMT based physiological data analysis for anxiety status prediction using cloud computing. Journal of Discrete Mathematical Sciences and Cryptography, 25(4), 1019–1029. https://doi.org/10.1080/09720529.2022.2072426
Ryherd, K., Jasinska, K., Van Dyke, J. A., Hung, Y.-H., Baron, E., Mencl, W. E., Zevin, J., & Landi, N. (2018). Cortical regions supporting reading comprehension skill for single words and discourse. Brain and Language, 186, 32–43. https://doi.org/10.1016/j.bandl.2018.08.001
Schürmann, M., & Başar, E. (2001). Functional aspects of alpha oscillations in the EEG. International Journal of Psychophysiology, 39(2), 151–158. https://doi.org/10.1016/S0167-8760(00)00138-0
Sharma, R., & Meena, H. K. (2024). Emerging trends in EEG signal processing: A systematic review. SN Computer Science, 5(4), 415. https://doi.org/10.1007/s42979-024-02773-w
Shen, F., Dai, G., Lin, G., Zhang, J., Kong, W., & Zeng, H. (2020). EEG-based emotion recognition using 4D convolutional recurrent neural network. Cognitive Neurodynamics, 14(6), 815–828. https://doi.org/10.1007/s11571-020-09634-1
Shine, J. M., Lewis, L. D., Garrett, D. D., & Hwang, K. (2023). The impact of the human thalamus on brain-wide information processing. Nature Reviews Neuroscience, 24(7), 416–430. https://doi.org/10.1038/s41583-023-00701-0
Spalding, D. M. (2021). Impacts of trait anxiety on attention and feature binding in visual working memory. https://doi.org/10.48730/33n0-px64
Sultan, D. R. A., & Fatima, D. R. N. (2025). Attention Modulation in reading. Lumina Literati Publishing. https://www.luminaliterati.com/
Takada, M., Nishida, K., Gondo, Y., Kikuchi-Hayakawa, H., Ishikawa, H., Suda, K., Kawai, M., Hoshi, R., Kuwano, Y., Miyazaki, K., & Rokutan, K. (2017). Beneficial effects of Lactobacillus casei strain Shirota on academic stress-induced sleep disturbance in healthy adults: a double-blind, randomised, placebo-controlled trial. Beneficial Microbes, 8(2), 153–162. https://doi.org/10.3920/BM2016.0150
Tan, P. Z., Bylsma, L. M., Silk, J. S., Siegle, G. J., Forbes, E. E., McMakin, D. L., Dahl, R. E., Ryan, N. D., & Ladouceur, C. D. (2022). Neural indices of performance monitoring are associated with daily emotional functioning in youth with anxiety disorders: An ERP and EMA study. International Journal of Psychophysiology, 178, 34–42. https://doi.org/10.1016/j.ijpsycho.2022.06.004
Tasyakuranti, A. N., Sumarti, H., Kusuma, H. H., Istikomah, I., & Prastyo, I. S. (2022). Analysis of The Effect of Istighfar Dhikr to Adolescent Anxiety at Beta Wave Activity Using Electroencephalogram (EEG) Examination. Jurnal Neutrino: Jurnal Fisika dan Aplikasinya, 15(1), 31–37. https://doi.org/10.18860/neu.v15i1.17270
Tenório, K., Pereira, E., Remigio, S., Costa, D., Oliveira, W., Dermeval, D., da Silva, A. P., Bittencourt, I. I., & Marques, L. B. (2022). Brain-imaging techniques in educational technologies: A systematic literature review. Education and Information Technologies, 27(1), 1183–1212. https://doi.org/10.1007/s10639-021-10608-x
Wang, C. H., Moreau, D., & Kao, S. C. (2019). From the lab to the field: Potential applications of dry EEG systems to understand the brain-behavior relationship in sports. Frontiers in Neuroscience, 13(AUG), 1–6. https://doi.org/10.3389/fnins.2019.00893
Wen, T. Y., & Aris, S. A. M. (2020). Electroencephalogram (EEG) stress analysis on alpha/beta ratio and theta/beta ratio. Indones. J. Electr. Eng. Comput. Sci, 17(1), 175–182. https://doi.org/10.11591/ijeecs.v17.i1.pp175-182
Wu, J.-J., Cui, Y., Yang, Y.-S., Kang, M.-S., Jung, S.-C., Park, H. K., Yeun, H.-Y., Jang, W. J., Lee, S., Kwak, Y. S., & Eun, S.-Y. (2014). Modulatory effects of aromatherapy massage intervention on electroencephalogram, psychological assessments, salivary cortisol and plasma brain-derived neurotrophic factor. Complementary Therapies in Medicine, 22(3), 456–462. https://doi.org/https://doi.org/10.1016/j.ctim.2014.04.001
Zaharchuk, H. A., Shevlin, A., & van Hell, J. G. (2021). Are our brains more prescriptive than our mouths? Experience with dialectal variation in syntax differentially impacts ERPs and behavior. Brain and Language, 218, 104949. https://doi.org/10.1016/j.bandl.2021.104949
Zhang, L., Cao, W., & Tsung, L. (2024). Unpacking changing emotions in multiple contexts: idiodynamic study of college students’ academic emotions. International Review of Applied Linguistics in Language Teaching, 0. https://doi.org/10.1515/iral-2023-0290








