{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T17:48:01Z","timestamp":1784051281730,"version":"3.55.0"},"reference-count":68,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T00:00:00Z","timestamp":1746835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Qualcomm Ireland","award":["UNI-479522"],"award-info":[{"award-number":["UNI-479522"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hypoxic\u2013Ischemic Encephalopathy (HIE) occurs in patients who experience a decreased flow of blood and oxygen to the brain, with the optimal window for effective treatment being within the first six hours of life. This puts a significant demand on medical professionals to accurately and effectively grade the severity of the HIE present, which is a time-consuming and challenging task. This paper proposes a novel workflow for background EEG grading, implementing a blend of Digital Signal Processing (DSP) and Machine-Learning (ML) techniques. First, the EEG signal is transformed into an amplitude and frequency modulated audio spectrogram, which enhances its relevant signal properties. The difference between EEG Grades 1 and 2 is enhanced. A convolutional neural network is then designed as a regressor to map the input image into an EEG grade, by utilizing an optimized rounding module to leverage the monotonic relationship among the grades. Using a nested cross-validation approach, an accuracy of 89.97% was achieved, in particular improving the AUC of the most challenging grades, Grade 1 and Grade 2, to 0.98 and 0.96. The results of this study show that the proposed representation and workflow increase the potential for background grading of EEG signals, increasing the accuracy of grading background patterns that are most relevant for therapeutic intervention, across large windows of time.<\/jats:p>","DOI":"10.3390\/s25103007","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T06:17:08Z","timestamp":1747030628000},"page":"3007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Long-Term Neonatal EEG Modeling with DSP and ML for Grading Hypoxic\u2013Ischemic Encephalopathy Injury"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1261-6375","authenticated-orcid":false,"given":"Leah","family":"Twomey","sequence":"first","affiliation":[{"name":"Electrical and Electronic Engineering Department, University College Cork, T12 K8AF Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergi","family":"Gomez","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Engineering Department, University College Cork, T12 K8AF Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emanuel","family":"Popovici","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Engineering Department, University College Cork, T12 K8AF Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andriy","family":"Temko","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Engineering Department, University College Cork, T12 K8AF Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s13760-020-01308-3","article-title":"Pathophysiology of Hypoxic\u2013Ischemic Encephalopathy: A Review of the Past and a View on the Future","volume":"120","author":"Greco","year":"2020","journal-title":"Acta Neurol. Belg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1038\/pr.2013.206","article-title":"Intrapartum-Related Neonatal Encephalopathy Incidence and Impairment at Regional and Global Levels for 2010 with Trends from 1990","volume":"74","author":"Lee","year":"2013","journal-title":"Pediatr. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"711","DOI":"10.18203\/2349-3291.ijcp20211082","article-title":"Immediate Complications of Hypoxic-Ischemic Encephalopathy in Term Neonates with Resistive Index as Prognostic Factor","volume":"8","author":"Likitha","year":"2021","journal-title":"Int. J. Contemp. Pediatr."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, D.Y., Lee, I.C., Wang, X.A., and Wong, S.H. (2021). Early Biomarkers and Hearing Impairments in Patients with Neonatal Hypoxic\u2013Ischemic Encephalopathy. Diagnostics, 11.","DOI":"10.3390\/diagnostics11112056"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.ejpn.2020.04.003","article-title":"Minor Neurological Signs and Behavioural Function at Age 2 Years in Neonatal Hypoxic Ischaemic Encephalopathy (HIE)","volume":"27","author":"Edmonds","year":"2020","journal-title":"Eur. J. Paediatr. Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Okulu, E., Hirfano\u011flu, \u0130., Satar, M., Erdeve, O., Koc, E., Ozlu, F., Gokce, M., Armangil, D., Tun\u00e7, G., and Demirel, N. (2023). An Observational, Multicenter, Registry-Based Cohort Study of Turkish Neonatal Society in Neonates with Hypoxic Ischemic Encephalopathy. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0295759"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1159\/000518654","article-title":"Temporal Trends in the Severity and Mortality of Neonatal Hypoxic-Ischemic Encephalopathy in the Era of Hypothermia","volume":"118","author":"Arnaez","year":"2021","journal-title":"Neonatology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3685","DOI":"10.1080\/14767058.2018.1468881","article-title":"Neonatal Hypoxic\u2013Ischemic Encephalopathy: Emerging Therapeutic Strategies Based on Pathophysiologic Phases of the Injury","volume":"32","author":"Wang","year":"2019","journal-title":"J. Matern.-Fetal Neonatal Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1080\/20469047.2022.2163135","article-title":"Short-Term Outcome of Perinatal Hypoxic-Ischaemic Encephalopathy at Chiang Mai University Hospital, Thailand: A 15-Year Retrospective Study","volume":"42","author":"Malai","year":"2022","journal-title":"Paediatr. Int. Child Health"},{"key":"ref_10","first-page":"77","article-title":"Hypoxic Ischemic Encephalopathy (HIE) in Term and Preterm Infants","volume":"43","author":"Ristovska","year":"2022","journal-title":"PRILOZI"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jia, W., Lei, X., Dong, W., and Li, Q. (2018). Benefits of Starting Hypothermia Treatment within 6 h vs. 6\u201312 h in Newborns with Moderate Neonatal Hypoxic-Ischemic Encephalopathy. BMC Pediatr., 18.","DOI":"10.1186\/s12887-018-1013-2"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1097\/01.PCC.0000288714.61037.56","article-title":"Predicting Outcome in Children with Hypoxic Ischemic Encephalopathy","volume":"8","author":"Abend","year":"2007","journal-title":"Pediatr. Crit. Care Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1542\/peds.2012-1297","article-title":"Prognostic Tests in Term Neonates with Hypoxic-Ischemic Encephalopathy: A Systematic Review","volume":"131","author":"Offringa","year":"2013","journal-title":"Pediatrics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1001\/jamapediatrics.2014.3269","article-title":"Hypoxic-Ischemic Encephalopathy: A Review for the Clinician","volume":"169","author":"Weiss","year":"2015","journal-title":"JAMA Pediatr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1002\/cns3.20042","article-title":"Quantitative EEG in the Neonatal Intensive Care Unit: Current Application and Future Promise","volume":"1","author":"Keene","year":"2023","journal-title":"Ann. Child Neurol. Soc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1097\/WNP.0b013e3182872919","article-title":"Continuous EEG Monitoring in the Neonatal Intensive Care Unit","volume":"30","author":"McCoy","year":"2013","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1111\/j.1651-2227.2010.01809.x","article-title":"An International Survey of EEG Use in the Neonatal Intensive Care Unit","volume":"99","author":"Boylan","year":"2010","journal-title":"Acta Paediatr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.cnp.2018.03.002","article-title":"Visual EEG Reviewing Times with SCORE EEG","volume":"3","author":"Brogger","year":"2018","journal-title":"Clin. Neurophysiol. Pract."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.clinph.2015.05.024","article-title":"Grading Hypoxic\u2013Ischemic Encephalopathy Severity in Neonatal EEG Using GMM Supervectors and the Support Vector Machine","volume":"127","author":"Ahmed","year":"2016","journal-title":"Clin. Neurophysiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"046007","DOI":"10.1088\/1741-2552\/abe8ae","article-title":"Grading Hypoxic-Ischemic Encephalopathy in Neonatal EEG with Convolutional Neural Networks and Quadratic Time\u2013Frequency Distributions","volume":"18","author":"Raurale","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_21","first-page":"721","article-title":"Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG","volume":"122","author":"Guo","year":"2020","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.neucli.2020.12.003","article-title":"Quantitative Approach to Early Neonatal EEG Visual Analysis in Hypoxic-Ischemic Encephalopathy Severity: Bridging the Gap between Eyes and Machine","volume":"51","author":"Lacan","year":"2021","journal-title":"Neurophysiol. Clin."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, S., Marnane, W.P., Boylan, G.B., and Lightbody, G. (2023). Neonatal Hypoxic-Ischemic Encephalopathy Grading from Multi-Channel EEG Time-Series Data Using a Fully Convolutional Neural Network. Technologies, 11.","DOI":"10.3390\/technologies11060151"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s10470-021-01805-2","article-title":"An Automated Classification of EEG Signals Based on Spectrogram and CNN for Epilepsy Diagnosis","volume":"108","author":"Mandhouj","year":"2021","journal-title":"Analog. Integr. Circuits Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.seizure.2019.07.009","article-title":"Automated Spectrographic Seizure Detection Using Convolutional Neural Networks","volume":"71","author":"Yan","year":"2019","journal-title":"Seizure Eur. J. Epilepsy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/TTS.2023.3239526","article-title":"Automatic and Efficient Framework for Identifying Multiple Neurological Disorders From EEG Signals","volume":"4","author":"Tawhid","year":"2023","journal-title":"IEEE Trans. Technol. Soc."},{"key":"ref_27","unstructured":"(2025, May 08). HMS\u2014Harmful Brain Activity Classification. Available online: https:\/\/kaggle.com\/competitions\/hms-harmful-brain-activity-classification."},{"key":"ref_28","unstructured":"Torres, J., Buldain P\u00e9rez, J.D., and Beltr\u00e1n Bl\u00e1zquez, J.R. (2019). Arrhythmia Detection Using Convolutional Neural Models, Springer."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Le, M.D., Singh Rathour, V., Truong, Q.S., Mai, Q., Brijesh, P., and Le, N. (2021, January 27\u201330). Multi-Module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification. Proceedings of the 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Athens, Greece.","DOI":"10.1109\/BHI50953.2021.9508527"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"92871","DOI":"10.1109\/ACCESS.2019.2928017","article-title":"ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network","volume":"7","author":"Huang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1016\/j.clinph.2010.12.059","article-title":"Quantitative EEG Analysis in Neonatal Hypoxic Ischaemic Encephalopathy","volume":"122","author":"Korotchikova","year":"2011","journal-title":"Clin. Neurophysiol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, H., Ortigoza, E.B., Kota, S., Liu, Y., Zhang, R., and Chalak, L.F. (2022). Feasibility of EEG Phase-Amplitude Coupling to Stratify Encephalopathy Severity in Neonatal HIE Using Short Time Window. Brain Sci., 12.","DOI":"10.2139\/ssrn.4037957"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.jpeds.2021.09.058","article-title":"Neonatal Seizure Management: Is the Timing of Treatment Critical?","volume":"243","author":"Pavel","year":"2022","journal-title":"J. Pediatr."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gomez, S., O\u2019Sullivan, M., Popovici, E., Mathieson, S., Boylan, G., and Temko, A. (2018). On Sound-Based Interpretation of Neonatal EEG. arXiv.","DOI":"10.1109\/ISSC.2018.8585349"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Loui, P., Koplin-Green, M., Frick, M., and Massone, M. (2014). Rapidly Learned Identification of Epileptic Seizures from Sonified EEG. Front. Hum. Neurosci., 8.","DOI":"10.3389\/fnhum.2014.00820"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1016\/j.clinph.2007.01.025","article-title":"Event-Based Sonification of EEG Rhythms in Real Time","volume":"118","author":"Baier","year":"2007","journal-title":"Clin. Neurophysiol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chalak, L.F., Ferriero, D.M., Gunn, A.J., Robertson, N.J., Boylan, G.B., Molloy, E.J., Thoresen, M., and Inder, T.E. (2024). Mild HIE and Therapeutic Hypothermia: Gaps in Knowledge with under-Powered Trials. Pediatr. Res., 1\u20133.","DOI":"10.1038\/s41390-024-03537-1"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1007\/s10439-012-0710-5","article-title":"An Automated System for Grading EEG Abnormality in Term Neonates with Hypoxic-Ischaemic Encephalopathy","volume":"41","author":"Stevenson","year":"2013","journal-title":"Ann. Biomed. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1038\/s41597-023-02002-8","article-title":"Neonatal EEG Graded for Severity of Background Abnormalities in Hypoxic-Ischaemic Encephalopathy","volume":"10","author":"Mathieson","year":"2023","journal-title":"Sci. Data"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"O\u2019Toole, J.M., Mathieson, S.R., Magarelli, F., Marnane, W.P., Lightbody, G., and Boylan, G.B. (2022). Neonatal EEG Graded for Severity of Background Abnormalities. Zenodo.","DOI":"10.1038\/s41597-023-02002-8"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e459","DOI":"10.1542\/peds.2008-2190","article-title":"Early EEG Findings in Hypoxic-Ischemic Encephalopathy Predict Outcomes at 2 Years","volume":"124","author":"Murray","year":"2009","journal-title":"Pediatrics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/S0887-8994(03)00277-7","article-title":"Wavelet Analysis for Neonatal Electroencephalographic Seizures","volume":"29","author":"Kitayama","year":"2003","journal-title":"Pediatr. Neurol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1097\/WNP.0b013e3182872b24","article-title":"American Clinical Neurophysiology Society Standardized EEG Terminology and Categorization for the Description of Continuous EEG Monitoring in Neonates: Report of the American Clinical Neurophysiology Society Critical Care Monitoring Committee","volume":"30","author":"Tsuchida","year":"2013","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1123\/jab.13.2.226","article-title":"Reconstructing Digital Signals Using Shannon\u2019s Sampling Theorem","volume":"13","author":"Hamill","year":"1997","journal-title":"J. Appl. Biomech."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TASLP.2017.2778423","article-title":"Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge","volume":"26","author":"Mesaros","year":"2018","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/s43588-021-00183-z","article-title":"The Fast Continuous Wavelet Transformation (fCWT) for Real-Time, High-Quality, Noise-Resistant Time\u2013Frequency Analysis","volume":"2","author":"Arts","year":"2022","journal-title":"Nat. Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1119\/1.3226852","article-title":"Time Series Analysis with the Hilbert\u2013Huang Transform","volume":"77","author":"Donnelly","year":"2009","journal-title":"Am. J. Phys."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1109\/TBME.2009.2026735","article-title":"Time-Frequency Phase Analysis of Ictal EEG Recordings with the S-transform","volume":"56","author":"Pinnegar","year":"2009","journal-title":"IEEE Trans. Bio-Med. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s11004-017-9691-0","article-title":"Least-Squares Wavelet Analysis of Unequally Spaced and Non-stationary Time Series and Its Applications","volume":"49","author":"Ghaderpour","year":"2017","journal-title":"Math. Geosci."},{"key":"ref_50","unstructured":"McInnes, L., Healy, J., and Melville, J. (2020). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022). A ConvNet for the 2020s. arXiv.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_52","unstructured":"Wightman, R., Raw, N., Soare, A., Arora, A., Ha, C., Reich, C., Guan, F., Kaczmarzyk, J. (2023). Rwightman\/Pytorch-Image-Models: V0.8.10dev0 Release. Zenodo."},{"key":"ref_53","unstructured":"Loshchilov, I., and Hutter, F. (2017). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv."},{"key":"ref_54","unstructured":"Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A.G. (2019). Averaging Weights Leads to Wider Optima and Better Generalization. arXiv."},{"key":"ref_55","unstructured":"(2025, May 08). Automatic Mixed Precision\u2014PyTorch Tutorials 2.4.0+cu121 Documentation. Available online: https:\/\/pytorch.org\/tutorials\/recipes\/recipes\/amp_recipe.html."},{"key":"ref_56","unstructured":"(2025, May 08). PetFinder.My Adoption Prediction. Available online: https:\/\/kaggle.com\/competitions\/petfinder-adoption-prediction."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1093\/comjnl\/7.4.308","article-title":"A Simplex Method for Function Minimization","volume":"7","author":"Nelder","year":"1965","journal-title":"Comput. J."},{"key":"ref_58","unstructured":"(2008). Mean Squared Error. The Concise Encyclopedia of Statistics, Springer."},{"key":"ref_59","unstructured":"Draper, N.R., and Smith, H. (1998). On Worthwhile Regressions, Big F\u2019s, and R2. Applied Regression Analysis, John Wiley & Sons, Ltd.. Chapter 11."},{"key":"ref_60","unstructured":"Powers, D.M.W. (2020). Evaluation: From Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A Systematic Analysis of Performance Measures for Classification Tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_62","first-page":"1","article-title":"The Truth of the F-measure","volume":"1","author":"Sasaki","year":"2007","journal-title":"Teach Tutor Mater"},{"key":"ref_63","unstructured":"(2025, May 08). A Coefficient of Agreement for Nominal Scales\u2014Jacob Cohen. Available online: https:\/\/journals.sagepub.com\/doi\/10.1177\/001316446002000104."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Singh, A., Mittal, S., Malhotra, P., and Srivastava, Y. (2020, January 11\u201313). Clustering Evaluation by Davies-Bouldin Index(DBI) in Cereal Data Using K-Means. Proceedings of the 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC48092.2020.ICCMC-00057"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Shahapure, K.R., and Nicholas, C. (2020, January 6\u20139). Cluster Quality Analysis Using Silhouette Score. Proceedings of the 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), Sydney, NSW, Australia.","DOI":"10.1109\/DSAA49011.2020.00096"},{"key":"ref_66","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ahmed, M.Z.I., Sinha, N., Ghaderpour, E., Phadikar, S., and Ghosh, R. (2023). A Novel Baseline Removal Paradigm for Subject-Independent Features in Emotion Classification Using EEG. Bioengineering, 10.","DOI":"10.3390\/bioengineering10010054"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Massar, H., Stergiadis, C., Nsiri, B., Drissi, T.B., and Klados, M.A. (2024). EMD-BSS: A Hybrid Methodology Combining Empirical Mode Decomposition and Blind Source Separation to Eliminate the Ocular Artifacts from EEG Recordings. Biomed. Signal Process. Control, 95.","DOI":"10.1016\/j.bspc.2024.106475"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/10\/3007\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:30:29Z","timestamp":1760031029000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/10\/3007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,10]]},"references-count":68,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["s25103007"],"URL":"https:\/\/doi.org\/10.3390\/s25103007","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,10]]}}}