{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T15:03:37Z","timestamp":1784905417305,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Peltier Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sci.","Brain Sciences"],"abstract":"<jats:p>Background and Objectives: Individuals with Parkinson\u2019s disease (PD) experience deficits in working memory (WM) and sustained attention (ATTN), but diagnosing and monitoring these deficits remains challenging. This study compares machine learning (ML) classification models trained on EEG and pupillometry data from WM and ATTN tasks to identify task-specific and shared cognitive biomarkers of PD. Methods: EEG and pupillometry were recorded from PD patients and healthy controls (HC) during a visual change detection WM task and a continuous performance ATTN task. A standardized toolbox extracted 108 features, reduced via PCA and recursive elimination (RE) and classified using an SVM-RBF within a nested, 5-fold cross-validated pipeline, with class balancing (SMOTE) and feature selection performed strictly within training folds to prevent leakage. Results: On internal test folds, WM achieved 71% accuracy (F1 = 0.701) and ATTN achieved 73% (F1 = 0.699); on an independent hold-out set, WM achieved 63% accuracy (F1 = 0.626) and ATTN achieved 70% (F1 = 0.623). ATTN showed higher accuracy and precision, WM showed higher recall, and univariate analyses independently supported several top-ranked features (e.g., theta\/beta and alpha\/theta ratios); PAI and FAA, though top features in both tasks, reached univariate significance only in ATTN. These results indicate WM and ATTN yield complementary, task-linked neurophysiological signatures relevant to PD classification. Conclusion: At its current stage, this pipeline functions as a research tool for biomarker discovery rather than a clinical diagnostic, though larger, externally validated samples could support future screening and monitoring applications.<\/jats:p>","DOI":"10.3390\/brainsci16080781","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T13:53:26Z","timestamp":1784901206000},"page":"781","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comparison of Machine Learning Models for Classification of Parkinson\u2019s Disease During a Working Memory and Sustained Attention Task"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4456-8113","authenticated-orcid":false,"given":"Mercedes A.","family":"Terry","sequence":"first","affiliation":[{"name":"Biomedical Engineering Department, University of North Dakota, Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6749-265X","authenticated-orcid":false,"given":"Samuel A.","family":"Birkholz","sequence":"additional","affiliation":[{"name":"Department of Psychology, North Dakota State University, Fargo, ND 58102, USA"},{"name":"Department of Neurology, Sanford Health, Fargo, ND 58103, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey S.","family":"Johnson","sequence":"additional","affiliation":[{"name":"Department of Psychology, North Dakota State University, Fargo, ND 58102, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5631-1594","authenticated-orcid":false,"given":"Jau-Shin","family":"Lou","sequence":"additional","affiliation":[{"name":"Department of Neurology, Sanford Health, Fargo, ND 58103, USA"},{"name":"Department of Neurology, University of North Dakota School of Medicine and Health Science, Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3552-195X","authenticated-orcid":false,"given":"Asenath X. A.","family":"Huether","sequence":"additional","affiliation":[{"name":"Department of Psychology, Minnesota State Community and Technical College, Moorhead, MN 56560, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Keller","sequence":"additional","affiliation":[{"name":"Department of Neurology, Sanford Health, Fargo, ND 58103, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7257-0817","authenticated-orcid":false,"given":"Enrique","family":"Alvarez-Vazquez","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department, University of North Dakota, Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"ref_1","first-page":"S3","article-title":"The Emerging Evidence of the Parkinson Pandemic","volume":"8","author":"Dorsey","year":"2018","journal-title":"J. Park. Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.37349\/ent.2023.00036","article-title":"Pathophysiology of non-motor signs in Parkinsons disease: Some recent updating with brief presentation","volume":"3","author":"Radad","year":"2023","journal-title":"Explor. Neuroprot Ther."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1212\/01.wnl.0000180516.69442.95","article-title":"Cognitive profile of patients with newly diagnosed Parkinson disease","volume":"65","author":"Muslimovic","year":"2005","journal-title":"Neurology"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aarsland, D., Batzu, L., Halliday, G.M., Geurtsen, G.J., Ballard, C., Chaudhuri, K.R., and Weintraub, D. (2021). Parkinson disease-associated cognitive impairment. Nat. Rev. Dis. Primers, 7.","DOI":"10.1038\/s41572-021-00280-3"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Biundo, R., Weis, L., and Antonini, A. (2016). Cognitive decline in Parkinson\u2019s disease: The complex picture. npj Park. Dis., 2.","DOI":"10.1038\/npjparkd.2016.18"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.parkreldis.2016.04.009","article-title":"Cognitive decline and quality of life in incident Parkinson\u2019s disease: The role of attention","volume":"27","author":"Lawson","year":"2016","journal-title":"Park. Relat. Disord."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1212\/WNL.0000000000000066","article-title":"Characterizing mild cognitive impairment in incident Parkinson disease: The ICICLE-PD study","volume":"82","author":"Yarnall","year":"2014","journal-title":"Neurology"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Goldman, J.G., Vernaleo, B.A., Camicioli, R., Dahodwala, N., Dobkin, R.D., Ellis, T., Galvin, J.E., Marras, C., Edwards, J., and Fields, J. (2018). Cognitive impairment in Parkinson\u2019s disease: A report from a multidisciplinary symposium on unmet needs and future directions to maintain cognitive health. npj Park. Dis., 4.","DOI":"10.1038\/s41531-018-0055-3"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1177\/08919887231195219","article-title":"Caregiver Burden in Parkinson Disease: A Scoping Review of the Literature from 2017\u20132022","volume":"37","author":"Aamodt","year":"2024","journal-title":"J. Geriatr. Psychiatry Neurol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.jamda.2016.03.004","article-title":"Contributory Factors to Caregiver Burden in Parkinson Disease","volume":"17","author":"Pieri","year":"2016","journal-title":"J. Am. Med. Dir. Assoc."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fang, C., Lv, L., Mao, S., Dong, H., and Liu, B. (2020). Cognition Deficits in Parkinson\u2019s Disease: Mechanisms and Treatment. Park. Dis., 2020.","DOI":"10.1155\/2020\/2076942"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fan, Y., Liang, X., Han, L., Shen, Y., Shen, B., Chen, C., Sun, Y., Wang, J., and Tang, Y. (2020). Determinants of Quality of Life According to Cognitive Status in Parkinson\u2019s Disease. Front. Aging Neurosci., 12.","DOI":"10.3389\/fnagi.2020.00269"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lawrence, B.J., Gasson, N., and Loftus, A.M. (2016). Prevalence and Subtypes of Mild Cognitive Impairment in Parkinson\u2019s Disease. Sci. Rep., 6.","DOI":"10.1038\/srep33929"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Robida, K.R., Politakis, V.A., Oblak, A., Ozimi\u010d, A.S., Burger, H., Pirto\u0161ek, Z., and Bon, J. (2023). Detecting Subtle Cognitive Impairment in Patients with Parkinson\u2019s Disease and Normal Cognition: A Novel Cognitive Control Challenge Task (C3T). Brain Sci., 13.","DOI":"10.3390\/brainsci13060961"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Labrador-Espinosa, M.A., Silva-Rodr\u00edguez, J., Mir, P., and Grothe, M.J. (2025). Data-driven characterization of distinct cognitive subtypes in Parkinson\u2019s disease dementia. npj Park. Dis., 11.","DOI":"10.1038\/s41531-025-00970-9"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Altham, C., Zhang, H., and Pereira, E. (2024). Machine learning for the detection and diagnosis of cognitive impairment in Parkinson\u2019s Disease: A systematic review. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0303644"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Anjum, F., Espinoza, A.I., Cole, R.C., Singh, A., May, P., Uc, E.Y., Dasgupta, S., and Narayanan, N.S. (2024). Resting-state EEG measures cognitive impairment in Parkinson\u2019s disease. npj Park. Dis., 10.","DOI":"10.1038\/s41531-023-00602-0"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aubin, P.M., Serackis, A., and Griskevicius, J. (2012, January 28\u201330). Support Vector Machine Classification of Parkinson\u2019s Disease, Essential Tremor and Healthy Control Subjects Based on Upper Extremity Motion. Proceedings of the 2012 International Conference on Biomedical Engineering and Biotechnology, Macau, China.","DOI":"10.1109\/iCBEB.2012.387"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"12784","DOI":"10.3390\/s140712784","article-title":"Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ma, Y., Ding, X., She, Q., Luo, Z., Potter, T., and Zhang, Y. (2016). Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization. Comput. Math. Methods Med., 2016.","DOI":"10.1155\/2016\/4941235"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Srinivasan, S., Ramadass, P., Mathivanan, S.K., Selvam, K.P., Shivahare, B.D., and Shah, M.A. (2024). Detection of Parkinson disease using multiclass machine learning approach. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-64004-9"},{"key":"ref_22","unstructured":"(2025, July 20). Deep Learning-Based Electroencephalography Analysis: A Systematic Review\u2014IOPscience. Available online: https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ab260c."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, Y., Cheng, Y., Xiao, Y., Shang, H., and Ou, R. (2025). The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis. J. Med. Internet Res., 27.","DOI":"10.2196\/59649"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kahya, M., Moon, S., Lyons, K.E., Pahwa, R., Akinwuntan, A.E., and Devos, H. (2018). Pupillary Response to Cognitive Demand in Parkinson\u2019s Disease: A Pilot Study. Front. Aging Neurosci., 10.","DOI":"10.3389\/fnagi.2018.00090"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.ijpsycho.2022.04.004","article-title":"Classifying brain states and pupillary responses associated with the processing of old and new information","volume":"176","author":"Araneda","year":"2022","journal-title":"Int. J. Psychophysiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1109\/TBME.2009.2016670","article-title":"Decision-level fusion of EEG and pupil features for single-trial visual detection analysis","volume":"56","author":"Qian","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"589","DOI":"10.5405\/jmbe.1605","article-title":"Support-vector-machine-based meditation experience evaluation using electroencephalography signals","volume":"34","author":"Lee","year":"2014","journal-title":"J. Med. Biol. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Quitadamo, L.R., Cavrini, F., Sbernini, L., Riillo, F., Bianchi, L., Seri, S., and Saggio, G. (2017). Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: A review. J. Neural Eng., 14.","DOI":"10.1088\/1741-2552\/14\/1\/011001"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1016\/j.ejor.2017.08.040","article-title":"High dimensional data classification and feature selection using support vector machines","volume":"265","author":"Ghaddar","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Nakayama, Y., Yata, K., and Aoshima, M. (2017). Support vector machine and its bias correction in high-dimension, low-sample-size settings. arXiv.","DOI":"10.1016\/j.jspi.2017.05.005"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Statnikov, A., Wang, L., and Aliferis, C.F. (2008). A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-319"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Razaque, A., Frej, M.B.H., Almi\u2019ani, M., Alotaibi, M., and Alotaibi, B. (2021). Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification. Sensors, 21.","DOI":"10.3390\/s21134431"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hemachandira, V.S., and Viswanathan, R. (2022). A Framework on Performance Analysis of Mathematical Model-Based Classifiers in Detection of Epileptic Seizure from EEG Signals with Efficient Feature Selection. J. Healthc. Eng., 2022.","DOI":"10.1155\/2022\/7654666"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shokrpour, S., MoghadamFarid, A., Abkenar, S.B., Kashani, M.H., Akbari, M., and Sarvizadeh, M. (2025). Machine learning for Parkinson\u2019s disease: A comprehensive review of datasets, algorithms, and challenges. npj Park. Dis., 11.","DOI":"10.1038\/s41531-025-01025-9"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Maitin, A.M., Mu\u00f1oz, J.P.R., and Garc\u00eda-Tejedor, \u00c1.J. (2022). Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson\u2019s Disease: A Systematic Review. Appl. Sci., 12.","DOI":"10.3390\/app12146967"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T.H., and Faubert, J. (2019). Deep learning-based electroencephalography analysis: A systematic review. arXiv.","DOI":"10.1088\/1741-2552\/ab260c"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, S., Dai, G., Li, J., Zhu, X., Huang, X., Li, Y., Tan, M., Wang, L., Fang, P., and Chen, X. (2024). An interpretable model based on graph learning for diagnosis of Parkinson\u2019s disease with voice-related EEG. npj Digit. Med., 7.","DOI":"10.1038\/s41746-023-00983-9"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.clinph.2024.11.013","article-title":"Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer\u2019s disease and normal aging","volume":"170","author":"Liao","year":"2025","journal-title":"Clin. Neurophysiol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ren, G., Kumar, A., Mahmoud, S.S., and Fang, Q. (2024). A deep neural network and transfer learning combined method for cross-task classification of error-related potentials. Front. Hum. Neurosci., 18.","DOI":"10.3389\/fnhum.2024.1394107"},{"key":"ref_40","unstructured":"Birkholz, S. (2025, July 11). Attention and Memory Function in Healthy Adults and Adults with Parkinson\u2019s Disease\u2014ProQuest. North Dakota State University, 2024. Available online: https:\/\/www.proquest.com\/openview\/3c6fa3478827442006c780ce2064f7d2\/1?pq-origsite=gscholar&cbl=18750&diss=y."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Terry, M., Birkholz, S., Lou, J.-S., Huether, A., Keller, J., and Vazquez Alvarez, E. (2026, January 30). A Toolbox for Task-Based EEG and Pupillometry Feature Extraction for Cognitive Biomarker Discovery in Parkinson\u2019s Disease. Available online: https:\/\/ssrn.com\/abstract=6086736.","DOI":"10.2139\/ssrn.6086736"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"da Rocha, T.F., Costa, V., Camargo, L., Fernandes, E.B., and Gianloren\u00e7o, A.C. (2025). Resting-State EEG Alpha Asymmetry as a Potential Marker of Clinical Features in Parkinson\u2019s Disease. J. Pers. Med., 15.","DOI":"10.3390\/jpm15070291"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.1002\/mds.28682","article-title":"Asymmetric Dopamine Transporter Loss Affects Cognitive and Motor Progression in Parkinson\u2019s Disease","volume":"36","author":"Fiorenzato","year":"2021","journal-title":"Mov. Disord."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Grissmann, S., Faller, J., Scharinger, C., Sp\u00fcler, M., and Gerjets, P. (2017). Electroencephalography Based Analysis of Working Memory Load and Affective Valence in an N-back Task with Emotional Stimuli. Front. Hum. Neurosci., 11.","DOI":"10.3389\/fnhum.2017.00616"}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/8\/781\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:09:38Z","timestamp":1784902178000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/8\/781"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,24]]},"references-count":44,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["brainsci16080781"],"URL":"https:\/\/doi.org\/10.3390\/brainsci16080781","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,24]]}}}