{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:25:10Z","timestamp":1775539510541,"version":"3.50.1"},"reference-count":20,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T00:00:00Z","timestamp":1696377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["110-2221-E-002-110-MY3"],"award-info":[{"award-number":["110-2221-E-002-110-MY3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>One\u2019s working memory process is a fundamental cognitive activity which often serves as an indicator of brain disease and cognitive impairment. In this research, the approach to evaluate working memory ability by means of electroencephalography (EEG) analysis was proposed. The result shows that the EEG signals of subjects share some characteristics when performing working memory tasks. Through correlation analysis, a working memory model describes the changes in EEG signals within alpha, beta and gamma waves, which shows an inverse tendency compared to Zen meditation. The working memory ability of subjects can be predicted using multi-linear support vector regression (SVR) with fuzzy C-mean (FCM) clustering and knowledge-based fuzzy support vector regression (FSVR), which reaches the mean square error of 0.6 in our collected data. The latter, designed based on the working memory model, achieves the best performance. The research provides the insight of the working memory process from the EEG aspect to become an example of cognitive function analysis and prediction.<\/jats:p>","DOI":"10.3390\/s23198246","type":"journal-article","created":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T03:05:19Z","timestamp":1696475119000},"page":"8246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Working Memory Ability Evaluation Based on Fuzzy Support Vector Regression"],"prefix":"10.3390","volume":"23","author":[{"given":"Jia-Hsun","family":"Lo","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han-Pang","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Su-Ching","family":"Sung","sequence":"additional","affiliation":[{"name":"Department of Gerontology and Health Care Management, Chang Gung University of Science and Technology, Taoyuan City 33303, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Charbonnier, S., Roy, R., Dole\u017ealov\u00e1, R., Campagne, A., and Bonnet, S. (2016). Estimation of Working Memory Load Using EEG Connectivity Measures, Conference of Biosignals.","DOI":"10.5220\/0005638201220128"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kuanar, S., Athitsos, V., Pradhan, N., Mishra, A., and Rao, K.R. (2018, January 15\u201320). Cognitive Analysis of Working Memory Load from EEG, by A Deep Recurrent Neural Network. Proceedings of the IEEE International Conference on Acoustics, Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462243"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Barua, S., Ahmed, M.U., and Begum, S. (2020). Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification. Brain Sci., 10.","DOI":"10.3390\/brainsci10080526"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5422","DOI":"10.1109\/JSEN.2023.3237383","article-title":"Automatic Eyeblink and Muscular Artifact Detection and Removal From EEG Signals Using k-Nearest Neighbor Classifier and Long Short-Term Memory Networks","volume":"23","author":"Ghosh","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/TAFFC.2019.2922912","article-title":"From Regional to Global Brain: A Novel Hierarchical Spatial-Temporal Neural Network Model for EEG Emotion Recognition","volume":"13","author":"Li","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.biopsycho.2004.03.002","article-title":"Frontal EEG asymmetry as a moderator and mediator of emotion","volume":"67","author":"Coan","year":"2004","journal-title":"Biol. Psychol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1038\/nn.4135","article-title":"Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity","volume":"18","author":"Finn","year":"2015","journal-title":"Nat. Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17031","DOI":"10.1038\/s41598-022-21384-0","article-title":"Person-identifying brainprints are stably embedded in EEG mindprints","volume":"12","author":"Yang","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"630","DOI":"10.3390\/signals4030034","article-title":"Early Signatures of Brain Injury in the Preterm Neonatal EEG","volume":"4","author":"Abbasi","year":"2023","journal-title":"Signals"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108552","DOI":"10.1016\/j.jneumeth.2019.108552","article-title":"Prediction of working memory ability based on EEG by functional data analysis","volume":"333","author":"Zhang","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Changoluisa, V., Poch, C., and Rodriguez, P.C.F.B. (2022). Predicting Working Memory performance based on specific individual EEG spatiotemporal features. bioRxiv.","DOI":"10.1101\/2022.05.06.490941"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107827","DOI":"10.1016\/j.dib.2022.107827","article-title":"A dataset of EEG recordings from 47 participants collected during a virtual reality working memory task where attention was cued by a social avatar and non-social stick cue","volume":"41","author":"Gregory","year":"2022","journal-title":"Data Brief"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"David, W. (2016). Wechsler Intelligence Scale for Children, Psychological Corporation.","DOI":"10.1037\/t79544-000"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1017\/S1041610294001602","article-title":"The Cognitive Abilities Screening Instrument (CASI): A Practical Test for Cross-Cultural Epidemiological Studies of Dementia","volume":"6","author":"Teng","year":"2005","journal-title":"Int. Psychogeriatrics"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3547","DOI":"10.1161\/STROKEAHA.115.011226","article-title":"Montreal Cognitive Assessment One Cutoff Never Fits All","volume":"6","author":"Wong","year":"2015","journal-title":"Stroke"},{"key":"ref_16","unstructured":"Lo, J., Huang, C., and Huang, H. (2022, January 18\u201320). Dementia Diagnosis with Electroencephalography. Proceedings of the International Automatic Control Conference of CACS, Taipei, Taiwan."},{"key":"ref_17","first-page":"203","article-title":"Support Vector Regression","volume":"11","author":"Basak","year":"2007","journal-title":"Process. Lett. Rev"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"184997","DOI":"10.1109\/ACCESS.2020.3030083","article-title":"Robust Multi-Linear Fuzzy SVR Designed With the Aid of Fuzzy C-Means Clustering Based on Insensitive Data Information","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"167","DOI":"10.4015\/S1016237205000263","article-title":"Meditation EEG Interpretation Based on Novel Fuzzy-Merging Strategies and Wavelet Features","volume":"17","author":"Chang","year":"2005","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4015\/S1016237206000026","article-title":"F-VEP and Alpha-suppressed EEG Physiological Evidence of Inner-light Perception During Zen Meditation","volume":"18","author":"Chang","year":"2006","journal-title":"Biomed. Eng. Appl. Basis Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8246\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:05:25Z","timestamp":1760130325000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8246"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,4]]},"references-count":20,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23198246"],"URL":"https:\/\/doi.org\/10.3390\/s23198246","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,4]]}}}