{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T22:41:44Z","timestamp":1759963304244},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684567","type":"print"},{"value":"9781643684574","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,25]]},"abstract":"<jats:p>Resting-state electroencephalography pre-processing methods in machine learning studies into Parkinson\u2019s disease classification vary widely. Here three separate data sets were pre-processed to four different stages to investigate the effects on evaluation metrics, using power features from six regions-of-interest, Random Forest Classifiers for feature selection, and Support Vector Machines for classification. This showed muscle artefact inflated evaluation metrics, and alpha and theta band features produced the best results when fully pre-processing data.<\/jats:p>","DOI":"10.3233\/shti231254","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:30:35Z","timestamp":1706178635000},"source":"Crossref","is-referenced-by-count":2,"title":["Evaluating Effects of Resting-State Electroencephalography Data Pre-Processing on a Machine Learning Task for Parkinson\u2019s Disease"],"prefix":"10.3233","author":[{"given":"Robin","family":"Vlieger","sequence":"first","affiliation":[{"name":"Australian National University, Canberra, Australian Capital Territory, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Daskalaki","sequence":"additional","affiliation":[{"name":"Australian National University, Canberra, Australian Capital Territory, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deborah","family":"Apthorp","sequence":"additional","affiliation":[{"name":"University of New England, Armidale, New South Wales, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian J.","family":"Lueck","sequence":"additional","affiliation":[{"name":"Australian National University, Canberra, Australian Capital Territory, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanna","family":"Suominen","sequence":"additional","affiliation":[{"name":"Australian National University, Canberra, Australian Capital Territory, Australia"},{"name":"University of Turku, Turku, Southwest Finland, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231254","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:30:36Z","timestamp":1706178636000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231254"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231254","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}