{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T11:46:28Z","timestamp":1789991188928,"version":"4.0.1"},"reference-count":101,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,8]],"date-time":"2017-06-08T00:00:00Z","timestamp":1496880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment","award":["Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment"],"award-info":[{"award-number":["Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA\u2013WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA\u2013WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA\u2013WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation X C o r r and peak signal to noise ratio ( P S N R ) (ANOVA, p \u02c2 0.05). The AICA\u2013WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA\u2013WT (ANOVA, p \u02c2 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing.<\/jats:p>","DOI":"10.3390\/s17061326","type":"journal-article","created":{"date-parts":[[2017,6,8]],"date-time":"2017-06-08T10:26:09Z","timestamp":1496917569000},"page":"1326","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA\u2013WT during Working Memory Tasks"],"prefix":"10.3390","volume":"17","author":[{"given":"Noor","family":"Al-Qazzaz","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic &amp; Systems Engineering, Faculty of Engineering &amp; Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia"},{"name":"Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, Baghdad University, Baghdad 47146, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sawal","family":"Hamid Bin Mohd Ali","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic &amp; Systems Engineering, Faculty of Engineering &amp; Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1759-0118","authenticated-orcid":false,"given":"Siti","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia"},{"name":"Malaysian Research Institute of Ageing (MyAgeing), Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd","family":"Islam","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Multimedia Universiti, MMU Cyberjaya, Selangor 63100, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2105-8725","authenticated-orcid":false,"given":"Javier","family":"Escudero","sequence":"additional","affiliation":[{"name":"Institute for Digital Communications, School of Engineering, The University of Edinburgh, Edinburgh EH9 3FB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,8]]},"reference":[{"key":"ref_1","first-page":"906038","article-title":"Role of EEG as Biomarker in the Early Detection and Classification of Dementia","volume":"2014","author":"Ali","year":"2014","journal-title":"Sci. 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