{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T09:40:11Z","timestamp":1785577211147,"version":"3.56.0"},"reference-count":65,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T00:00:00Z","timestamp":1650499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Qatar National Research Fund (QNRF)","award":["NPRP12S-0227-190164"],"award-info":[{"award-number":["NPRP12S-0227-190164"]}]},{"name":"International Research Collaboration Co-Fund (IRCC)","award":["IRCC-2021-001"],"award-info":[{"award-number":["IRCC-2021-001"]}]},{"DOI":"10.13039\/501100004515","name":"Universiti Kebangsaan Malaysia","doi-asserted-by":"publisher","award":["DPK-2021-001"],"award-info":[{"award-number":["DPK-2021-001"]}],"id":[{"id":"10.13039\/501100004515","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove\/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average \u00a0(29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average \u00a0(53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average \u00a0and \u00a0values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average \u00a0(16.55 dB, utilizing db1 wavelet packet) and largest average \u00a0(41.40%, using fk8 wavelet packet). The highest average \u00a0and \u00a0using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average \u00a0also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both \u00a0and \u00a0values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.<\/jats:p>","DOI":"10.3390\/s22093169","type":"journal-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T03:46:11Z","timestamp":1650512771000},"page":"3169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis"],"prefix":"10.3390","volume":"22","author":[{"given":"Md Shafayet","family":"Hossain","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0744-8206","authenticated-orcid":false,"given":"Muhammad E. H.","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0459-0365","authenticated-orcid":false,"given":"Mamun Bin Ibne","family":"Reaz","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4819-863X","authenticated-orcid":false,"given":"Sawal Hamid Md","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9060-0346","authenticated-orcid":false,"given":"Ahmad Ashrif A.","family":"Bakar","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serkan","family":"Kiranyaz","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7068-9112","authenticated-orcid":false,"given":"Amith","family":"Khandakar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9795-6137","authenticated-orcid":false,"given":"Mohammed","family":"Alhatou","sequence":"additional","affiliation":[{"name":"Neuromuscular Division, Department of Neurology, Al-Khor Branch, Hamad General Hospital, Doha 3050, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rumana","family":"Habib","sequence":"additional","affiliation":[{"name":"Department of Neurology, BIRDEM General Hospital, Dhaka 1000, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9794-411X","authenticated-orcid":false,"given":"Muhammad Maqsud","family":"Hossain","sequence":"additional","affiliation":[{"name":"NSU Genome Research Institute (NGRI), North South University, Dhaka 1229, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1212\/01.wnl.0000243257.85592.9a","article-title":"Electroencephalography: Basic principles, clinical applications, and related fields","volume":"67","author":"Henry","year":"2006","journal-title":"Neurology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1212\/WNL.49.1.277","article-title":"Assessment of digital EEG, quantitative EEG, and EEG brain mapping: Report of the American Academy of Neurology and the American Clinical Neurophysiology Society","volume":"49","author":"Nuwer","year":"1997","journal-title":"Neurology"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shoeb, A., Guttag, J., Schachter, S., Schomer, D., Bourgeois, B., and Treves, S.T. 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