{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:52:45Z","timestamp":1769842365756,"version":"3.49.0"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"vor","delay-in-days":353,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Functional near\u2010infrared spectroscopy (fNIRS) is a low\u2010cost and noninvasive method to measure the hemodynamic responses of cortical brain activities and has received great attention in brain\u2010computer interface (BCI) applications. In this paper, we present a method based on deep learning and the time\u2010frequency map (TFM) of fNIRS signals to classify the three motor execution tasks including right\u2010hand tapping, left\u2010hand tapping, and foot tapping. To simultaneously obtain the TFM and consider the correlation among channels, we propose to utilize the two\u2010dimensional discrete orthonormal Stockwell transform (2D\u2010DOST). The TFMs for oxygenated hemoglobin (HbO), reduced hemoglobin (HbR), and two linear combinations of them are obtained and then we propose three fusion schemes for combining their deep information extracted by the convolutional neural network (CNN). Two CNNs, LeNet and MobileNet, are considered and their structures are modified to maximize the accuracy. Due to the lack of enough signals for training CNNs, data augmentation based on the Wasserstein generative adversarial network (WGAN) is performed. Several simulations are performed to assess the performance of the proposed method in three\u2010class and binary scenarios. The results present the efficiency of the proposed method in different scenarios. Also, the proposed method outperforms the recently introduced methods.<\/jats:p>","DOI":"10.1155\/2023\/3178284","type":"journal-article","created":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T22:50:06Z","timestamp":1703112606000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Fusion of Deep Features from 2D\u2010DOST of fNIRS Signals for Subject\u2010Independent Classification of Motor Execution Tasks"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9963-7508","authenticated-orcid":false,"given":"Pouya","family":"Khani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8304-6394","authenticated-orcid":false,"given":"Vahid","family":"Solouk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2431-4920","authenticated-orcid":false,"given":"Hashem","family":"Kalbkhani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4291-1748","authenticated-orcid":false,"given":"Farid","family":"Ahmadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/8812844"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/1567567"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.03.049"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/5480760"},{"key":"e_1_2_9_5_2","doi-asserted-by":"crossref","unstructured":"HuveG. TakahashiK. andHashimotoM. Brain activity recognition with a wearable fNIRS using neural networks Proceedings of the 2017 IEEE international conference on mechatronics and automation (ICMA) August 2017 Takamatsu Japan IEEE 1573\u20131578.","DOI":"10.1109\/ICMA.2017.8016051"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aaaf82"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8121486"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2020.103589"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22072575"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2012.01.002"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22197623"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2023.3281855"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.1062889"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abf187"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5533565"},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","unstructured":"HiroyasuT. HanawaK. andYamamotoU. Gender classification of subjects from cerebral blood flow changes using Deep Learning Proceedings of the 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) December 2014 Orlando FL USA IEEE 229\u2013233.","DOI":"10.1109\/CIDM.2014.7008672"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abd2ca"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22051932"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/78.492555"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00341-z"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-01622-1_11"},{"key":"e_1_2_9_22_2","unstructured":"HowardA. G. ZhuM. ChenB. KalenichenkoD. WangW. WeyandT. AndreettoM. andAdamH. Mobilenets: efficient convolutional neural networks for mobile vision applications 2017 https:\/\/arxiv.org\/abs\/1704.04861."},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab6cb9"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2017.05.008"},{"key":"e_1_2_9_26_2","doi-asserted-by":"crossref","unstructured":"SiddiqueT.andMahmudM. S. Classification of fNIRS data under uncertainty: a Bayesian neural network approach Proceedings of the 2020 IEEE International Conference on E-health Networking Application and Services (HEALTHCOM) March 2021 Shenzhen China IEEE 1\u20134.","DOI":"10.1109\/HEALTHCOM49281.2021.9398971"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abb417"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2022.3140531"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2023.3330911"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13534-023-00291-x"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2022.3190431"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.3390\/computers9030072"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.3390\/s23136077"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2018.01117"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/3178284.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/3178284.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2023\/3178284","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:16:35Z","timestamp":1735622195000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2023\/3178284"}},"subtitle":[],"editor":[{"given":"Mohammad R.","family":"Khosravi","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["10.1155\/2023\/3178284"],"URL":"https:\/\/doi.org\/10.1155\/2023\/3178284","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"2023-09-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"3178284"}}