{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:36:51Z","timestamp":1782484611197,"version":"3.54.5"},"reference-count":98,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T00:00:00Z","timestamp":1680480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Korean government (MSIT)","doi-asserted-by":"publisher","award":["NRF2021R1I1A2059735"],"award-info":[{"award-number":["NRF2021R1I1A2059735"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent decades, the brain\u2013computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset\u2019s dimensionality, increase the computing effectiveness, and enhance the BCI\u2019s performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p &lt; 0.01) of 94.83 \u00b1 5.5%, 92.57 \u00b1 6.9%, and 85.66 \u00b1 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.<\/jats:p>","DOI":"10.3390\/s23073714","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T02:03:00Z","timestamp":1680573780000},"page":"3714","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0716-3932","authenticated-orcid":false,"given":"Amad","family":"Zafar","sequence":"first","affiliation":[{"name":"Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5300-189X","authenticated-orcid":false,"given":"Shaik Javeed","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7326-1813","authenticated-orcid":false,"given":"Muhammad Umair","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5632-5208","authenticated-orcid":false,"given":"Seung Won","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Khosrowabadi, R., Quek, C., Ang, K.K., Tung, S.W., and Heijnen, M. 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