{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T14:28:29Z","timestamp":1772202509927,"version":"3.50.1"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"publisher","award":["EEQ\/2019\/000624"],"award-info":[{"award-number":["EEQ\/2019\/000624"]}],"id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:p> Brain-Computer Interface is an emerging field that focuses on transforming brain data into machine commands. EEG-based BCI is widely used due to the non-invasive nature of Electroencephalogram. Classification of EEG signals is one of the primary components in BCI applications. Steady-State Visually Evoked Potential (SSVEP) paradigms have gained importance because of lesser training time, higher precision, and improved information transfer rate compared to P300 and motor imagery paradigms. In this paper, a novel hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron classifier (APS-MLP) is proposed to improve the classification accuracy of SSVEP five classes viz. 6.66, 7.5, 8.57, 10 and 12 Hz, signals. Scaled Conjugate Gradient descent anchors the initial position of Particle Swarm Optimization. The best position, Pbest, of each particle initializes an SCG-MLP, the accuracy of APS-MLP is obtained by averaging the accuracies of each SCG-MLP. The proposed method is compared with standard classifiers namely, k-NN, SVM, LDA and MLP. In which, the proposed algorithm achieves improved training and testing accuracies of 88.69% and 95.4% respectively, which is 12\u201315% higher than the standard EEG-based BCI classifiers. The proposed algorithm is robust, with a Cohen\u2019s kappa coefficient of 0.96, and will be used in applications such as motion control and improving the quality of life for people with disabilities. <\/jats:p>","DOI":"10.1142\/s021821302340016x","type":"journal-article","created":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T08:44:33Z","timestamp":1684917873000},"source":"Crossref","is-referenced-by-count":4,"title":["Classification of Visually Evoked Potential EEG Using Hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron Classifier"],"prefix":"10.1142","volume":"32","author":[{"given":"Ravichander","family":"Janapati","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, SR University, Warangal, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vishwas","family":"Dalal","sequence":"additional","affiliation":[{"name":"Department of Cognitive Science, SR University, Warangal, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Usha","family":"Desai","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, SR University, Warangal, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rakesh","family":"Sengupta","sequence":"additional","affiliation":[{"name":"Department of Cognitive Science, SR University, Warangal, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shrirang A.","family":"Kulkarni","sequence":"additional","affiliation":[{"name":"School of Global Health Management and Informatics, University of Central Florida, Orlando, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6091-1880","authenticated-orcid":false,"given":"D. Jude","family":"Hemanth","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Karunya University, Coimbatore, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,5,22]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S021821302340016X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T08:45:33Z","timestamp":1684917933000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S021821302340016X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":0,"journal-issue":{"issue":"03","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["10.1142\/S021821302340016X"],"URL":"https:\/\/doi.org\/10.1142\/s021821302340016x","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5]]},"article-number":"2340016"}}