{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:06:03Z","timestamp":1771261563688,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016560","name":"Iqra University","doi-asserted-by":"publisher","award":["er 015ME0-2"],"award-info":[{"award-number":["er 015ME0-2"]}],"id":[{"id":"10.13039\/100016560","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Motor imagery (MI)-based brain\u2013computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain\u2019s neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel\/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms\u2014an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain\u2013computer interfaces.<\/jats:p>","DOI":"10.3390\/s21196570","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:37:49Z","timestamp":1633901869000},"page":"6570","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8722-5787","authenticated-orcid":false,"given":"Muhammad Ahsan","family":"Awais","sequence":"first","affiliation":[{"name":"Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9306-6655","authenticated-orcid":false,"given":"Mohd Zuki","family":"Yusoff","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8815-6146","authenticated-orcid":false,"given":"Danish M.","family":"Khan","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia"},{"name":"Department of Telecommunications Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9812-0435","authenticated-orcid":false,"given":"Norashikin","family":"Yahya","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9638-6379","authenticated-orcid":false,"given":"Nidal","family":"Kamel","sequence":"additional","affiliation":[{"name":"Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2000-8398","authenticated-orcid":false,"given":"Mansoor","family":"Ebrahim","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Sciences, and Technology, Iqra University, Karachi 75500, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"011001","DOI":"10.1088\/1741-2552\/aaf12e","article-title":"A comprehensive review of EEG-based brain\u2013computer interface paradigms","volume":"16","author":"Abiri","year":"2019","journal-title":"J. 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