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To address this issue, many studies have proposed application of machine learning techniques for early MCI diagnosis based on electroencephalography (EEG). In this study, a machine learning framework for MCI diagnosis is proposed in this study, which extracts spectral, functional connectivity, and nonlinear features from EEG signals. The sequential backward feature selection (SBFS) algorithm is used to select the best subset of features. Several classification models and different combinations of feature sets are measured to identify the best ones for the proposed framework. A dataset of 16 and 18 EEG data of normal and MCI subjects is used to validate the proposed system. Metrics including accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) are evaluated using 10-fold crossvalidation. An average AC of 99.4%, SE of 98.8%, SP of 100%, F1 of 99.4%, and FDR of 0% have been provided by the best performance of the proposed framework using the linear support vector machine (LSVM) classifier and the combination of all feature sets. The acquired results confirm that the proposed framework provides an accurate and robust performance for recognizing MCI cases and outperforms previous approaches. Based on the obtained results, it is possible to be developed in order to use as a computer-aided diagnosis (CAD) tool for clinical purposes.<\/jats:p>","DOI":"10.1155\/2022\/2014001","type":"journal-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T03:35:08Z","timestamp":1660275308000},"page":"1-17","source":"Crossref","is-referenced-by-count":23,"title":["Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3163-3893","authenticated-orcid":true,"given":"Reza Akbari","family":"Movahed","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Hamedan University of Technology, Hamedan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2733-6423","authenticated-orcid":true,"given":"Mohammadreza","family":"Rezaeian","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Hamedan University of Technology, Hamedan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","volume-title":"Dementia: a NICE-SCIE guideline on supporting people with dementia and their carers in health and social care","author":"National Collaborating Centre for Mental Health (UK)","year":"2007"},{"key":"2","first-page":"66","article-title":"Dementia and risk reduction: an analysis of protective and modifiable factors","author":"M. 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