{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T20:05:55Z","timestamp":1776456355068,"version":"3.51.2"},"reference-count":58,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,5]],"date-time":"2020-07-05T00:00:00Z","timestamp":1593907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user\u2019s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.<\/jats:p>","DOI":"10.3390\/s20133765","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T09:49:11Z","timestamp":1594028951000},"page":"3765","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8151-0413","authenticated-orcid":false,"given":"Muhammad Adeel","family":"Asghar","sequence":"first","affiliation":[{"name":"Telecommunication Engineering Department, University of Engineering and Technology, Taxila 47050, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8616-3959","authenticated-orcid":false,"given":"Muhammad Jamil","family":"Khan","sequence":"additional","affiliation":[{"name":"Telecommunication Engineering Department, University of Engineering and Technology, Taxila 47050, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4237-5890","authenticated-orcid":false,"given":"Muhammad","family":"Rizwan","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Engineering and Technology, Taxila 47050, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2284-0479","authenticated-orcid":false,"given":"Raja Majid","family":"Mehmood","sequence":"additional","affiliation":[{"name":"Information and Communication Technology Department, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang 43900, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6081-0852","authenticated-orcid":false,"given":"Sun-Hee","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul 02841, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tarnowski, P., Ko\u0142odziej, M., Majkowski, A., and Rak, R.J. 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