{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T06:19:50Z","timestamp":1773469190771,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University Researchers","award":["PNURSP2022R51"],"award-info":[{"award-number":["PNURSP2022R51"]}]},{"name":"Princess Nourah bint Abdulrahman University","award":["PNURSP2022R51"],"award-info":[{"award-number":["PNURSP2022R51"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional advertising techniques seek to govern the consumer\u2019s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers\u2019 actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s22249744","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T03:32:32Z","timestamp":1670902352000},"page":"9744","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications"],"prefix":"10.3390","volume":"22","author":[{"given":"Syed Mohsin Ali","family":"Shah","sequence":"first","affiliation":[{"name":"Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0504-3558","authenticated-orcid":false,"given":"Syed Muhammad","family":"Usman","sequence":"additional","affiliation":[{"name":"Department of Creative Technologies, Faculty of Computing and AI, Air University, Islamabad 44000, Pakistan"}]},{"given":"Shehzad","family":"Khalid","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Bahria University, Islamabad 44000, Pakistan"}]},{"given":"Ikram Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering, The University of West London, London W5 5RF, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2891-7844","authenticated-orcid":false,"given":"Aamir","family":"Anwar","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering, The University of West London, London W5 5RF, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1523-1330","authenticated-orcid":false,"given":"Saddam","family":"Hussain","sequence":"additional","affiliation":[{"name":"School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5406-0389","authenticated-orcid":false,"given":"Syed Sajid","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2571-1848","authenticated-orcid":false,"given":"Hela","family":"Elmannai","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}]},{"given":"Abeer D.","family":"Algarni","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}]},{"given":"Waleed","family":"Manzoor","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Bahria University, Islamabad 44000, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"604639","DOI":"10.3389\/fnhum.2020.604639","article-title":"Recognition of consumer preference by analysis and classification EEG signals","volume":"14","author":"Aldayel","year":"2021","journal-title":"Front. Hum. Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19087","DOI":"10.1007\/s11042-017-4580-6","article-title":"Analysis of EEG signals and its application to neuromarketing","volume":"76","author":"Yadava","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","unstructured":"Shao, G.N., Kim, H., and Imran, S. (2021, September 05). 2016 Use of EEG for Neuromarketing Applications. Available online: https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S092633731500346X."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Aldayel, M., Ykhlef, M., and Al-Nafjan, A. (2020). Deep learning for EEG-based preference classification in neuromarketing. Appl. Sci., 10.","DOI":"10.3390\/app10041525"},{"key":"ref_5","first-page":"20","article-title":"The contributions of neuromarketing in marketing research","volume":"5","author":"Hammou","year":"2013","journal-title":"J. Manag. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1108\/EJM-12-2016-0805","article-title":"Applying EEG in consumer neuroscience","volume":"52","author":"Lin","year":"2018","journal-title":"Eur. J. Mark."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1509\/jmr.13.0564","article-title":"Using EEG to predict consumers\u2019 future choices","volume":"52","author":"Telpaz","year":"2015","journal-title":"J. Mark. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1080\/10447318.2013.780869","article-title":"EEG-based brain-computer interfaces: A thorough literature survey","volume":"29","author":"Hwang","year":"2013","journal-title":"Int. J. Hum.-Comput. Interact."},{"key":"ref_9","first-page":"730218","article-title":"Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains","volume":"2014","year":"2014","journal-title":"Int. Sch. Res. Not."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s11571-015-9363-z","article-title":"Aesthetic preference recognition of 3D shapes using EEG","volume":"10","author":"Chew","year":"2016","journal-title":"Cogn. Neurodyn."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alharithi, F.S., Almulihi, A.H., Bourouis, S., Alroobaea, R., and Bouguila, N. (2021). Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition. Sensors, 21.","DOI":"10.3390\/s21072450"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.eij.2015.06.002","article-title":"Brain computer interfacing: Applications and challenges","volume":"16","author":"Abdulkader","year":"2015","journal-title":"Egypt. Inform. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105441","DOI":"10.1016\/j.compbiomed.2022.105441","article-title":"Game induced emotion analysis using electroencephalography","volume":"145","author":"Khan","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Almulihi, A.H., Alharithi, F.S., Bourouis, S., Alroobaea, R., Pawar, Y., and Bouguila, N. (2021). Oil Spill Detection in SAR Images Using Online Extended Variational Learning of Dirichlet Process Mixtures of Gamma Distributions. Remote Sens., 13.","DOI":"10.3390\/rs13152991"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.3389\/fnins.2020.594566","article-title":"Is EEG suitable for marketing research? A systematic review","volume":"14","author":"Bazzani","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.neunet.2017.01.013","article-title":"Prediction of advertisement preference by fusing EEG response and sentiment analysis","volume":"92","author":"Gauba","year":"2017","journal-title":"Neural Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"020141","DOI":"10.1063\/1.5005474","article-title":"Deep learning for EEG-Based preference classification","volume":"1891","author":"Teo","year":"2017","journal-title":"Aip Conf. Proc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"114","DOI":"10.30780\/IJTRS.V3.I3.2018.015","article-title":"Significance of Neuromarketing on consumer buying behaviour","volume":"3","author":"Devaru","year":"2018","journal-title":"Int. J. Tech. Res. Sci. SIGNIFICANCE"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.cmpb.2013.11.010","article-title":"Like\/dislike analysis using EEG: Determination of most discriminative channels and frequencies","volume":"113","author":"Yilmaz","year":"2014","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_20","first-page":"186","article-title":"Best Feature Extraction and Classification Algorithms for EEG Signals in Neuromarketing","volume":"7","author":"Zamani","year":"2020","journal-title":"Front. Biomed. Technol."},{"key":"ref_21","first-page":"76","article-title":"My destination in your brain: A novel neuromarketing approach for evaluating the effectiveness of destination marketing","volume":"7","author":"Bastiaansen","year":"2018","journal-title":"J. Destin. Mark. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3803","DOI":"10.1016\/j.eswa.2012.12.095","article-title":"Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking","volume":"40","author":"Khushaba","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.neuroimage.2011.07.042","article-title":"Effects of subjective preference of colors on attention-related occipital theta oscillations","volume":"59","author":"Kawasaki","year":"2012","journal-title":"NeuroImage"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/j.joep.2010.03.008","article-title":"Application of frontal EEG asymmetry to advertising research","volume":"31","author":"Ohme","year":"2010","journal-title":"J. Econ. Psychol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.seizure.2019.08.006","article-title":"Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies","volume":"71","author":"Usman","year":"2019","journal-title":"Seizure"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"61","DOI":"10.12720\/joams.1.1.61-65","article-title":"A new recommender system for 3D e-commerce: An EEG based approach","volume":"1","author":"Guo","year":"2013","journal-title":"J. Adv. Manag. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s12115-010-9408-1","article-title":"Neuromarketing: The new science of consumer behavior","volume":"48","author":"Morin","year":"2011","journal-title":"Society"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.jneumeth.2010.07.009","article-title":"The issue of multiple univariate comparisons in the context of neuroelectric brain mapping: An application in a neuromarketing experiment","volume":"191","author":"Vecchiato","year":"2010","journal-title":"J. Neurosci. Methods"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"12378","DOI":"10.1016\/j.eswa.2012.04.084","article-title":"Choice modeling and the brain: A study on the Electroencephalogram (EEG) of preferences","volume":"39","author":"Khushaba","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"39998","DOI":"10.1109\/ACCESS.2020.2976866","article-title":"Epileptic seizures prediction using deep learning techniques","volume":"8","author":"Usman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.bbe.2021.01.001","article-title":"Epileptic seizure prediction using scalp electroencephalogram signals","volume":"41","author":"Usman","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"627892","DOI":"10.1155\/2014\/627892","article-title":"EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation","volume":"2014","author":"Jirayucharoensak","year":"2014","journal-title":"Sci. World J."},{"key":"ref_33","unstructured":"Bashivan, P., Rish, I., Yeasin, M., and Codella, N. (2015). Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv."},{"key":"ref_34","unstructured":"Zhang, D., Yao, L., Zhang, X., Wang, S., Chen, W., and Boots, R. (2017). EEG-based intention recognition from spatio-temporal representations via cascade and parallel convolutional recurrent neural networks. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1186\/s12911-016-0310-7","article-title":"Detecting epileptic seizures with electroencephalogram via a context-learning model","volume":"16","author":"Xun","year":"2016","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0274-4","article-title":"Severely imbalanced big data challenges: Investigating data sampling approaches","volume":"6","author":"Hasanin","year":"2019","journal-title":"J. Big Data"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8640596","DOI":"10.1155\/2022\/8640596","article-title":"A novel approach to automate complex software modularization using a fact extraction system","volume":"2022","author":"Khan","year":"2022","journal-title":"J. Math."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104710","DOI":"10.1016\/j.compbiomed.2021.104710","article-title":"A deep learning based ensemble learning method for epileptic seizure prediction","volume":"136","author":"Usman","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106818","DOI":"10.1016\/j.eplepsyres.2021.106818","article-title":"Detection of preictal state in epileptic seizures using ensemble classifier","volume":"178","author":"Usman","year":"2021","journal-title":"Epilepsy Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ren, Y., and Wu, Y. (2014, January 6\u201311). Convolutional deep belief networks for feature extraction of EEG signal. Proceedings of the IEEE 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889383"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1023\/A:1007878001388","article-title":"Real time processing of affective and cognitive stimuli in the human brain extracted from MEG signals","volume":"13","author":"Ioannides","year":"2000","journal-title":"Brain Topogr."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1111\/1467-8616.00144","article-title":"Brands on the brain: Neuro-images of advertising","volume":"11","author":"Ambler","year":"2000","journal-title":"Bus. Strategy Rev."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chakravarthi, B., Ng, S.C., Ezilarasan, M., and Leung, M.F. (2022). EEG-based emotion recognition using hybrid CNN and LSTM classification. Front. Comput. Neurosci.","DOI":"10.3389\/fncom.2022.1019776"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms5567","article-title":"Audience preferences are predicted by temporal reliability of neural processing","volume":"5","author":"Dmochowski","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1111\/j.1460-9568.2004.03467.x","article-title":"The distributed neuronal systems supporting choice-making in real-life situations: Differences between men and women when choosing groceries detected using magnetoencephalography","volume":"20","author":"Braeutigam","year":"2004","journal-title":"Eur. J. Neurosci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S0896-6273(03)00293-9","article-title":"Beauty in the brain of the beholder","volume":"38","author":"Senior","year":"2003","journal-title":"Neuron"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Gupta, A., Shreyam, R., Garg, R., and Sayed, T. (2017, January 3\u20134). Correlation of neuromarketing to neurology. Proceedings of the International Conference on Materials, Alloys and Experimental Mechanics (ICMAEM-2017), Narsimha Reddy Engineering College, Hyderabad, India.","DOI":"10.1088\/1757-899X\/225\/1\/012129"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Reddy, G.T., Bhattacharya, S., Ramakrishnan, S.S., Chowdhary, C.L., Hakak, S., Kaluri, R., and Reddy, M.P.K. (2020, January 24\u201325). An ensemble based machine learning model for diabetic retinopathy classification. Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India.","DOI":"10.1109\/ic-ETITE47903.2020.235"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Murugappan, M., Murugappan, S., and Gerard, C. (2014, January 7\u20139). Wireless EEG signals based neuromarketing system using Fast Fourier Transform (FFT). Proceedings of the 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, Kuala Lumpur, Malaysia.","DOI":"10.1109\/CSPA.2014.6805714"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.neuron.2004.09.019","article-title":"Neural correlates of behavioral preference for culturally familiar drinks","volume":"44","author":"McClure","year":"2004","journal-title":"Neuron"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified least squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_53","first-page":"523","article-title":"Intelligent tutoring supported collaborative learning (ITSCL): A hybrid framework","volume":"11","author":"Haq","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e944","DOI":"10.7717\/peerj-cs.944","article-title":"Feature selection of EEG signals in neuromarketing","volume":"8","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Santhiya, P., and Chitrakala, S. (2022). PTCERE: Personality-trait mapping using cognitive-based emotion recognition from electroencephalogram signals. Vis. Comput.","DOI":"10.1007\/s00371-022-02502-5"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"9002101","DOI":"10.1155\/2022\/9002101","article-title":"An EEG-Based Neuromarketing Approach for Analyzing the Preference of an Electric Car","volume":"2022","author":"Raiesdana","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Gill, R., and Singh, J. (2022). A Proposed LSTM-Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG. Adv. Anal. Deep. Learn. Model.","DOI":"10.1002\/9781119792437.ch8"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1016\/j.ijresmar.2020.10.005","article-title":"Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning","volume":"38","author":"Adam","year":"2021","journal-title":"Int. J. Res. Mark."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"390","DOI":"10.4236\/jbise.2010.34054","article-title":"Classification of human emotion from EEG using discrete wavelet transform","volume":"3","author":"Murugappan","year":"2010","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_60","first-page":"23","article-title":"Neuromarketing: A review of research and implications for marketing","volume":"7","author":"Nilashi","year":"2020","journal-title":"J. Soft Comput. Decis. Support Syst."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/978-981-15-2317-5_47","article-title":"kNN and SVM classification for EEG: A review","volume":"632","author":"Shaabani","year":"2020","journal-title":"InECCE2019"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"103722","DOI":"10.1016\/j.compbiomed.2020.103722","article-title":"The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data","volume":"120","author":"Kang","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.1002\/ima.22577","article-title":"Nonparametric learning approach based on infinite flexible mixture model and its application to medical data analysis","volume":"31","author":"Bourouis","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3498","DOI":"10.1109\/TBME.2012.2217495","article-title":"Toward an EEG-based recognition of music liking using time-frequency analysis","volume":"59","author":"Hadjidimitriou","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Rakshit, A., and Lahiri, R. (2016, January 4\u20136). Discriminating different color from EEG signals using interval-type 2 fuzzy space classifier (a neuro-marketing study on the effect of color to Cognitive State). Proceedings of the 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India.","DOI":"10.1109\/ICPEICES.2016.7853388"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9744\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:39:56Z","timestamp":1760146796000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9744"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,12]]},"references-count":65,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249744"],"URL":"https:\/\/doi.org\/10.3390\/s22249744","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,12]]}}}