{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T15:41:42Z","timestamp":1777650102786,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,14]],"date-time":"2020-08-14T00:00:00Z","timestamp":1597363200000},"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>Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence\u2014High Arousal, High Valence\u2014Low Arousal, Low Valence\u2014High Arousal, and Low Valence\u2014Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.<\/jats:p>","DOI":"10.3390\/s20164551","type":"journal-article","created":{"date-parts":[[2020,8,14]],"date-time":"2020-08-14T08:28:35Z","timestamp":1597393715000},"page":"4551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":114,"title":["CNN and LSTM-Based Emotion Charting Using Physiological Signals"],"prefix":"10.3390","volume":"20","author":[{"given":"Muhammad Najam","family":"Dar","sequence":"first","affiliation":[{"name":"Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering,  National University of Sciences and Technology, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6208-7231","authenticated-orcid":false,"given":"Muhammad Usman","family":"Akram","sequence":"additional","affiliation":[{"name":"Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering,  National University of Sciences and Technology, Islamabad 44000, Pakistan"}]},{"given":"Sajid Gul","family":"Khawaja","sequence":"additional","affiliation":[{"name":"Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering,  National University of Sciences and Technology, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1688-4448","authenticated-orcid":false,"given":"Amit N.","family":"Pujari","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, England, UK"},{"name":"School of Engineering, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1089\/tmj.2017.0250","article-title":"Deep ECGNet: An optimal deep learning framework for monitoring mental stress using ultra short-term ECG signals","volume":"24","author":"Hwang","year":"2018","journal-title":"Telemed. e-Health"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ferdinando, H., Sepp\u00e4nen, T., and Alasaarela, E. 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