{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T12:00:54Z","timestamp":1784894454039,"version":"3.55.0"},"reference-count":93,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Physiological signal analysis using wearable biosensors like an electroencephalogram (EEG) and an electrocardiogram (ECG) is widely investigated for affective computing; however, the integration of deep learning-based affective computing within 6G-driven IoT healthcare infrastructures remains limited, with data transmission latency posing a significant challenge. This study proposes a framework for EEG and ECG-based affective state analysis over 6G IoT networks. We utilize an attention-based deep learning model for three-class emotion recognition from EEG signals, and a ResNet50-based convolutional neural network for three-class stress\/affective state classification using ECG data. The framework\u2019s communication performance is evaluated through ray tracing simulations in a virtual hospital environment at 7 GHz and 92 GHz bands. Experimental results on SEED and WESAD datasets demonstrate that the EEG model achieved 90.93% classification accuracy, while the ECG model yielded 82.26% validation and 65.64% test accuracy. Wireless analysis showed RMS delay spread values of 6.53 ns (7 GHz) and 3.32 ns (92 GHz), with correlation bandwidths of 30.63 MHz and 60.24 MHz, respectively. These findings demonstrate the feasibility of integrating wearable biosensor-based affective state analysis with 6G-oriented IoT healthcare communication frameworks, providing a robust foundation for future personalized health and human state monitoring applications.<\/jats:p>","DOI":"10.3390\/bios16080400","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:05:42Z","timestamp":1784819142000},"page":"400","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EEG and ECG Wearable Biosensor-Based Affective State Analysis Using Deep Learning over 6G IoT Healthcare Networks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6514-8634","authenticated-orcid":false,"given":"Hazal Su","family":"B\u0131\u00e7akc\u0131 Ye\u015filkaya","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, \u0130zmir Bak\u0131r\u00e7ay University, 35665 \u0130zmir, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3215-6363","authenticated-orcid":false,"given":"Mete","family":"\u00d6zbaltan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, \u0130zmir Bak\u0131r\u00e7ay University, 35665 \u0130zmir, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0191-312X","authenticated-orcid":false,"given":"Nihan","family":"\u00d6zbaltan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering and Architecture, \u0130zmir Bak\u0131r\u00e7ay University, 35665 \u0130zmir, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9680-4622","authenticated-orcid":false,"given":"Cihat","family":"\u015eeker","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, \u0130zmir Bak\u0131r\u00e7ay University, 35665 \u0130zmir, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2235-4240","authenticated-orcid":false,"given":"Bartu","family":"Ye\u015filkaya","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering and Architecture, \u0130zmir Katip \u00c7elebi University, Balatcik Campus, 35620 \u0130zmir, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3644-0692","authenticated-orcid":false,"given":"Bengisu","family":"Yal\u00e7\u0131nkaya","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering and Architecture, \u0130zmir Bak\u0131r\u00e7ay University, 35665 \u0130zmir, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"66888","DOI":"10.1109\/ACCESS.2025.3559932","article-title":"IoT-powered mental healthcare: Sleep state analyzing and monitoring based on EEG signals","volume":"13","author":"Do","year":"2025","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100859","DOI":"10.1016\/j.cosrev.2025.100859","article-title":"AIoT for human emotion recognition: Potentials, challenges, and healthcare applications","volume":"60","author":"Yaqoob","year":"2026","journal-title":"Comput. Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1049\/bme2.12105","article-title":"Activity-based electrocardiogram biometric verification using wearable devices","volume":"12","author":"Santopietro","year":"2023","journal-title":"IET Biom."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18469","DOI":"10.1109\/JIOT.2023.3277500","article-title":"HealthFaaS: AI-Based Smart Healthcare System for Heart Patients Using Serverless Computing","volume":"10","author":"Golec","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/JIOT.2021.3103320","article-title":"6G Internet of Things: A comprehensive survey","volume":"9","author":"Nguyen","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5043","DOI":"10.1109\/JBHI.2024.3367736","article-title":"Neural Networks Based Smart E-Health Application for the Prediction of Tuberculosis Using Serverless Computing","volume":"28","author":"Murugesan","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_7","unstructured":"B\u0131\u00e7akc\u0131, H.S. (2024). A Novel Framework for ECG Biometric Verification on Mobile Devices Utilising Activity Classification. [Ph.D. Thesis, University of Kent]."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ye\u015filkaya, B., G\u00fcren, O., Bahar, M.T., Turhal, L.N., and Akan, A. (2020). Estimation of Emotion Status Using IAPS Image Data Set. Proceedings of the 2020 28th Signal Processing and Communications Applications Conference (SIU), IEEE.","DOI":"10.1109\/SIU49456.2020.9302223"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dall\u2019Ora, N., Felli, L., Aldegheri, S., Vicino, N., and Giuliano, R. (2025). LumiCare: A Context-Aware Mobile System for Alzheimer\u2019s Patients Integrating AI Agents and 6G. Electronics, 14.","DOI":"10.3390\/electronics14173516"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., and Yang, X. (2018). A review of emotion recognition using physiological signals. Sensors, 18.","DOI":"10.3390\/s18072074"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dzedzickis, A., Kaklauskas, A., and Bucinskas, V. (2020). Human emotion recognition: Review of sensors and methods. Sensors, 20.","DOI":"10.3390\/s20030592"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TAFFC.2017.2714671","article-title":"Emotions recognition using EEG signals: A survey","volume":"10","author":"Alarcao","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.entcs.2019.04.009","article-title":"Emotion recognition from physiological signal analysis: A review","volume":"343","author":"Egger","year":"2019","journal-title":"Electron. Notes Theor. Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hasnul, M.A., Aziz, N.A.A., Alelyani, S., Mohana, M., and Aziz, A.A. (2021). Electrocardiogram-based emotion recognition systems and their applications in healthcare\u2014A review. Sensors, 21.","DOI":"10.3390\/s21155015"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bustos-Lopez, M., Cruz-Ramirez, N., Guerra-Hernandez, A., S\u00e1nchez-Morales, L.N., Cruz-Ramos, N.A., and Alor-Hernandez, G. (2022). Wearables for engagement detection in learning environments: A review. Biosensors, 12.","DOI":"10.3390\/bios12070509"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hui, T.K., and Sherratt, R.S. (2018). Coverage of emotion recognition for common wearable biosensors. Biosensors, 8.","DOI":"10.3390\/bios8020030"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/T-AFFC.2011.25","article-title":"A multimodal database for affect recognition and implicit tagging","volume":"3","author":"Soleymani","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/JBHI.2017.2688239","article-title":"DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices","volume":"22","author":"Katsigiannis","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TAFFC.2016.2625250","article-title":"ASCERTAIN: Emotion and personality recognition using commercial sensors","volume":"9","author":"Subramanian","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_20","first-page":"479","article-title":"Amigos: A dataset for affect, personality and mood research on individuals and groups","volume":"12","author":"Abadi","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/TAFFC.2014.2339834","article-title":"Feature extraction and selection for emotion recognition from EEG","volume":"5","author":"Jenke","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TCDS.2016.2587290","article-title":"Multichannel EEG-based emotion recognition via group sparse canonical correlation analysis","volume":"9","author":"Zheng","year":"2016","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","article-title":"Identifying stable patterns over time for emotion recognition from EEG","volume":"10","author":"Zheng","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chai, X., Wang, Q., Zhao, Y., Li, Y., Liu, D., Liu, X., and Bai, O. (2017). A fast, efficient domain adaptation technique for cross-domain electroencephalography (EEG)-based emotion recognition. Sensors, 17.","DOI":"10.3390\/s17051014"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tripathi, S., Acharya, S., Sharma, R., Mittal, S., and Bhattacharya, S. (2017, January 4\u20139). Using deep and convolutional neural networks for accurate emotion classification on DEAP data. Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i2.19105"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1007\/s12559-017-9533-x","article-title":"Hierarchical convolutional neural networks for EEG-based emotion recognition","volume":"10","author":"Li","year":"2018","journal-title":"Cogn. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, J., Zhou, H., and Zhong, N. (2017). Human emotion recognition with electroencephalographic multidimensional features by hybrid deep neural networks. Appl. Sci., 7.","DOI":"10.3390\/app7101060"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wu, Q., Qiu, M., Wang, Y., and Chen, X. (2018). Emotion recognition from multi-channel EEG through parallel convolutional recurrent neural network. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), IEEE.","DOI":"10.1109\/IJCNN.2018.8489331"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1109\/TBME.2019.2897651","article-title":"EEG based emotion recognition by combining functional connectivity network and local activations","volume":"66","author":"Li","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cimtay, Y., and Ekmekcioglu, E. (2020). Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition. Sensors, 20.","DOI":"10.3390\/s20072034"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1007\/s11571-020-09634-1","article-title":"EEG-based emotion recognition using 4D convolutional recurrent neural network","volume":"14","author":"Shen","year":"2020","journal-title":"Cogn. Neurodyn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","article-title":"EEG emotion recognition using dynamical graph convolutional neural networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1109\/TAFFC.2020.2994159","article-title":"EEG-based emotion recognition using regularized graph neural networks","volume":"13","author":"Zhong","year":"2020","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1109\/TAFFC.2020.3025777","article-title":"EEG-based emotion recognition via channel-wise attention and self attention","volume":"14","author":"Tao","year":"2020","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1109\/TAFFC.2022.3169001","article-title":"TSception: Capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition","volume":"14","author":"Ding","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kim, Y., and Choi, A. (2020). EEG-based emotion classification using long short-term memory network with attention mechanism. Sensors, 20.","DOI":"10.3390\/s20236727"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Nandi, A., Xhafa, F., Subirats, L., and Fort, S. (2021). Real-time emotion classification using eeg data stream in e-learning contexts. Sensors, 21.","DOI":"10.3390\/s21051589"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Qin, Y., Zhang, Y., Zhang, Y., Liu, S., and Guo, X. (2023). Application and development of EEG acquisition and feedback technology: A review. Biosensors, 13.","DOI":"10.3390\/bios13100930"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yan, N., Chen, G., and Zhang, X. (2026). Emotion recognition using multi-view EEG-fNIRS and cross-attention feature fusion. Biosensors, 16.","DOI":"10.3390\/bios16030145"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","article-title":"Emotionmeter: A multimodal framework for recognizing human emotions","volume":"49","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1109\/TAFFC.2015.2432810","article-title":"Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability","volume":"6","author":"Nardelli","year":"2015","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101646","DOI":"10.1016\/j.bspc.2019.101646","article-title":"A machine learning model for emotion recognition from physiological signals","volume":"55","author":"Delahoz","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sep\u00falveda, A., Castillo, F., Palma, C., and Rodriguez-Fernandez, M. (2021). Emotion recognition from ECG signals using wavelet scattering and machine learning. Appl. Sci., 11.","DOI":"10.3390\/app11114945"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Can, Y.S., Chalabianloo, N., Ekiz, D., and Ersoy, C. (2019). Continuous stress detection using wearable sensors in real life: Algorithmic programming contest case study. Sensors, 19.","DOI":"10.3390\/s19081849"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3366","DOI":"10.1109\/JSEN.2020.3023886","article-title":"Using wearable physiological sensors for affect-aware intelligent tutoring systems","volume":"21","author":"Alqahtani","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rinella, S., Massimino, S., Fallica, P.G., Giacobbe, A., Donato, N., Coco, M., Neri, G., Parenti, R., Perciavalle, V., and Conoci, S. (2022). Emotion recognition: Photoplethysmography and electrocardiography in comparison. Biosensors, 12.","DOI":"10.3390\/bios12100811"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"Deap: A database for emotion analysis; using physiological signals","volume":"3","author":"Koelstra","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1007\/s00521-015-2149-8","article-title":"Wavelet-based emotion recognition system using EEG signal","volume":"28","author":"Mohammadi","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1007\/s11760-013-0591-6","article-title":"Emotion classification during music listening from forehead biosignals","volume":"9","author":"Naji","year":"2015","journal-title":"Signal Image Video Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/T-AFFC.2011.37","article-title":"Multimodal emotion recognition in response to videos","volume":"3","author":"Soleymani","year":"2011","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s10044-016-0567-6","article-title":"Emotion recognition from EEG signals by using multivariate empirical mode decomposition","volume":"21","author":"Mert","year":"2018","journal-title":"Pattern Anal. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1037\/h0077714","article-title":"A circumplex model of affect","volume":"39","author":"Russell","year":"1980","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Liu, Y.H., Cheng, W.T., Hsiao, Y.T., Wu, C.T., and Jeng, M.D. (2014). EEG-based emotion recognition based on kernel Fisher\u2019s discriminant analysis and spectral powers. Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE.","DOI":"10.1109\/SMC.2014.6974254"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Mehmood, R.M., and Lee, H.J. (2016). Emotion recognition from EEG brain signals based on particle swarm optimization and genetic search. Proceedings of the 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), IEEE.","DOI":"10.1109\/ICMEW.2016.7574682"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Mohammadpour, M., Hashemi, S.M.R., and Houshmand, N. (2017). Classification of EEG-based emotion for BCI applications. Proceedings of the 2017 Artificial Intelligence and Robotics (IRANOPEN), IEEE.","DOI":"10.1109\/RIOS.2017.7956455"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Krisnandhika, B., Faqih, A., Pumamasari, P.D., and Kusumoputro, B. (2017). Emotion recognition system based on EEG signals using relative wavelet energy features and a modified radial basis function neural networks. Proceedings of the 2017 International Conference on Consumer Electronics and Devices (ICCED), IEEE.","DOI":"10.1109\/ICCED.2017.8019990"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Candra, H., Yuwono, M., Chai, R., Nguyen, H.T., and Su, S. (2017). EEG emotion recognition using reduced channel wavelet entropy and average wavelet coefficient features with normal mutual information method. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC.2017.8036862"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., and Van Laerhoven, K. (2018, January 16\u201320). Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI \u201918), New York, NY, USA.","DOI":"10.1145\/3242969.3242985"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"70590","DOI":"10.1109\/ACCESS.2020.2986810","article-title":"Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals","volume":"8","author":"Gjoreski","year":"2020","journal-title":"IEEE Access"},{"key":"ref_60","unstructured":"Seo, W., Kim, N., Kim, S., Lee, C., and Park, S.M. (2021). Multimodal deep learning for stress detection using wearable physiological sensors. Proceedings of the 2021 IEEE International Conference on Big Data and Smart Computing (BigComp), IEEE."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"103139","DOI":"10.1016\/j.jbi.2019.103139","article-title":"Stress detection in daily life scenarios using smart phones and wearable sensors: A survey","volume":"92","author":"Can","year":"2019","journal-title":"J. Biomed. Inform."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s11277-024-10912-y","article-title":"Towards the analysis of regularized denoising autoencoder for biosignal processing: Lasso versus ridge norms","volume":"134","author":"Dasan","year":"2024","journal-title":"Wirel. Pers. Commun."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"38292","DOI":"10.1109\/JIOT.2025.3585233","article-title":"A portable neurofeedback training system for attention improvement based on high-performance edge CNN accelerator","volume":"12","author":"He","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"075255","DOI":"10.1088\/1402-4896\/ade5d3","article-title":"Advanced chaotic wavelet encryption for simultaneous securing multi-lead ECG and EEG physiological signals in IoMT applications","volume":"100","author":"Sone","year":"2025","journal-title":"Phys. Scr."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Agiomavritis, F., and Karanasiou, I. (2026). Tactical-Grade Wearables and Authentication Biometrics. Sensors, 26.","DOI":"10.3390\/s26030759"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"10134","DOI":"10.1109\/TCE.2025.3606642","article-title":"Adaptive Federated Learning Framework for Privacy-Preserving Consumer-Centric IoMT: A Novel Secure Data Collaboration Model","volume":"71","author":"Islam","year":"2025","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1049\/cmu2.12240","article-title":"Technology-driven 5G enabled e-healthcare system during COVID-19 pandemic","volume":"16","author":"Alshammari","year":"2022","journal-title":"IET Commun."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"132444","DOI":"10.1109\/ACCESS.2024.3455900","article-title":"Patient monitoring system based on internet of things: A review and related challenges with open research issues","volume":"12","author":"Mahmmod","year":"2024","journal-title":"IEEE Access"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/MWC.001.1900333","article-title":"Vision, requirements, and technology trend of 6G: How to tackle the challenges of system coverage, capacity, user data-rate and movement speed","volume":"27","author":"Chen","year":"2020","journal-title":"IEEE Wirel. Commun."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Sakthi, U., Alasmari, A., Girija, S., Senthil, P., Qamar, S., and Hariharasitaraman, S. (2024). Smart healthcare based cyber physical system modeling by block chain with cloud 6G network and machine learning techniques. Wirel. Pers. Commun., 1\u201325.","DOI":"10.1007\/s11277-024-11243-8"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sherman, D.L., and Thakor, N.V. (2020). EEG Signal Processing: Theory and Applications. Neural Engineering, Springer International Publishing.","DOI":"10.1007\/978-3-030-43395-6_3"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Song, T., Liu, S., Zheng, W., Zong, Y., and Cui, Z. (2020, January 7\u201312). Instance-adaptive graph for EEG emotion recognition. Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i03.5656"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"206734","DOI":"10.1109\/ACCESS.2020.3037717","article-title":"Key advances in pervasive edge computing for industrial internet of things in 5g and beyond","volume":"8","author":"Narayanan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"102693","DOI":"10.1016\/j.jnca.2020.102693","article-title":"Blockchain for 5G and beyond networks: A state of the art survey","volume":"166","author":"Nguyen","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Duan, R.N., Zhu, J.Y., and Lu, B.L. (2013). Differential entropy feature for EEG-based emotion classification. Proceedings of the 6th International IEEE\/EMBS Conference on Neural Engineering (NER), IEEE.","DOI":"10.1109\/NER.2013.6695876"},{"key":"ref_77","unstructured":"(2026, July 12). Brain-like Computing and Machine Intelligence Laboratory, Shanghai Jiao Tong University. SEED: SJTU Emotion EEG Dataset. Available online: https:\/\/bcmi.sjtu.edu.cn\/home\/seed\/seed.html."},{"key":"ref_78","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., and Van Laerhoven, K. (2026, July 12). WESAD (Wearable Stress and Affect Detection). Available online: https:\/\/archive.ics.uci.edu\/dataset\/465\/wesad."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3051996","article-title":"A sliding window common spatial pattern for enhancing motor imagery classification in EEG-BCI","volume":"70","author":"Gaur","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_80","unstructured":"International Telecommunication Union (2023). ITU-R P.2040-3: Effects of Building Materials and Structures on Radiowave Propagation Above About 100 MHz, ITU Radiocommunication Sector (ITU-R). Available online: https:\/\/www.itu.int\/rec\/R-REC-P.2040-3-202308-S\/en."},{"key":"ref_81","unstructured":"(2019). IEEE Standard for Safety Levels with Respect to Human Exposure to Electric, Magnetic, and Electromagnetic Fields, 0 Hz to 300 GHz (Standard No. IEEE Std C95.1-2019)."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2713432","DOI":"10.1155\/int\/2713432","article-title":"Accurate Indoor Channel Modeling for mmWave Communication Systems in Smart Environments Using Ray Tracing and Measurement-Based Validation","volume":"2026","author":"Yao","year":"2026","journal-title":"Int. J. Intell. Syst."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"8161","DOI":"10.1109\/JIOT.2025.3616189","article-title":"Computer Vision-Based Beamforming Optimization with Trustworthy AI for 6G-IoT Services","volume":"13","author":"Cao","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Shakya, D., Ying, M., Rappaport, T.S., Poddar, H., Ma, P., Wang, Y., and Al-Wazani, I. (2024). Propagation measurements and channel models in Indoor Environment at 6.75 GHz FR1 (C) and 16.95 GHz FR3 Upper-mid band Spectrum for 5G and 6G. Proceedings of the GLOBECOM 2024-2024 IEEE Global Communications Conference, IEEE.","DOI":"10.1109\/GLOBECOM52923.2024.10901735"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Ying, M., Shakya, D., Rappaport, T.S., Ma, P., Wang, Y., Al-Wazani, I., Wu, Y., and Poddar, H. (2025). Upper mid-band channel measurements and characterization at 6.75 GHz FR1 (C) and 16.95 GHz FR3 in an indoor factory scenario. Proceedings of the ICC 2025-IEEE International Conference on Communications, IEEE.","DOI":"10.1109\/ICC52391.2025.11160744"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Poddar, H., and Chowdary, A. (2025). Single and Multi-Frequency Path Loss Models for Indoor Hotspot Scenario Based on Measurements Conducted at 6.75, 16.95, 28, 73 and 142 GHz. arXiv.","DOI":"10.1109\/GLOBECOM59602.2025.11432539"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Moraitis, N., Rogaris, A., Popescu, I., and Nikita, K.S. (2025). Indoor Corridor Channel Measurements and Characterization in the Upper Mid-Band. Proceedings of the 2025 19th European Conference on Antennas and Propagation (EuCAP), IEEE.","DOI":"10.23919\/EuCAP63536.2025.10999275"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"6073","DOI":"10.1109\/TVT.2023.3347074","article-title":"LOS-to-NLOS channel transition measurements, analysis and characterization for the 90 GHz band","volume":"73","author":"Afroze","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/JMW.2025.3562285","article-title":"95 GHz Indoor Propagation Measurement and 3GPP-Compatible Channel Model for Sub-THz Indoor Short-Range Communications","volume":"5","author":"Koda","year":"2025","journal-title":"IEEE J. Microwaves"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Yin, L., Yang, R., and Yao, Y. (2021). Channel sounding and scene classification of indoor 6G millimeter wave channel based on machine learning. Electronics, 10.","DOI":"10.3390\/electronics10070843"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"14249","DOI":"10.1109\/TWC.2024.3411031","article-title":"Channel Characterization for the 90 GHz Band in Aviation Environments","volume":"23","author":"Afroze","year":"2024","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1109\/OJVT.2025.3584903","article-title":"A novel stochastic channel model for WPAN short-range scenarios based on 3GPP framework and LoS multipath measurements at 105 GHz","volume":"6","author":"Hashimoto","year":"2025","journal-title":"IEEE Open J. Veh. Technol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3191","DOI":"10.1109\/TAP.2025.3526860","article-title":"Multiple-Frequency-Band Channel Characterization for In-Vehicle Wireless Networks","volume":"73","author":"Li","year":"2025","journal-title":"IEEE Trans. Antennas Propag."}],"container-title":["Biosensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/8\/400\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T11:02:48Z","timestamp":1784890968000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/8\/400"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,23]]},"references-count":93,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["bios16080400"],"URL":"https:\/\/doi.org\/10.3390\/bios16080400","relation":{},"ISSN":["2079-6374"],"issn-type":[{"value":"2079-6374","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,23]]}}}