NeuroSense: A Computational AI Model for Continuous Psychological State Prediction

NeuroSense: A Computational AI Model for Continuous Psychological State Prediction | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 December 2025 V1 Latest version Share on NeuroSense: A Computational AI Model for Continuous Psychological State Prediction Authors : Pushkar Sharma 0009-0009-8326-2519 [email protected] , Sandhyarani Dora , Damini Suryavanshi , Uzaib Saiyad , and Rajesh Sable Authors Info & Affiliations https://doi.org/10.22541/au.176463741.15717110/v1 Published International Journal for Research in Applied Science and Engineering Technology Version of record Peer review timeline 217 views 118 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract An emerging interdisciplinary challenge in artificial intelligence, computational psychology, and neuroscience is the ongoing evaluation of human psychological states. Conventional mental-state assessments rely on clinical interviews and episodic, subjective self-reports, which are not flexible in real time. This paper presents NeuroSense, a computational AI framework that uses multimodal signals such as EEG, heart-rate variability (HRV), speech prosody, facial micro-expressions, linguistic sentiment, and contextual behavioral features to predict dynamic psychological states. A multimodal fusion pipeline comprising a Spatio-Temporal EEG Encoder, Physiological Dynamics Model, Affective Facial Transformer, Prosodic Emotional Encoder, and NLP-based Cognitive Load Estimator is integrated by NeuroSense. Continuous prediction using a hybrid deep learning framework is made possible by the convergence of these signals into a Unified Psychological State Vector (UPSV). High potential for real-time affect estimation, stress prediction, cognitive load modeling, and mental fatigue detection is demonstrated by experiments conducted on benchmark datasets. Future studies will investigate neuro-adaptive intelligent interfaces, wearable IoT integration, and federated learning. Supplementary Material File (neurosense- a computational ai model for continuous psychological state prediction.pdf) Download 217.96 KB Information & Authors Information Version history V1 Version 1 02 December 2025 Peer review timeline Published International Journal for Research in Applied Science and Engineering Technology Version of Record 30 Nov 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords affective computing continuous emotion estimation deep learning eeg hrv multimodal ai neuroai psychological state prediction Authors Affiliations Pushkar Sharma 0009-0009-8326-2519 [email protected] ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University, India View all articles by this author Sandhyarani Dora ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University, India View all articles by this author Damini Suryavanshi ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University, India View all articles by this author Uzaib Saiyad ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University, India View all articles by this author Rajesh Sable ¹Undergraduate Researchers, Vimal Tormal Poddar BCA College, Veer Narmad South Gujarat University, India View all articles by this author Metrics & Citations Metrics Article Usage 217 views 118 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Pushkar Sharma, Sandhyarani Dora, Damini Suryavanshi, et al. NeuroSense: A Computational AI Model for Continuous Psychological State Prediction. Authorea . 02 December 2025. DOI: https://doi.org/10.22541/au.176463741.15717110/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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