NeuroSense: A Computational AI Model for Continuous Psychological State Prediction

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.

Introduction

NeuroSense is a multimodal AI framework for continuous, real-time prediction of human psychological states, including stress, anxiety, cognitive load, arousal, valence, and fatigue. Traditional assessment methods are limited by subjectivity, low temporal resolution, and inability to capture fast-changing states. NeuroSense overcomes these challenges by integrating EEG, ECG/HRV, facial expressions, speech, text, and behavioral data through deep learning models, producing a Unified Psychological State Vector (UPSV) updated every 1–5 seconds.

The system uses spatio-temporal EEG encoding, physiological dynamics modeling, vision transformers for micro-expressions, and NLP embeddings for cognitive load, with dynamic multimodal fusion. Experiments on benchmark datasets (DEAP, SEED, WESAD, AffectNet, RAVDESS) show significant improvements over baseline models in predicting valence, arousal, stress, cognitive load, and fatigue. Applications include mental health monitoring, adaptive learning, driver fatigue detection, telehealth, and VR/AR systems. Ethical considerations focus on privacy, consent, bias mitigation, and responsible use, while limitations include sensor dependency, EEG noise, and cross-cultural generalization.

Conclusion

A multimodal AI framework for ongoing psychological state prediction is presented by NeuroSense. In order to enable next-generation applications in mental health, education, and HCI, this work establishes the groundwork for neuro-adaptive systems with real-time affective and cognitive inference.

References

[1] Picard, R. W. (1997). Affective Computing. MIT Press. [2] Koelstra, S., Muhl, C., Soleymani, M., et al. (2012). DEAP: A database for emotion analysis using physiological signals. IEEE Trans. Affective Computing, 3(1), 18–31. [3] Soleymani, M., Pantic, M., & Pun, T. (2012). Multimodal emotion recognition using physiological signals. IEEE Trans. Affective Computing, 3(2), 211–223. [4] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. [5] Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., et al. (2001). Emotion recognition in human–computer interaction. IEEE Signal Processing Mag., 18(1), 32–80. [6] Busso, C., Bulut, M., Lee, C.-C., et al. (2008). IEMOCAP: Interactive emotional dyadic motion capture database. Lang. Resources & Eval., 42, 335–359. [7] Dhall, A., Goecke, R., & Gedeon, T. (2021). A survey of human stress and emotion recognition from physiological signals. Pattern Recognition Letters, 146, 1–11. [8] Kim, M., & Sridharan, V. N. (2021). Emotion recognition using EEG signals with deep learning. Sensors, 21(19), 6587. [9] Rizzo, A., & Bouchard, T. (2021). AI in mental health: Emotion-sensitive computing. Computer, 54(10), 16–26. [10] Wang, J., Chen, F., & Xu, Y. (2020). Continuous emotion recognition using multimodal deep learning. IEEE Trans. Affective Computing.

Copyright

Copyright © 2025 Pushkar Sharma, Sandhyarani Dora, Damini Suryavanshi, Uzaib Saiyad, Rajesh Sable. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.