NeuroSense: A Computational AI Model for Continuous Psychological State Prediction

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