ItemConference Paper

Integrating Behavioural Science using the Psycho-Intelligence Framework in Connected Systems

Title
Integrating Behavioural Science using the Psycho-Intelligence Framework in Connected Systems
Creator
Iwendi, Celestine; Nwibo, Ezekiel Gabriel; Sharma, Vandana; Lemeke, Collins; Uwah, Salome Enoshi; Ukura, Kumasuun Kimberly
Description
The fast-growing convergence of neuroscience, behaviour computing, and adaptive artificial intelligence (AI) offers the possibility to transform human, machine interaction. This work presents Psycho-Intelligence, a new, closed-loop system that merges electroencephalography (EEG) and inertial motion unit (IMU) signals to adaptively recognise and react to users' cognitive and affective states. Levying low-cost wearable sensors (Muse EEG and MPU-6050), the system has real-time signal acquisition, sophisticated preprocessing, spectral and statistical feature extraction, as well as multimodal fusion features. Dimensionality reduction and feature selection techniques, including Principal Component Analysis and XGBoost gain metrics, enhance learning optimally. Multiple machine learning algorithms like Random Forest, SVM and XGBoost are trained to identify engagement states with high accuracy, warranted by extensive testing through cross-validation, ROC AUC, and F1-scores. The pipeline is incorporated into an adaptive feedback system that can regulate chatbot tone, learning material, or interactive graphics based on detected user states. Statistical validation with linear mixed models confirms the robustness of EEG-derived measurements in engagement prediction. The research establishes a new paradigm for emotionally intelligent AI systems and provides a technical foundation for ethical, real-time psycho-behavioural intelligence for communication networks, education systems, and cognitive health monitoring. 2025 IEEE.
Source
2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Adaptive Human-AI Interaction; Behavioural Computing; EEG-IMU Fusion; Multimodal Emotion Recognition; Psycho-Intelligence
Coverage
Iwendi C., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Nwibo E.G., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Lemeke C., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Uwah S.E., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom; Ukura K.K., Centre of Intelligence of Things (CIoTh), University of Greater Manchester, Bolton, United Kingdom
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833157981-4;
Format
online
Language
English
Type
Conference paper

Citation

Iwendi, Celestine; Nwibo, Ezekiel Gabriel; Sharma, Vandana; Lemeke, Collins; Uwah, Salome Enoshi; Ukura, Kumasuun Kimberly, “Integrating Behavioural Science using the Psycho-Intelligence Framework in Connected Systems,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26120.

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