Human motor imagery provides a window on how the brain plans and refines movement, but its integration into digital-twin technology for real-time skill improvement remains limited. This thesis addresses that gap by formalising a brain-based digital twin (BB-DT) for cricket batting motor imagery that fuses electro encephalography with synchronised kinematic data to model, predict and provide feedback on skilled interceptive actions. The thesis develops a four-layer neuro-kinematic architecture spanning signal processing, synthetic-data augmentation, personalised neural mass modelling and closed-loop intervention. It specifies validation criteria - representational fidelity, identifiability and tractability - for judging when a brain-based twin is theoretically sound, and treats synthetic EEG as a theory-guided instrument for hypothesis testing rather than a simple data surrogate. Methodologically, the research combines consumer-grade 14-channel EEG, expert-validated cricket-stroke videos, kinematic capture, and a matched-filter signal-processing pipeline. A conditional GAN supplies 85.1% synthetic augmentation to mitigate empirical sparsity, while an XGBoost classifier delivers real-time mental-state discrimination within an 80 ms feedback window. The BB-DT is demonstrated in a proof-of-concept coaching interface that provides neural efficiency feedback during batting imagery. The evaluation combines the cricket-specific dataset with external EEG datasets so that the computational claims are tested against both domain-specific and cross-dataset evidence. Across the thesis, the framework is assessed through controlled participant recordings, synthetic-data validation, latency profiling, classification experiments and prototype implementation evidence. This combination addresses central constraints in sport EEG: limited labelled data, noisy portable recordings, synchronisation between neural and movement streams, and the need for feedback fast enough to support training-oriented use. Findings show that domain-constrained synthetic augmentation maintains classification accuracy within 2% of all-real models and supports stable personalisation across sessions. The work also shows how a computational twin can expose interpretable links between alpha-band desynchronisation, predicted bat trajectory and subsequent kinematic refinement, extending prediction-based accounts of motor control. The thesis positions these results as proof-of-concept evidence rather than field-ready performance enhancement, with the principal validation focused on feasibility, latency, data governance and interpretable model behaviour under controlled laboratory conditions. The principal contribution is a unified computational framework for skilled interceptive movement that integrates streaming neurophysiology and kinematics for real-time modelling, classification and feedback within practical system constraints. A secondary contribution is a data-engineering and evaluation strategy for sparse sport-specific EEG settings, combining governed multimodal datasets, synthetic augmentation and reproducible cross-dataset validation. Together, these contributions provide design principles that generalise to rehabilitation, education, training technology and creative computing contexts where neural, behavioural and contextual data must be integrated responsibly.
D Pathak (Sat,) studied this question.