The growing adoption of EEG-enabled consumer Internet of Medical Things (IoMT) devices and wearable neuro-monitoring systems in healthcare and home-centric environments has raised critical concerns regarding data privacy, trust, and regulatory compliance. Although centralised deep learning approaches achieve strong performance in EEG-based emotion and cognitive state recognition, their reliance on centralised data aggregation limits scalability and applicability in privacy-sensitive consumer healthcare settings. Furthermore, existing federated learning approaches often inadequately address trust heterogeneity, non-IID neurophysiological variability, unreliable client participation, and the emerging requirement of federated unlearning in distributed IoMT environments. This paper proposes FedTrust-UNLEARN, a trust-aware federated learning framework with auditable unlearning for privacy-preserving EEG intelligence across heterogeneous consumer IoMT networks. The proposed framework integrates adaptive spectral representation learning, dynamic trust-aware aggregation, and an auditable federated unlearning mechanism that enables selective attenuation of client influence without requiring complete retraining. Extensive experiments on the SEED, SEED-IV, and DREAMER datasets under subject-dependent and subject-independent settings demonstrate competitive performance relative to centralised learning while improving robustness against unreliable clients, privacy preservation, and practical unlearning behaviour. The results demonstrate stable collaborative optimisation, bounded post-unlearning degradation, and communication-efficient deployment, highlighting the practicality of trustworthy, regulation-aware federated EEG intelligence for real-world consumer IoMT healthcare applications.