###### Abstract  
In recent years, cutting-edge technologies, such as artificial intelligence (AI), blockchain, and digital twin (DT), have revolutionized the healthcare sector by enhancing public health and treatment quality through precise diagnosis, preventive measures, and real-time care capabilities. Despite these advancements, the massive amount of generated biomedical data puts substantial challenges associated with information security, privacy, and scalability. Applying blockchain in healthcare-based digital twins ensures data integrity, immutability, consistency, and security, making it a critical component in addressing these challenges. Federated learning (FL) has also emerged as a promising AI technique to enhance privacy and enable decentralized data processing. This paper investigates the integration of digital twin concepts with blockchain and FL in the healthcare domain, focusing on their architecture and applications. It also explores platforms and solutions that leverage these technologies for secure and scalable medical implementations. A case study on federated learning for electroencephalogram (EEG) signal classification is presented, demonstrating its potential as a diagnostic tool for brain activity analysis and neurological disorder detection. Finally, we highlight the key challenges, emerging opportunities, and future directions in advancing healthcare digital twins with blockchain and federated learning, paving the way for a more intelligent, secure, and privacy-preserving medical ecosystem. eurm  
industry encounters, this integration will ultimately improve people's wellness and quality of life [1; 2; 3; 4; 5]. The methodologies used for remote monitoring and diagnosis of the patient with wearable electroencephalogram (EEG) devices are examples of a combination of new technological developments and data processing techniques with biomedical applications. Virtual representations of real-world items, procedures, or systems are known as digital twins (DT) [6]. Through digital twins, new avenues are provided for individuals and businesses to engage with the physical world in novel ways [7]. DTs have emerged as indispensable assets across diverse industries, including manufacturing [8], medical [9], and automotive [10]. By replicating physical systems, they serve as a foundational component that facilitates data fusion, simulation, and the integration of technology. This integration enables effective problem-solving for complex multidisciplinary challenges [11; 12]. Notably, the significance of this technology has also been acknowledged within various fields, encompassing disciplines such as hydrology [13], agriculture [14], smart garden [15], industrial fault diagnosis [16], remote measuring [17], and soil disciplines [18]. Moreover, DT technology plays a significant role in making progress in crucial sectors, especially in the medical field. This technology offers a comprehensive digital representation that can aid the medical sector [19], as it is significantly impacting this industry by optimizing clinical operations and driving innovation to enhance the cost-effectiveness, reliability, quality, and flexibility of the biomedical system. DT technology can create virtual replicas of human organs, bodily systems, and processes, which can be used for personalized medicine, treatment planning, and medical research [20]. For instance, the European Union has made significant progress in establishing initiatives for the creation of a digital representation of a human or disease state, which is referred to as the European virtual human twin (VHT) [21]. VHT technology is designed to replicate and predict the behavior of its physical equivalents, including interactions with any extra ailments a person may have. It is constructed using software models and data. Simultaneously, researchers globally have made remarkable progress in developing DTs for healthcare, personalized medicine, and predictive care [22; 23].