This thesis aims to evaluate the viability of using a Field Programmable Gate Array (FPGA) in a neural Implantable Medical Device (IMD). The primary motivation for incorporating FPGAs is their potential to support future functionalities, such as running neural networks for medical condition analysis but also advanced cybersecurity algorithms. These algorithms are compute-intensive, and accelerators like FPGAs offer advantages in terms of speed and efficiency. To assess the effectiveness of such a device, state-of-the-art Microcontroller Units (MCUs) commonly used in similar applications are employed as a reference. Comparisons are made between MCU-only platforms and hybrid platforms integrating both an MCU and an FPGA. Feasibility analysis considers operational modes and use cases based on various realistic scenarios. The results show mixed outcomes across scenarios. Under a 100% duty cycle, the FPGA demonstrates higher efficiency, consuming less active power than the MCU. However, at lower duty cycles, MCUs are generally more effective on average. The use of an FPGA becomes practical when power-gating techniques are applied to minimize power consumption during inactive periods.

Purkinje cell is a type of neuron that can be found in the cerebellum. What characterises Purkinje cell neural activity is the fact that it exhibits two types of spiking behaviour; the so-called simple and complex spikes. These two types of spikes are thought to play a role in motor functionality. In order to better understand the relationship between Purkinje cell neural activity and the motor-cortex, neuroscientists record such neural activity in mice. However, current experimental setups pose a challenge as they involve a wired connection between the animal’s head stage and the recording device, which limits the mouse’s natural behaviour by restricting its movement. This work proposes a lightweight neural-spike detection and classification architecture for acquiring Purkinje cell neural activity. The proposed design discards unneeded information, by detecting and classifying spikes in real-time. This type of compression enables data storage on a removable device in the head stage, freeing mice from wires. Its small formfactor allows unrestricted movement during experiments, while a power-efficient design ensures long-termoperation. The performance of the algorithm has been evaluated using a software implementation, yielding a combined accuracy for detection and classification ranging from 92.74% to 94.54%. The system has been synthesised using the 45 nm Nangate Open Cell library resulting in an ASIC with an area of 0.22mm2 and a power consumption of 0.412mW.

The recent advances in the semiconductor industry have given rise to the development of highly scalable, wireless and battery-free neural-implant interfaces that enable brain monitoring and brain stimulation with high spatial and temporal resolution. Such implants are referred to as Free-Floating Neural Implants (FFNI), as the small size and untethered communication allow them to be scattered throughout the cortex. Nevertheless, the plethora of proposed interfaces have failed to mention and act against the potential security implications that may arise in highly-constrained FFNIs even though the U.S. Food and Drug Administration (FDA) has recently acknowledged the possibility of short-/long-range attacks on wireless Implantable Medical Devices (IMD). Hence, in this project, the existing threats in FFNIs are revealed, followed by the proposal of a memristor-based lightweight security approach to secure intracranial electromagnetic transmissions whilst considering the anticipated physical limitations of these constrained topologies. More specifically, a consolidated envisioned system is highlighted for which a read-only GIFT cipher is implemented. This lightweight encryption block primarily consists of a One-Transistor-One-Memristor (1T1R) crossbar structure for carrying out operations such as Substitution, Permutation, and addRoundKey, without destroying the resistive states and by only performing ‘read’ operations to maintain low power operation. With a footprint of 0.0034 mm2 the 1T1R-GIFT cipher reaches an average power and energy consumption of only 60.38 µW and 241.52 pJ, respectively. However, the performance does not exceed a CMOS-based implementation yet, whose footprint is similar but has roughly half the average power and energy consumption. This can be attributed mainly to the immaturity of the memristor technology. This work demonstrates that only after further advancements in memristor logic gates, crossbar topologies and fabrication processes, highly-constrained FFNIs can fully benefit from the scalable memristor-based security paradigm.

Medical Body Area Networks (MBANs) are a cluster of possibly heterogeneous devices, communicating with each other in, on or around the human body. Through these devices, medical data is collected, processed in some way and transferred outside of the network. The IEEE 802.15.6 standard aims to govern communications between such devices. It includes a set of constraints for physical features and communication on the PHY and MAC level, as well as association/ disassociation protocols and security services that applications need to comply with. Given the high sensitivity of the medical data transmitted via MBANs, network security is crucial. This thesis consists of three main contributions: (a) a structured procedure to analyse the security features of the IEEE 802.15.6 standard by using realistic hypothetical scenarios is introduced (b) a thorough security analysis of the standard is conducted (c) recommendations on how to improve the security posture of the standard are given.

Epilepsy is a medical condition which is caused by excessive or synchronous neuronal activity of the brain cells. These activities can lead to attacks where the patient can lose conciseness or experiences random muscle cramps at seemingly any point in time. Using implantable on body sensors these seizure attacks could be detected and even prevented. These sensors would form a Medical Body Area Network (MBAN) which interconnects all of the sensors. This project looks at a proof of concept implementation of such an MBAN and focuses on a secure connection between an implant and a gateway device, which is a mobile phone. The implant and mobile phone will communicate with each other using Bluetooth Low Energy (BLE). This form of communication does not provide a secure pairing method for devices that lack in- and output capabilities, such as an implant. To set up a secure connection the data will be encrypted with an encryption key, which has to be shared between the implant and mobile phone. In order to do this in a secure way, an Out Of Band (OOB) channel will be used to pair the two devices. This thesis looks at three different OOB channels, Near Field Communication (NFC), ultrasound and galvanic coupling and compares them in therms of security, health safety, data rate, power consumption and feasibility.