# Biometric Authentication and Verification for Medical Cyber Physical Systems
## 6 Device Authentication
### EEG and ECG Authentication on IMDs  
Several new studies discussed Implantable Medical Devices (IMD) authentication systems using Electroencephalography (EEG) and Electrocardiography (ECG) [14; 33; 34; 35]. In general, most papers proposed IMD authentication based on the inter-pulse interval (IPIs). An ECG is used to provide a quantitative measurement of the electrical activity of a hear over a period of time [36]. An example ECG pulse is shown in Figure 3. The signals are recorded by attaching a series of electrodes to various locations on the the patient's body. An EEG signal is used to record brain activity, and is similarly recorded by using electrodes attached to the patient's head, as shown in Figure 4[37]. Recent developments in the field of bimolecular sensing have widened the scope of IMDs. By implementing carbon nanotubes (CNTs) and graphene nanoribbons (GNRs), nano-sensors provide a nonintrusive method of collecting biometric information. In addition to their passive applications, bio-nano-sensors have been developed to detect the presence of cancer [38], asthma attacks, and common viruses [39].  
Figure 3: An example Electrocardiography (ECG) signal pulse.  
A study introduced an authentication system for the IMDs called Heart-to-Heart (H2H) [33]. This system used the ECG for the authentication mechanism, since the user's body should already have an IMD that can read the body's ECG and send out the information. A touchable medical instrument, generically called a \"Programmer\", was created and for it to work, the user needs to keep physical contact with it so that it can accept and authorize access to the user's IMD to read the signals. A H2H mechanism is used to compare the equality of the user's ECG from the touchable medical instrument (Programmer) and the IMD in order to access the IMD data. Another paper presented a secure scheme for IMDs with comprehensive techniques for the ECG-based keys with secure protocol and on the access control mechanism on the IMD external devices with an authentication proxy to protect it [35]. Another study implements a Discrete Cosine Transform to process real-time ECG signals for more resilient feature extraction [40]. An accuracy rate of 97.78% with an average 1.21 second processing time is reached for 15 subjects. A major drawback for most ECG authentication studies is the presumption that real-life users are perfectly healthy and have no preexisting medical conditions. Further testing is necessary for individuals with cardiac issues (e.g., arrhythmia and ischemia).  
In [41], the authors incorporate an EEG-based authentication system with eye blinking. Several features are extracted (including event-related potential and morphological features) from EEG and eye-blinking signals. This information is passed to a convolutional neural network to score the two features. Least squares is used to produce their final estimation score. This method produced an improved accuracy of 97.6% (when compared to EEG-based authentication systems). Additionally, machine learning has been implemented to leverage brain EEG signals for authentication [42]. Raw EEG signals are filtered then segmented into sub-bands. These are used to extract several features to train an Error Correcting Code Support Vector Machine classifier. The input EEG vector and stored EEG vector are compared, and if a certain threshold is passed, then the user is authenticated [43]. Lower-cost, consumer-grade EEG authentication systems have been studied by [44], thus introducing the possibility of more widespread adaptation of this technology. Ref. [45] proposes an in-ear EEG sensor to collect data. This novel technique allows for even easier EEG authentication implementation.  
Figure 4: The typical sensor layout for detecting Electroencephalography (EEG) signals.