The human sense of smell is important for many vital functions, but with the current state of the art, there is a lack of objective and non-invasive methods for smell disorder diagnostics. In recent years, increasing attention is being paid to olfactory event-related potentials (OERPs) of the brain, as a viable tool for the objective assessment of olfactory dysfunctions. The aim of this review is to describe the main features of OERPs signals, the most widely used recording and processing techniques, and the scientific progress and relevance in the use of OERPs in many important application fields. In particular, the innovative role of OERPs is exploited in olfactory disorders that can influence emotions and personality or can be potential indicators of the onset or progression of neurological disorders. For all these reasons, this review presents and analyzes the latest scientific results and future challenges in the use of OERPs signals as an attractive solution for the objective monitoring technique of olfactory disorders.

Skin cancer is one of the most commonly diffused cancers in the world and its incidence rates have constantly increased in recent years. At the current state of the art, there is a lack of objective, quick and non-invasive methods for diagnosing this condition; this, combined with hospital crowding, may lead to late diagnosis. Starting from these considerations, this paper addresses the implementation of a microwave reflectometry based-system that can be used as a non-invasive method for the in-vivo diagnosis and early detection of biological abnormalities, such as skin cancer. This system relies on the dielectric contrasts existing between normal and anomalous skin tissues at microwave frequencies (in a frequency range up to 3 GHz). In particular, a truncated open-ended coaxial probe was designed, manufactured and tested to sense (in combination with a miniaturized Vector Network Analyzer) the variations of skin dielectric properties in a group of volunteer patients. The specific data processing demonstrated the suitability of the system for discriminating malignant and benign lesions from healthy skin, ensuring simultaneously effectiveness, low cost, compactness, comfortability and high sensitivity.

In spite of the technological advancement of the healthcare system, monitoring skin hydration still remains a challenging task. In particular, the state-of-the-art solutions are inadequate to obtain continuous real-time monitoring, especially in a wearable perspective. Starting from these considerations, this article addresses the implementation of an innovative wearable device that can monitor skin hydration through microwave reflectometry technique. To this purpose, a new flexible wearable sensing element (SE) was designed, manufactured, and validated to sense variation of skin hydration of the body, ensuring simultaneously a better wearability, and surface adherence to the skin. The system was preliminarily validated through a numerical analysis and then experimental tests were carried out through a low-cost portable vector network analyzer (VNA), which is particularly convenient in view of fully-wearable applications and comparable, from the metrological point of view to more expensive VNAs operating in the frequency range of interest. Results showed that the proposed SE configuration and the low-cost VNA hold potential for achieving a fully-wearable system for monitoring skin hydration.

Electroencephalogram (EEG) plays a significant role in the analysis of cerebral activity, although the recorded electrical brain signals are always contaminated with artifacts. This represents the major issue limiting the use of EEG in daily life applications, as artifact removal process still remains a challenging task. Among the available methodologies, Artifact Subspace Reconstruction (ASR) is a promising tool that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameters have been validated only for high-density EEG acquisitions. In this regard, the present study proposes an enhanced procedure for the optimal individuation of ASR parameters, in order to successfully remove artifact in low-density EEG acquisitions (down to four channels). The proposed method starts from the analysis of real EEG data, to generate a large semi-simulated dataset with similar characteristics. Through a fine-tuning procedure on this semi-simulated data, the proposed method identifies the optimal parameters to be used for artifact removal on real data. The results show that the algorithm achieves an efficient removal of artifacts preserving brain signal information, also in low-density EEG signals, thus favoring the adoption of EEG also for more portable and/or daily-life applications.